act paragraphs containing any word from a predetermined list of LGTBQ terms 
EXTRACTIVE
The data for our case study spans over thirty years of articles from the New York Times, from January 1986 to December 2015, and was originally collected by BIBREF68 BIBREF68. The articles come from all sections of the newspaper, such as “World", “New York & Region", “Opinion", “Style", and “Sports". Our distributional semantic methods rely on all of the available data in order to obtain the most fine-grained understanding of the relationships between words possible. For the other techniques, we extract paragraphs containing any word from a predetermined list of LGTBQ terms (shown in Table TABREF19).
2003.03014
Key words : </s> com put ational Ġsoc iol ingu istics , Ġdehuman ization , Ġlex ical Ġvariation , Ġlanguage Ġchange , Ġmedia , ĠNew ĠYork ĠTimes , ĠLGBTQ </s> Warning : Ġthis Ġpaper Ġcontains Ġmaterial Ġthat Ġsome Ġmay Ġfind Ġoffensive Ġor Ġupsetting . </s> Introduction </s> Despite Ġthe ĠAmerican Ġpublic 's Ġincreasing Ġacceptance Ġof ĠLGBTQ Ġpeople Ġand Ġrecent Ġlegal Ġsuccesses , ĠLGBTQ Ġindividuals Ġfrequently Ġremain Ġthe Ġtargets Ġof Ġhate Ġand Ġviolence ĠB IB REF 0 , ĠB IB REF 1 , ĠB IB REF 2 . ĠAt Ġthe Ġcore Ġof Ġthis Ġissue Ġis Ġdehuman ization , ĠâĢ ľ the Ġact Ġof Ġperce iving Ġor Ġtreating Ġpeople Ġas Ġless Ġthan Ġhuman âĢ Ŀ ĠB IB REF 3 , Ġa Ġprocess Ġthat Ġheavily Ġcontributes Ġto Ġextreme Ġinter group Ġbias ĠB IB REF 4 . ĠLanguage Ġis Ġcentral Ġto Ġstudying Ġthis Ġphenomenon ; Ġlike Ġother Ġforms Ġof Ġbias ĠB IB REF 5 , ĠB IB REF 6 , ĠB IB REF 7 , ĠB IB REF 8 , ĠB IB REF 9 , Ġdehuman izing Ġattitudes Ġare Ġexpressed Ġthrough Ġsubtle Ġlinguistic Ġmanip ulations , Ġeven Ġin Ġcarefully - edited Ġtexts . ĠIt Ġis Ġcrucial Ġto Ġunderstand Ġthe Ġuse Ġof Ġsuch Ġlinguistic Ġsignals Ġin Ġmainstream Ġmedia , Ġas Ġthe Ġmedia 's Ġrepresentation Ġof Ġmarginalized Ġsocial Ġgroups Ġhas Ġfar - reaching Ġimplications Ġfor Ġsocial Ġacceptance , Ġpolicy , Ġand Ġsafety . </s> While Ġsmall - scale Ġstudies Ġof Ġdehuman ization Ġand Ġmedia Ġrepresentation Ġof Ġmarginalized Ġcommunities Ġprovide Ġvaluable Ġinsights Ġ( e . g . ĠB IB REF 10 ), Ġthere Ġexist Ġno Ġknown Ġlarge - scale Ġanalyses , Ġlikely Ġdue Ġto Ġdifficulties Ġin Ġquant ifying Ġsuch Ġa Ġsubjective Ġand Ġmult id imensional Ġpsychological Ġprocess . ĠHowever , Ġthe Ġability Ġto Ġdo Ġlarge - scale Ġanalysis Ġis Ġcrucial Ġfor Ġunderstanding Ġhow Ġdehuman izing Ġattitudes Ġhave Ġevolved Ġover Ġlong Ġperiods Ġof Ġtime . ĠFurthermore , Ġby Ġbeing Ġable Ġto Ġaccount Ġfor Ġa Ġgreater Ġamount Ġof Ġmedia Ġdiscourse Ġat Ġonce , Ġlarge - scale Ġtechniques Ġcan Ġprovide Ġa Ġmore Ġcomplete Ġview Ġof Ġthe Ġmedia Ġenvironment Ġto Ġwhich Ġthe Ġpublic Ġis Ġexposed . </s> L ingu istics Ġand Ġcomputer Ġscience Ġoffer Ġvaluable Ġmethods Ġand Ġinsights Ġon Ġwhich Ġsuch Ġlarge - scale Ġtechniques Ġmight Ġbe Ġdeveloped Ġfor Ġthe Ġstudy Ġof Ġdehuman ization . ĠBy Ġleveraging Ġmore Ġinformation Ġabout Ġthe Ġcontexts Ġin Ġwhich Ġmarginalized Ġgroups Ġare Ġdiscussed , Ġcomputational Ġlinguistic Ġmethods Ġnot Ġonly Ġenable Ġlarge - scale Ġstudy Ġof Ġa Ġcomplex Ġpsychological Ġphenomenon , Ġbut Ġcan Ġeven Ġreveal Ġlinguistic Ġvariations Ġand Ġchanges Ġthat Ġare Ġnot Ġeasily Ġidentifiable Ġthrough Ġqualitative Ġanalysis Ġalone . </s> In Ġthis Ġwork , Ġwe Ġdevelop Ġa Ġcomputational Ġlinguistic Ġframework Ġfor Ġanalyzing Ġdehuman izing Ġlanguage , Ġwith Ġa Ġfocus Ġon Ġlex ical Ġsignals Ġof Ġdehuman ization . ĠSocial Ġpsychologists Ġhave Ġidentified Ġnumerous Ġcomponents Ġof Ġdehuman ization , Ġsuch Ġas Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġdenial Ġof Ġagency , Ġmoral Ġdisgust , Ġand Ġlik ening Ġmembers Ġof Ġa Ġtarget Ġgroup Ġto Ġnon - human Ġentities Ġsuch Ġas Ġver min . ĠDrawing Ġupon Ġthis Ġrich Ġbody Ġof Ġliterature , Ġwe Ġfirst Ġidentify Ġclear Ġlinguistic Ġanalog s Ġfor Ġthese Ġcomponents Ġand Ġpropose Ġseveral Ġcomputational Ġtechniques Ġto Ġmeasure Ġthese Ġlinguistic Ġcorrelates . ĠWe Ġthen Ġapply Ġthis Ġgeneral Ġframework Ġto Ġexplore Ġchanging Ġrepresentations Ġof ĠLGBTQ Ġgroups Ġin Ġthe ĠNew ĠYork ĠTimes Ġover Ġthe Ġspan Ġof Ġthree Ġdecades . ĠWe Ġadditionally Ġuse Ġthis Ġlens Ġof Ġdehuman ization Ġto Ġinvestigate Ġdifferences Ġin Ġsocial Ġmeaning Ġbetween Ġthe Ġden otation ally - similar Ġlabels Ġgay Ġand Ġhomosexual . </s> This Ġpaper Ġaims Ġto Ġbridge Ġthe Ġgaps Ġbetween Ġcomputational Ġmodeling , Ġsoc iol ingu istics , Ġand Ġdehuman ization Ġresearch Ġwith Ġimplications Ġfor Ġseveral Ġdisciplines . ĠIn Ġaddition Ġto Ġenabling Ġlarge - scale Ġstudies Ġof Ġdehuman izing Ġlanguage Ġand Ġmedia Ġrepresentation Ġof Ġmarginalized Ġsocial Ġgroups , Ġthese Ġtechniques Ġcan Ġbe Ġbuilt Ġinto Ġsystems Ġthat Ġseek Ġto Ġcapture Ġboth Ġconscious Ġand Ġunconscious Ġbiases Ġin Ġtext . ĠFurthermore , Ġthis Ġwork Ġhas Ġimplications Ġfor Ġimproving Ġmachines ' Ġabilities Ġto Ġautomatically Ġdetect Ġhate Ġspeech Ġand Ġabusive Ġlanguage Ġonline , Ġwhich Ġare Ġtypically Ġunderpin ned Ġby Ġdehuman izing Ġlanguage . ĠFinally , Ġour Ġcase Ġstudy Ġdemonstrates Ġthat Ġsuch Ġcomputational Ġanalyses Ġof Ġdiscussions Ġabout Ġmarginalized Ġgroups Ġcan Ġprovide Ġunique Ġinsights Ġinto Ġlanguage Ġvariation Ġand Ġchange Ġwithin Ġsensitive Ġsociop olitical Ġcontexts , Ġand Ġhelp Ġus Ġunderstand Ġhow Ġpeople Ġ( and Ġinstitutions ) Ġuse Ġlanguage Ġto Ġexpress Ġtheir Ġideologies Ġand Ġattitudes Ġtowards Ġcertain Ġsocial Ġgroups . </s> Background Ġ: :: ĠDe human ization </s> Our Ġlex ical Ġsemantic Ġanalysis Ġinvolves Ġquant ifying Ġlinguistic Ġcorrelates Ġof Ġcomponent Ġpsychological Ġprocesses Ġthat Ġcontribute Ġto Ġdehuman ization . ĠOur Ġapproaches Ġare Ġinformed Ġby Ġsocial Ġpsychology Ġresearch Ġon Ġdehuman ization , Ġwhich Ġis Ġbriefly Ġsummarized Ġhere . ĠPrior Ġwork Ġhas Ġidentified Ġnumerous Ġrelated Ġprocesses Ġthat Ġcomprise Ġdehuman ization ĠB IB REF 4 . ĠOne Ġsuch Ġcomponent Ġis Ġlik ening Ġmembers Ġof Ġthe Ġtarget Ġgroup Ġto Ġnon - human Ġentities , Ġsuch Ġas Ġmachines Ġor Ġanimals ĠB IB REF 4 , ĠB IB REF 11 , ĠB IB REF 12 . ĠBy Ġperce iving Ġmembers Ġof Ġa Ġtarget Ġgroup Ġto Ġbe Ġnon - human , Ġthey Ġare ĠâĢ ľ outside Ġthe Ġboundary Ġin Ġwhich Ġmoral Ġvalues , Ġrules , Ġand Ġconsiderations Ġof Ġfairness Ġapply " ĠB IB REF 13 , Ġwhich Ġthus Ġleads Ġto Ġviolence Ġand Ġother Ġforms Ġof Ġabuse . ĠMet aph ors Ġand Ġimagery Ġrelating Ġtarget Ġgroups Ġto Ġver min Ġare Ġparticularly Ġinsidious Ġand Ġplayed Ġa Ġprominent Ġrole Ġin Ġthe Ġgenocide Ġof ĠJews Ġin ĠNazi ĠGermany Ġand ĠT uts is Ġin ĠRwanda ĠB IB REF 14 . ĠMore Ġrecently , Ġthe Ġver min Ġmetaphor Ġhas Ġbeen Ġinvoked Ġby Ġthe Ġmedia Ġto Ġdiscuss Ġterrorists Ġand Ġpolitical Ġleaders Ġof Ġmajority - Muslim Ġcountries Ġafter ĠSeptember Ġ11 ĠB IB REF 15 . ĠAccording Ġto ĠB IB REF 16 , Ġthe Ġver min Ġmetaphor Ġis Ġparticularly Ġpowerful Ġbecause Ġit Ġconceptual izes Ġthe Ġtarget Ġgroup Ġas ĠâĢ ľ eng aged Ġin Ġthreatening Ġbehavior , Ġbut Ġdevoid Ġof Ġthought Ġor Ġemotional Ġdesire ". </s> Dis g ust Ġunder lies Ġthe Ġdehuman izing Ġnature Ġof Ġthese Ġmetaphors Ġand Ġis Ġitself Ġanother Ġimportant Ġelement Ġof Ġdehuman ization . ĠDis g ust Ġcontributes Ġto Ġmembers Ġof Ġtarget Ġgroups Ġbeing Ġperceived Ġas Ġless - than - human Ġand Ġof Ġnegative Ġsocial Ġvalue ĠB IB REF 17 . ĠIt Ġis Ġoften Ġev oked Ġ( both Ġin Ġreal Ġlife Ġand Ġexperimental Ġsettings ) Ġthrough Ġlik ening Ġa Ġtarget Ġgroup Ġto Ġanimals . ĠB IB REF 18 Ġfind Ġthat Ġprim ing Ġparticipants Ġto Ġfeel Ġdisgust Ġfacilitates ĠâĢ ľ moral Ġexclusion Ġof Ġout - groups ". ĠExper iments Ġby ĠB IB REF 17 Ġand ĠB IB REF 19 Ġsimilarly Ġfind Ġthat Ġdisgust Ġis Ġa Ġpredictor Ġof Ġdehuman izing Ġperceptions Ġof Ġa Ġtarget Ġgroup . ĠBoth Ġmoral Ġdisgust Ġtowards Ġa Ġparticular Ġsocial Ġgroup Ġand Ġthe Ġinvocation Ġof Ġnon - human Ġmetaphors Ġare Ġfacilitated Ġby Ġessential ist Ġbeliefs Ġabout Ġgroups , Ġwhich ĠB IB REF 4 Ġpresents Ġas Ġa Ġnecessary Ġcomponent Ġof Ġdehuman ization . ĠIn Ġorder Ġto Ġdistinguish Ġbetween Ġhuman Ġand Ġnon - human , Ġdehuman ization Ġrequires Ġan Ġexaggerated Ġperception Ġof Ġinter group Ġdifferences . ĠEssential ist Ġthinking Ġthus Ġcontributes Ġto Ġdehuman ization Ġby Ġleading Ġto Ġthe Ġperception Ġof Ġsocial Ġgroups Ġas Ġcategor ically Ġdistinct , Ġwhich Ġin Ġturn Ġemphasizes Ġinter group Ġdifferences ĠB IB REF 4 </s> According Ġto ĠB IB REF 4 , Ġmuch Ġof Ġthe Ġprior Ġresearch Ġdescribes ĠâĢ ľ extremely Ġnegative Ġevaluations Ġof Ġothers " Ġas Ġa Ġmajor Ġcomponent Ġof Ġdehuman ization . ĠThis Ġis Ġespecially Ġpronounced Ġin ĠB IB REF 20 's Ġaccount Ġof Ġdeleg it im ization , Ġwhich Ġinvolves Ġusing Ġnegative Ġcharacteristics Ġto Ġcategor ize Ġgroups Ġthat Ġare ĠâĢ ľ ex cluded Ġfrom Ġthe Ġrealm Ġof Ġacceptable Ġnorms Ġand Ġvalues " ĠB IB REF 20 . ĠWhile ĠB IB REF 20 Ġdefines Ġdeleg it im ization Ġas Ġa Ġdistinct Ġprocess , Ġhe Ġconsiders Ġdehuman ization Ġto Ġbe Ġone Ġmeans Ġof Ġdeleg it im ization . ĠB IB REF 13 Ġalso Ġdiscusses Ġbroader Ġprocesses Ġof Ġmoral Ġexclusion , Ġone Ġof Ġwhich Ġis Ġdehuman ization . ĠA Ġclosely Ġrelated Ġprocess Ġis Ġpsychological Ġdist ancing , Ġin Ġwhich Ġone Ġperce ives Ġothers Ġto Ġbe Ġobjects Ġor Ġnonexistent ĠB IB REF 13 . ĠB IB REF 21 Ġidentifies Ġelements Ġthat Ġcontribute Ġto Ġthe Ġobject ification Ġ( and Ġthus Ġdehuman ization ) Ġof Ġwomen , Ġone Ġof Ġwhich Ġis Ġdenial Ġof Ġsubject ivity , Ġor Ġthe Ġhabitual Ġneglect Ġof Ġone 's Ġexperiences , Ġemotions , Ġand Ġfeelings . </s> Another Ġcomponent Ġof Ġdehuman ization Ġis Ġthe Ġdenial Ġof Ġagency Ġto Ġmembers Ġof Ġthe Ġtarget Ġgroup ĠB IB REF 4 . ĠAccording Ġto ĠB IB REF 16 , Ġthere Ġare Ġthree Ġtypes Ġof Ġagency : Ġ" the Ġability Ġto Ġ( 1 ) Ġexperience Ġemotion Ġand Ġfeel Ġpain Ġ( aff ective Ġmental Ġstates ), Ġ( 2 ) Ġact Ġand Ġproduce Ġan Ġeffect Ġon Ġtheir Ġenvironment Ġ( behavior al Ġpotential ), Ġand Ġ( 3 ) Ġthink Ġand Ġhold Ġbeliefs Ġ( c ognitive Ġmental Ġstates ) ĠB IB REF 16 . ĠDe human ization Ġtypically Ġinvolves Ġthe Ġdenial Ġof Ġone Ġor Ġmore Ġof Ġthese Ġtypes Ġof Ġagency ĠB IB REF 16 . </s> In ĠSection ĠSEC REF 4 , Ġwe Ġintroduce Ġcomputational Ġlinguistic Ġmethods Ġto Ġquantify Ġseveral Ġof Ġthese Ġcomponents Ġof Ġdehuman ization . </s> Background Ġ: :: ĠRelated ĠComput ational ĠWork </s> While Ġthis Ġis Ġthe Ġfirst Ġknown Ġcomputational Ġwork Ġthat Ġfocuses Ġon Ġdehuman ization , Ġwe Ġdraw Ġupon Ġa Ġgrowing Ġbody Ġof Ġliterature Ġat Ġthe Ġintersection Ġof Ġnatural Ġlanguage Ġprocessing Ġand Ġsocial Ġscience Ġresearch . ĠWe Ġare Ġparticularly Ġinspired Ġby Ġthe Ġarea Ġof Ġautomatically Ġdetecting Ġsubjective Ġlanguage , Ġwhich Ġwas Ġlargely Ġpioneered Ġby ĠJ any ce ĠW ie be Ġand Ġcolleagues , Ġwho Ġdeveloped Ġnovel Ġlex ical Ġresources Ġand Ġalgorithms Ġfor Ġthis Ġtask Ġbased Ġon Ġword Ġand Ġphrase Ġdistributions ĠB IB REF 5 . ĠThese Ġresources Ġhave Ġbeen Ġused Ġas Ġlingu istically - informed Ġfeatures Ġin Ġmachine Ġlearning Ġclassification Ġof Ġbiased Ġlanguage ĠB IB REF 7 . ĠOther Ġwork Ġhas Ġexpanded Ġthis Ġlex icon - based Ġapproach Ġto Ġaccount Ġfor Ġthe Ġrole Ġof Ġsynt actic Ġform Ġfor Ġidentifying Ġthe Ġwriter 's Ġperspective Ġtowards Ġdifferent Ġentities Ġmentioned Ġin Ġa Ġtext ĠB IB REF 6 , ĠB IB REF 22 . </s> These Ġmethods Ġhave Ġbeen Ġused Ġand Ġexpanded Ġto Ġanalyze Ġper n icious , Ġbut Ġoften Ġimplicit Ġsocial Ġbiases ĠB IB REF 23 . ĠFor Ġexample , ĠB IB REF 8 Ġanalyzed Ġracial Ġbias Ġin Ġpolice Ġtranscripts Ġby Ġtraining Ġmachine Ġlearning Ġclass ifiers Ġwith Ġlinguistic Ġfeatures Ġinformed Ġby Ġpolit eness Ġtheory ĠB IB REF 8 , Ġand ĠB IB REF 24 Ġinvestigated Ġhistorical Ġracial Ġbiases Ġin Ġmass Ġmedia Ġthrough Ġchanging Ġword Ġembed d ings ĠB IB REF 24 . ĠOther Ġwork Ġhas Ġlooked Ġat Ġwords Ġin Ġparticular Ġsynt actic Ġcontexts Ġto Ġanalyze Ġpower Ġand Ġagency , Ġand Ġhow Ġthese Ġconcepts Ġrelate Ġto Ġgender Ġbias Ġin Ġmovies ĠB IB REF 25 Ġand Ġin Ġmass Ġmedia Ġarticles Ġabout Ġthe Ġ# Me Too Ġmovement ĠB IB REF 26 . ĠThere Ġhas Ġalso Ġbeen Ġa Ġgrowing Ġfocus Ġon Ġthe Ġanalysis Ġof Ġsubtle Ġmanifestations Ġof Ġsocial Ġbiases , Ġsuch Ġas Ġcondesc ension ĠB IB REF 27 , Ġmicro ag ress ions ĠB IB REF 9 , Ġand ĠâĢ ľ other ing " Ġlanguage ĠB IB REF 28 , ĠB IB REF 29 . ĠIn Ġaddition , Ġour Ġfocus Ġon Ġdehuman ization Ġis Ġclosely Ġrelated Ġto Ġthe Ġdetection Ġand Ġanalysis Ġof Ġhate Ġspeech Ġand Ġabusive Ġlanguage ĠB IB REF 30 , ĠB IB REF 31 . </s> Gender Ġand Ġracial Ġbias Ġhave Ġalso Ġbeen Ġidentified Ġwithin Ġwidely - de ploy ed ĠN LP Ġsystems , Ġfor Ġtasks Ġincluding Ġtoxicity Ġdetection ĠB IB REF 32 , Ġsentiment Ġanalysis ĠB IB REF 33 , Ġcore ference Ġresolution ĠB IB REF 34 , Ġlanguage Ġidentification ĠB IB REF 35 , Ġand Ġin Ġmany Ġother Ġareas ĠB IB REF 36 . ĠGiven Ġthe Ġbiases Ġcaptured , Ġreproduced , Ġand Ġperpet uated Ġin ĠN LP Ġsystems , Ġthere Ġis Ġalso Ġa Ġgrowing Ġinterest Ġin Ġmitigating Ġsubjective Ġbiases ĠB IB REF 36 , Ġwith Ġapproaches Ġincluding Ġmodifying Ġembed ding Ġspaces ĠB IB REF 37 , ĠB IB REF 38 , Ġaugment ing Ġdatasets ĠB IB REF 39 , Ġand Ġadapting Ġnatural Ġlanguage Ġgeneration Ġmethods Ġto ĠâĢ ľ neutral ize " Ġtext ĠB IB REF 40 . </s> A Ġrelated Ġline Ġof Ġresearch Ġhas Ġdeveloped Ġcomputational Ġapproaches Ġto Ġinvestigate Ġlanguage Ġuse Ġand Ġvariation Ġin Ġdiscourse Ġabout Ġcomplex Ġsociop olitical Ġissues Ġin Ġthe Ġmedia . ĠFor Ġexample , Ġsome Ġwork Ġhas Ġdrawn Ġupon Ġpolitical Ġcommunication Ġtheory Ġto Ġautomatically Ġdetect Ġthe Ġframing Ġof Ġan Ġissue ĠB IB REF 41 , ĠB IB REF 42 , ĠB IB REF 43 Ġthrough Ġboth Ġsupervised Ġclassification ĠB IB REF 44 , ĠB IB REF 45 Ġand Ġun super vised Ġmethods , Ġsuch Ġas Ġtopic Ġmodeling Ġand Ġlex icon Ġinduction ĠB IB REF 46 , ĠB IB REF 47 , ĠB IB REF 48 . ĠScholars Ġhave Ġalso Ġdeveloped Ġcomputational Ġmethods Ġto Ġidentify Ġlex ical Ġcues Ġof Ġpartisan Ġpolitical Ġspeech , Ġpolitical Ġsl ant Ġin Ġmass Ġmedia , Ġand Ġpolarization Ġin Ġsocial Ġmedia ĠB IB REF 49 , ĠB IB REF 50 , ĠB IB REF 48 . </s> Background Ġ: :: ĠAtt itudes ĠTowards ĠLGBTQ ĠCommunities Ġin Ġthe ĠUnited ĠStates </s> Because Ġwe Ġapply Ġour Ġframework Ġto Ġstudy Ġdehuman izing Ġlanguage Ġin Ġdiscussions Ġabout ĠLGBTQ Ġcommunities Ġin Ġthe ĠNew ĠYork ĠTimes , Ġsome Ġbackground Ġis Ġnecessary . ĠOverall , Ġthe ĠAmerican Ġpublic Ġhas Ġbecome Ġmore Ġaccepting Ġof ĠLGBTQ Ġpeople Ġand Ġsupportive Ġof Ġtheir Ġrights . ĠIn Ġ1977 , Ġequal Ġpercentages Ġof Ġrespondents Ġ( 43 %) Ġagreed Ġand Ġdisagreed Ġwith Ġthe Ġstatement Ġthat Ġgay Ġor Ġlesbian Ġrelations Ġbetween Ġconsent ing Ġadults Ġshould Ġbe Ġlegal ĠB IB REF 2 . ĠAppro val Ġof Ġgay Ġand Ġlesbian Ġrelations Ġthen Ġdecreased Ġin Ġthe Ġ1980 s ; Ġin Ġ1986 , Ġonly Ġ32 % Ġof Ġrespondents Ġbelieved Ġthey Ġshould Ġbe Ġlegal . ĠAccording Ġto ĠGallup , Ġattitudes Ġhave Ġbecome Ġincreasingly Ġpositive Ġsince Ġthe Ġ1990 s , Ġand Ġin Ġ2019 , Ġ73 % Ġresponded Ġthat Ġgay Ġor Ġlesbian Ġrelations Ġshould Ġbe Ġlegal . ĠThe ĠPew ĠResearch Ġcenter Ġbegan Ġsurve ying ĠAmericans Ġabout Ġtheir Ġbeliefs Ġabout Ġsame - sex Ġmarriage Ġin Ġ2001 Ġand Ġfound Ġsimilar Ġtrends ĠB IB REF 51 . ĠBetween Ġ2001 Ġand Ġ2019 , Ġsupport Ġfor Ġsame - sex Ġmarriage Ġamong Ġrespondents Ġjumped Ġfrom Ġ35 % Ġto Ġ61 %. </s> In Ġaddition Ġto Ġthe Ġpublic 's Ġoverall Ġattitudes , Ġit Ġis Ġimportant Ġto Ġconsider Ġvariation Ġand Ġchange Ġin Ġthe Ġspecific Ġwords Ġused Ġto Ġrefer Ġto ĠLGBTQ Ġpeople . ĠBecause Ġthese Ġlabels Ġpotentially Ġconvey Ġmany Ġdifferent Ġsocial Ġmeanings Ġand Ġhave Ġdifferent Ġrelationships Ġwith Ġdehuman ization Ġin Ġthe Ġmedia , Ġa Ġprimary Ġfocus Ġof Ġthis Ġstudy Ġinvolves Ġcomparing Ġdifferent ĠLGBTQ Ġlabels , Ġspecifically Ġgay Ġand Ġhomosexual . ĠThe ĠGallup Ġsurvey Ġasked Ġfor Ġopinions Ġon Ġlegality Ġof ĠâĢ ľ hom osexual Ġrelations " Ġuntil Ġ2008 , Ġbut Ġthen Ġchanged Ġthe Ġwording Ġto ĠâĢ ľ gay Ġand Ġlesbian Ġrelations ". ĠThis Ġwas Ġlikely Ġbecause Ġmany Ġpeople Ġwho Ġidentify Ġas Ġgay Ġand Ġlesbian Ġfind Ġthe Ġword Ġhomosexual Ġto Ġbe Ġoutdated Ġand Ġderogatory . ĠAccording Ġto Ġthe ĠLGBTQ Ġmedia Ġmonitoring Ġorganization ĠGL A AD , Ġhomosexual 's Ġoff ens iveness Ġorigin ates Ġin Ġthe Ġword 's Ġdehuman izing Ġclinical Ġhistory , Ġwhich Ġhad Ġfalsely Ġsuggested Ġthat ĠâĢ ľ people Ġattracted Ġto Ġthe Ġsame Ġsex Ġare Ġsomehow Ġdise ased Ġor Ġpsychologically / em otion ally Ġdis ordered " Ġ. ĠBeyond Ġits Ġoutdated Ġclinical Ġassociations , Ġsome Ġargue Ġthat Ġthe Ġword Ġhomosexual Ġis Ġmore Ġclosely Ġassociated Ġwith Ġsex Ġand Ġall Ġof Ġits Ġnegative Ġcon notations Ġsimply Ġby Ġvirtue Ġof Ġcontaining Ġthe Ġword Ġsex , Ġwhile Ġterms Ġsuch Ġas Ġgay Ġand Ġlesbian Ġavoid Ġsuch Ġcon notations ĠB IB REF 52 . ĠMost Ġnewspapers , Ġincluding Ġthe ĠNew ĠYork ĠTimes , Ġalmost Ġexclusively Ġused Ġthe Ġword Ġhomosexual Ġin Ġarticles Ġabout Ġgay Ġand Ġlesbian Ġpeople Ġuntil Ġthe Ġlate Ġ1980 s ĠB IB REF 53 . ĠThe ĠNew ĠYork ĠTimes Ġbegan Ġusing Ġthe Ġword Ġgay Ġin Ġnon - qu oted Ġtext Ġin Ġ1987 . ĠMany Ġmajor Ġnewspapers Ġbegan Ġrestricting Ġthe Ġuse Ġof Ġthe Ġword Ġhomosexual Ġin Ġ2006 ĠB IB REF 52 . ĠAs Ġof Ġ2013 , Ġthe ĠNew ĠYork ĠTimes Ġhas Ġconfined Ġthe Ġuse Ġof Ġhomosexual Ġto Ġspecific Ġreferences Ġto Ġsexual Ġactivity Ġor Ġclinical Ġorientation , Ġin Ġaddition Ġto Ġdirect Ġquotes Ġand Ġparaph r ases Ġ. </s> Beyond Ġdifferences Ġin Ġhow ĠLGBTQ Ġpeople Ġperceive Ġthe Ġterms Ġgay Ġor Ġlesbian Ġrelative Ġto Ġhomosexual , Ġit Ġhas Ġbeen Ġshown Ġthat Ġthe Ġspecific Ġchoice Ġof Ġlabel Ġcan Ġalso Ġhave Ġan Ġeffect Ġon Ġattitudes Ġtowards ĠLGBTQ Ġpeople . ĠIn Ġ2012 , ĠB IB REF 54 Ġasked Ġsurvey Ġrespondents Ġabout Ġeither ĠâĢ ľ gay Ġand Ġlesbian Ġrights " Ġor ĠâĢ ľ hom osexual Ġrights ". ĠRespond ents Ġwho Ġread Ġthe Ġword ĠâĢ ľ hom osexual " Ġtended Ġto Ġshow Ġless Ġsupport Ġfor ĠLGBTQ Ġrights . ĠThis Ġeffect Ġwas Ġprimarily Ġdriven Ġby Ġhigh Ġauthor itar ians , Ġpeople Ġwho Ġshow Ġhigh Ġsensitivity Ġto Ġinter group Ġdistinctions . ĠThe Ġauthors Ġposit Ġthat Ġhomosexual Ġmakes Ġsocial Ġgroup Ġdistinctions Ġmore Ġblatant Ġthan Ġgay Ġor Ġlesbian , Ġwhich Ġthen Ġleads Ġto Ġa Ġgreater Ġdegree Ġof Ġpsychological Ġdist ancing , Ġthus Ġenabling Ġparticipants Ġto Ġfurther Ġremove ĠLGBTQ Ġpeople Ġfrom Ġtheir Ġrealm Ġof Ġmoral Ġconsideration ĠB IB REF 54 . ĠBased Ġon Ġprior Ġresearch Ġand Ġevolving Ġmedia Ġguidelines , Ġwe Ġexpect Ġour Ġcomputational Ġanalysis Ġto Ġshow Ġthat Ġhomosexual Ġoccurs Ġin Ġmore Ġdehuman izing Ġcontexts Ġthan Ġother ĠLGBTQ Ġlabels , Ġsuch Ġas Ġgay . </s> Oper ational izing ĠDe human ization </s> In ĠSection ĠSEC REF 1 , Ġwe Ġbriefly Ġdiscussed Ġmultiple Ġelements Ġof Ġdehuman ization Ġthat Ġhave Ġbeen Ġidentified Ġin Ġsocial Ġpsychology Ġliterature . ĠHere Ġwe Ġintroduce Ġand Ġquantify Ġlex ical Ġcorrelates Ġto Ġoperational ize Ġfour Ġof Ġthese Ġcomponents : Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġdenial Ġof Ġagency , Ġmoral Ġdisgust , Ġand Ġuse Ġof Ġnon - human Ġmetaphors Ġ( particularly Ġver min ). </s> Oper ational izing ĠDe human ization Ġ: :: ĠNegative ĠEvaluation Ġof Ġa ĠTarget ĠGroup </s> One Ġprominent Ġaspect Ġof Ġdehuman ization Ġis Ġextremely Ġnegative Ġevaluations Ġof Ġmembers Ġof Ġa Ġtarget Ġgroup ĠB IB REF 4 . ĠAttribution Ġof Ġnegative Ġcharacteristics Ġto Ġmembers Ġof Ġa Ġtarget Ġgroup Ġin Ġorder Ġto Ġexclude Ġthat Ġgroup Ġfrom ĠâĢ ľ the Ġrealm Ġof Ġacceptable Ġnorms Ġand Ġvalues " Ġis Ġspecifically Ġthe Ġkey Ġcomponent Ġof Ġdeleg it im ization , Ġa Ġprocess Ġof Ġmoral Ġexclusion Ġclosely Ġrelated Ġto Ġdehuman ization . ĠWe Ġhypothes ize Ġthat Ġthis Ġnegative Ġevaluation Ġof Ġa Ġtarget Ġgroup Ġcan Ġbe Ġrealized Ġby Ġwords Ġand Ġphrases Ġwhose Ġcon notations Ġhave Ġextremely Ġlow Ġval ence , Ġwhere Ġthe Ġval ence Ġof Ġa Ġword Ġrefers Ġto Ġthe Ġdimension Ġof Ġmeaning Ġcorresponding Ġto Ġpositive / negative Ġ( or Ġpleasure / dis ple asure ) ĠB IB REF 55 , ĠB IB REF 56 . ĠThus , Ġwe Ġpropose Ġseveral Ġval ence Ġlex icon - based Ġapproaches Ġto Ġmeasure Ġthis Ġcomponent : Ġparagraph - level Ġval ence Ġanalysis , ĠCon notation ĠFrames , Ġand Ġword Ġembed ding Ġneighbor Ġval ence . ĠEach Ġof Ġthese Ġtechniques Ġhas Ġdifferent Ġadvantages Ġand Ġdisadvantages Ġregarding Ġprecision Ġand Ġinterpret ability . </s> Oper ational izing ĠDe human ization Ġ: :: ĠNegative ĠEvaluation Ġof Ġa ĠTarget ĠGroup Ġ: :: ĠPar agraph - level ĠVal ence ĠAnalysis </s> As Ġwe Ġmentioned Ġabove , Ġone Ġimportant Ġdimension Ġof Ġaffect ive Ġmeaning Ġis Ġval ence , Ġwhich Ġcorresponds Ġto Ġan Ġindividual 's Ġevaluation Ġof Ġan Ġevent Ġor Ġconcept , Ġranging Ġfrom Ġnegative / un pleasant Ġto Ġpositive / pleasant ĠB IB REF 55 , ĠB IB REF 57 . ĠA Ġstraightforward Ġlex ical Ġapproach Ġto Ġquantify Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup Ġis Ġthus Ġto Ġmeasure Ġthe Ġaverage Ġval ence Ġover Ġall Ġwords Ġin Ġcorpor a Ġcontaining Ġdiscussions Ġof Ġthe Ġtarget Ġgroup . ĠWe Ġquantify Ġthis Ġby Ġusing Ġthe Ġval ence Ġdimension Ġfrom Ġthe ĠN RC ĠV AD Ġlex icon , Ġwhich Ġcontains Ġreal - valued Ġscores Ġranging Ġfrom Ġzero Ġto Ġone Ġfor Ġval ence , Ġarousal Ġand Ġdominance Ġfor Ġ20 , 000 ĠEnglish Ġwords , Ġwhere Ġa Ġscore Ġof Ġzero Ġis Ġthe Ġlowest Ġval ence Ġ( most Ġnegative Ġemotion ) Ġand Ġa Ġscore Ġof Ġone Ġis Ġthe Ġhighest Ġpossible Ġval ence Ġ( most Ġpositive Ġemotion ) ĠB IB REF 56 . ĠWords Ġwith Ġthe Ġhighest Ġval ence Ġin Ġthe ĠN RC ĠV AD ĠLex icon Ġinclude Ġlove , Ġhappy , Ġand Ġhappily , Ġwhile Ġwords Ġwith Ġthe Ġlowest Ġval ence Ġinclude Ġtoxic , Ġnightmare , Ġand Ġshit . </s> In Ġour Ġcase Ġstudy , Ġwe Ġqual itatively Ġfound Ġthat Ġparagraphs Ġwere Ġthe Ġoptimal Ġlevel Ġof Ġcontext Ġfor Ġanalysis ; Ġfull Ġarticles Ġoften Ġfocused Ġon Ġtopics Ġunrelated Ġto ĠLGBTQ Ġcommunities , Ġeven Ġif Ġan ĠLGBTQ Ġlabel Ġwas Ġmentioned Ġsomewhere Ġin Ġthe Ġarticle , Ġwhile Ġsingle Ġsentences Ġdid Ġnot Ġprovide Ġenough Ġdiscussion Ġto Ġget Ġa Ġsense Ġof Ġhow ĠLGBTQ Ġgroups Ġare Ġrepresented Ġby Ġthe Ġnewspaper . ĠThus , Ġwe Ġcalculate Ġparagraph - level Ġscores Ġby Ġtaking Ġthe Ġaverage Ġval ence Ġscore Ġover Ġall Ġwords Ġin Ġthe Ġparagraph Ġthat Ġappear Ġ( or Ġwhose Ġle mm as Ġappear ) Ġin Ġthe ĠN RC ĠV AD Ġlex icon . </s> Oper ational izing ĠDe human ization Ġ: :: ĠNegative ĠEvaluation Ġof Ġa ĠTarget ĠGroup Ġ: :: ĠCon notation ĠFrames </s> While Ġparagraph - level Ġval ence Ġanalysis Ġis Ġquick Ġand Ġsimple , Ġit Ġis Ġsometimes Ġtoo Ġcoarse Ġbecause Ġwe Ġaim Ġto Ġunderstand Ġthe Ġsentiment Ġdirected Ġtowards Ġthe Ġtarget Ġgroup , Ġnot Ġjust Ġnearby Ġin Ġthe Ġtext . ĠFor Ġexample , Ġsuppose Ġthe Ġtarget Ġgroup Ġis Ġnamed ĠâĢ ľ B ". ĠA Ġsentence Ġsuch Ġas ĠâĢ ľ A Ġviolently Ġattacked ĠB " Ġwould Ġlikely Ġhave Ġextremely Ġnegative Ġval ence , Ġbut Ġthe Ġwriter Ġmay Ġnot Ġfeel Ġnegatively Ġtowards Ġthe Ġvictim , ĠâĢ ľ B ". </s> We Ġaddress Ġthis Ġby Ġusing ĠB IB REF 22 's ĠCon notation ĠFrames ĠLex icon , Ġwhich Ġcontains Ġrich Ġannotations Ġfor Ġ900 ĠEnglish Ġverbs Ġ( BI B REF 22 ). ĠAmong Ġother Ġthings , Ġfor Ġeach Ġverb , Ġthe ĠCon notation ĠFrames ĠLex icon Ġprovides Ġscores Ġ( ranging Ġfrom Ġ- 0 . 87 Ġto Ġ0 . 8 ) Ġfor Ġthe Ġwriter 's Ġperspective Ġtowards Ġthe Ġverb 's Ġsubject Ġand Ġobject . ĠIn Ġthe Ġexample Ġabove Ġfor Ġthe Ġverb Ġattack , Ġthe Ġlex icon Ġlists Ġthe Ġwriter 's Ġperspective Ġtowards Ġthe Ġsubject ĠâĢ ľ A ", Ġthe Ġattacker , Ġas Ġ- 0 . 6 Ġ( strong ly Ġnegative ) Ġand Ġthe Ġobject ĠâĢ ľ B " Ġas Ġ0 . 23 Ġ( weak ly Ġpositive ). </s> We Ġextract Ġall Ġsubject - verb - object Ġtu ples Ġcontaining Ġat Ġleast Ġone Ġtarget Ġgroup Ġlabel Ġusing Ġthe ĠSp acy Ġdependency Ġparser Ġ. ĠFor Ġeach Ġsubject Ġand Ġobject , Ġwe Ġcapture Ġthe Ġnoun Ġand Ġthe Ġmodifying Ġadject ives , Ġas Ġgroup Ġlabels Ġ( such Ġas Ġgay ) Ġcan Ġoften Ġtake Ġeither Ġnominal Ġor Ġadject ival Ġforms . ĠFor Ġeach Ġtuple , Ġwe Ġuse Ġthe Ġcon notation Ġframe Ġlex icon Ġto Ġdetermine Ġthe Ġwriter 's Ġperspective Ġeither Ġtowards Ġthe Ġsubject Ġif Ġthe Ġgroup Ġlabel Ġappears Ġin Ġthe Ġsubject Ġnoun Ġphrase , Ġor Ġperspective Ġtowards Ġthe Ġobject Ġif Ġthe Ġlabel Ġappears Ġin Ġthe Ġobject Ġnoun Ġphrase . ĠWe Ġthen Ġaverage Ġperspective Ġscores Ġover Ġall Ġtu ples . </s> Oper ational izing ĠDe human ization Ġ: :: ĠNegative ĠEvaluation Ġof Ġa ĠTarget ĠGroup Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠVal ence </s> While Ġa ĠCon notation ĠFrames Ġapproach Ġcan Ġbe Ġmore Ġprecise Ġthan Ġword - count ing Ġval ence Ġanalysis , Ġit Ġlimits Ġus Ġto Ġanalyzing ĠSV O Ġtri ples , Ġwhich Ġexcludes Ġa Ġlarge Ġportion Ġof Ġthe Ġavailable Ġdata Ġabout Ġthe Ġtarget Ġgroups . ĠThis Ġreveals Ġa Ġcon undrum : Ġbroader Ġcontext Ġcan Ġprovide Ġvaluable Ġinsights Ġinto Ġthe Ġimplicit Ġevaluations Ġof Ġa Ġsocial Ġgroup , Ġbut Ġwe Ġalso Ġwant Ġto Ġdirectly Ġprobe Ġattitudes Ġtowards Ġthe Ġgroup Ġitself . </s> We Ġaddress Ġthis Ġtension Ġby Ġtraining Ġvector Ġspace Ġmodels Ġto Ġrepresent Ġthe Ġdata , Ġin Ġwhich Ġeach Ġunique Ġword Ġin Ġa Ġlarge Ġcorpus Ġis Ġrepresented Ġby Ġa Ġvector Ġ( embed ding ) Ġin Ġhigh - dimensional Ġspace . ĠThe Ġgeometry Ġof Ġthe Ġresulting Ġvector Ġspace Ġcaptures Ġmany Ġsemantic Ġrelations Ġbetween Ġwords . ĠFurthermore , Ġprior Ġwork Ġhas Ġshown Ġthat Ġvector Ġspace Ġmodels Ġtrained Ġon Ġcorpor a Ġfrom Ġdifferent Ġtime Ġperiods Ġcan Ġcapture Ġsemantic Ġchange ĠB IB REF 58 , ĠB IB REF 59 . ĠFor Ġexample , Ġdi ach ronic Ġword Ġembed d ings Ġreveal Ġthat Ġthe Ġword Ġgay Ġmeant ĠâĢ ľ che er ful " Ġor ĠâĢ ľ d apper " Ġin Ġthe Ġearly Ġ20 th Ġcentury , Ġbut Ġshifted Ġto Ġits Ġcurrent Ġmeaning Ġof Ġsexual Ġorientation Ġby Ġthe Ġ1970 s . ĠBecause Ġword Ġembed d ings Ġare Ġcreated Ġfrom Ġreal - world Ġdata , Ġthey Ġcontain Ġreal - world Ġbiases . ĠFor Ġexample , ĠB IB REF 37 Ġdemonstrated Ġthat Ġgender Ġstereotypes Ġare Ġdeeply Ġingrained Ġin Ġthese Ġsystems . ĠThough Ġproblematic Ġfor Ġthe Ġwidespread Ġuse Ġof Ġthese Ġmodels Ġin Ġcomputational Ġsystems , Ġthese Ġrevealed Ġbiases Ġindicate Ġthat Ġword Ġembed d ings Ġcan Ġactually Ġbe Ġused Ġto Ġidentify Ġstereotypes Ġabout Ġsocial Ġgroups Ġand Ġunderstand Ġhow Ġthey Ġchange Ġover Ġtime ĠB IB REF 24 . </s> This Ġtechnique Ġcan Ġsimilarly Ġbe Ġapplied Ġto Ġunderstand Ġhow Ġa Ġsocial Ġgroup Ġis Ġnegatively Ġevaluated Ġwithin Ġa Ġlarge Ġtext Ġcorpus . ĠIf Ġthe Ġvector Ġcorresponding Ġto Ġa Ġsocial Ġgroup Ġlabel Ġis Ġlocated Ġin Ġthe Ġsemantic Ġembed ding Ġspace Ġnear Ġnegative Ġwords Ġor Ġterms Ġwith Ġclearly Ġnegative Ġevaluations , Ġthat Ġgroup Ġis Ġlikely Ġnegatively Ġevaluated Ġ( and Ġpossibly Ġdehuman ized ) Ġin Ġthe Ġtext . ĠThese Ġvector Ġspace Ġmodels Ġcan Ġbe Ġused Ġin Ġa Ġdi ach ronic Ġstudy Ġto Ġcapture Ġsubtle Ġchanges Ġin Ġthe Ġcon notations Ġof Ġa Ġgroup Ġlabel Ġover Ġtime , Ġeven Ġwhen Ġthe Ġden ot ational Ġmeaning Ġof Ġthe Ġlabel Ġhas Ġremained Ġconstant . ĠFurthermore , Ġcomparing Ġdifferences Ġin Ġwhat Ġwords Ġare Ġmost Ġclosely Ġassociated Ġwith Ġrelated Ġgroup Ġlabels Ġcan Ġreveal Ġmeaning Ġvariation Ġbetween Ġthese Ġlabels , Ġeven Ġif Ġthey Ġrefer Ġto Ġsimilar Ġpopulations . </s> We Ġfirst Ġpre process Ġthe Ġdata Ġby Ġlower c asing , Ġremoving Ġnumbers , Ġand Ġremoving Ġpunct uation . ĠWe Ġthen Ġuse Ġthe Ġword 2 vec Ġskip - gram Ġmodel Ġto Ġcreate Ġword Ġembed d ings ĠB IB REF 60 . ĠThe Ġresulting Ġvector Ġspaces Ġhave Ġ100 Ġdimensions , Ġand Ġwe Ġspecify Ġthe Ġfollowing Ġadditional Ġparameters Ġto Ġthe Ġmodel : Ġcontext Ġwindow Ġof Ġsize Ġ10 , Ġminimum Ġfrequency Ġof Ġ5 Ġfor Ġa Ġword Ġto Ġbe Ġincluded , Ġ10 Ġnegative Ġsamples , Ġ10 Ġiterations , Ġand Ġa Ġsampling Ġrate Ġof Ġ$ 10 ^{ - 4 } $. ĠWe Ġthen Ġuse Ġcos ine Ġsimilarity Ġto Ġdetermine Ġa Ġgroup Ġlabel 's Ġnearest Ġneighbors Ġin Ġthe Ġvector Ġspace . ĠFor Ġour Ġdi ach ronic Ġanalysis , Ġwe Ġfirst Ġtrain Ġword 2 vec Ġon Ġthe Ġentire Ġcorpus , Ġand Ġthen Ġuse Ġthe Ġresulting Ġvectors Ġto Ġinitialize Ġword 2 vec Ġmodels Ġfor Ġeach Ġyear Ġof Ġdata Ġin Ġorder Ġto Ġencourage Ġco herence Ġand Ġstability Ġacross Ġyears . ĠAfter Ġtraining Ġword 2 vec , Ġwe Ġzero - center Ġand Ġnormal ize Ġall Ġembed d ings Ġto Ġalleviate Ġthe Ġhub ness Ġproblem ĠB IB REF 61 . </s> We Ġthen Ġidentify Ġvectors Ġfor Ġgroup Ġlabels Ġby Ġtaking Ġthe Ġcent roid Ġof Ġall Ġmorph ological Ġforms Ġof Ġthe Ġlabel , Ġweighted Ġby Ġfrequency . ĠFor Ġexample , Ġthe Ġvector Ġrepresentation Ġfor Ġthe Ġlabel Ġgay Ġis Ġactually Ġthe Ġweighted Ġcent roid Ġof Ġthe Ġwords Ġgay Ġand Ġgays . ĠThis Ġenables Ġus Ġto Ġsimultaneously Ġaccount Ġfor Ġadject ival , Ġsingular Ġnominal , Ġand Ġplural Ġnominal Ġforms Ġfor Ġeach Ġsocial Ġgroup Ġlabel Ġwith Ġa Ġsingle Ġvector . ĠFinally , Ġwe Ġestimate Ġthe Ġval ence Ġfor Ġeach Ġgroup Ġlabel Ġby Ġidentifying Ġits Ġ1000 Ġnearest Ġneighbors Ġvia Ġcos ine Ġsimilarity Ġ( 1000 Ġwas Ġchosen Ġarbitrarily ), Ġand Ġcalculating Ġthe Ġaverage Ġval ence Ġof Ġall Ġneighbors Ġthat Ġappear Ġin Ġthe ĠN RC ĠV AD Ġval ence Ġlex icon . </s> We Ġalso Ġtried Ġusing Ġa Ġgroup Ġlabel 's Ġvector Ġrepresentation Ġto Ġinduce Ġa Ġval ence Ġscore Ġdirectly , Ġinstead Ġof Ġusing Ġnearest Ġneighbors Ġas Ġa Ġproxy , Ġby Ġadapting Ġthe Ġregression - based Ġsentiment Ġprediction Ġfrom ĠB IB REF 62 Ġfor Ġword Ġembed d ings . ĠWe Ġfound Ġthat Ġthis Ġapproach Ġyielded Ġsimilar Ġresults Ġas Ġanalyzing Ġnearest Ġneighbor Ġval ence Ġbut Ġwas Ġdifficult Ġto Ġqual itatively Ġinterpret . ĠMore Ġdetails Ġfor Ġand Ġresults Ġfrom Ġthis Ġtechnique Ġcan Ġbe Ġfound Ġin Ġthe ĠAppendix . </s> Oper ational izing ĠDe human ization Ġ: :: ĠDen ial Ġof ĠAgency </s> Den ial Ġof Ġagency Ġrefers Ġto Ġthe Ġlack Ġof Ġattribut ing Ġa Ġtarget Ġgroup Ġmember Ġwith Ġthe Ġability Ġto Ġcontrol Ġtheir Ġown Ġactions Ġor Ġdecisions ĠB IB REF 16 . ĠAutom atically Ġdetecting Ġthe Ġextent Ġto Ġwhich Ġa Ġwriter Ġattributes Ġcognitive Ġabilities Ġto Ġa Ġtarget Ġgroup Ġmember Ġis Ġan Ġextraordinarily Ġchallenging Ġcomputational Ġtask . ĠFortunately , Ġthe Ġsame Ġlex icons Ġused Ġto Ġoperational ize Ġnegative Ġevaluations Ġprovide Ġresources Ġfor Ġmeasuring Ġlex ical Ġsignals Ġof Ġdenial Ġof Ġagency . </s> Oper ational izing ĠDe human ization Ġ: :: ĠDen ial Ġof ĠAgency Ġ: :: ĠCon notation ĠFrames </s> As Ġin ĠSection ĠSEC REF 6 , Ġwe Ġuse ĠCon notation ĠFrames Ġto Ġquantify Ġthe Ġamount Ġof Ġagency Ġattributed Ġto Ġa Ġtarget Ġgroup . ĠWe Ġuse ĠB IB REF 25 's Ġextension Ġof ĠCon notation ĠFrames Ġfor Ġagency ĠB IB REF 25 . ĠFollowing ĠB IB REF 25 's Ġinterpretation , Ġentities Ġwith Ġhigh Ġagency Ġexert Ġa Ġhigh Ġdegree Ġof Ġcontrol Ġover Ġtheir Ġown Ġdecisions Ġand Ġare Ġactive Ġdecision - makers , Ġwhile Ġentities Ġwith Ġlow Ġagency Ġare Ġmore Ġpassive ĠB IB REF 25 . ĠThis Ġcontrast Ġis Ġparticularly Ġapparent Ġin Ġexample Ġsentences Ġsuch Ġas ĠX Ġsearched Ġfor ĠY Ġand ĠX Ġwaited Ġfor ĠY , Ġwhere Ġthe Ġverb Ġsearched Ġgives ĠX Ġhigh Ġagency Ġand Ġwaited Ġgives ĠX Ġlow Ġagency ĠB IB REF 25 . ĠAdditionally , ĠB IB REF 25 's Ġreleased Ġlex icon Ġfor Ġagency Ġindicates Ġthat Ġthe Ġsubjects Ġof Ġverbs Ġsuch Ġas Ġattack Ġand Ġpraise Ġare Ġgiven Ġhigh Ġagency , Ġwhile Ġthe Ġsubjects Ġof Ġdoubts , Ġneeds , Ġand Ġowes Ġare Ġgiven Ġlow Ġagency ĠB IB REF 25 . </s> This Ġlex icon Ġconsiders Ġthe Ġagency Ġattributed Ġto Ġsubjects Ġof Ġnearly Ġ2000 Ġtrans itive Ġand Ġintr ans itive Ġverbs . ĠTo Ġuse Ġthis Ġlex icon Ġto Ġquantify Ġdenial Ġof Ġagency Ġin Ġour Ġcorpus , Ġwe Ġextract Ġall Ġsentences ' Ġhead Ġverbs Ġand Ġtheir Ġcorresponding Ġsubjects , Ġwhere Ġthe Ġsubject Ġnoun Ġphrase Ġcontains Ġa Ġtarget Ġgroup Ġlabel . ĠUnlike ĠB IB REF 22 's Ġreal - valued ĠCon notation ĠFrames Ġlex icon Ġfor Ġperspective , Ġthe Ġagency Ġlex icon Ġonly Ġprovides Ġbinary Ġlabels , Ġso Ġwe Ġcalculate Ġthe Ġfraction Ġof Ġsubject - verb Ġpairs Ġwhere Ġa Ġsubject Ġcontaining Ġa Ġgroup Ġlabel Ġwas Ġgiven Ġhigh Ġagency Ġby Ġits Ġhead Ġverb . </s> Oper ational izing ĠDe human ization Ġ: :: ĠDen ial Ġof ĠAgency Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠDomin ance </s> The ĠN RC ĠV AD Ġlex icon 's Ġdominance Ġannotations Ġprovide Ġanother Ġresource Ġfor Ġquant ifying Ġdehuman ization ĠB IB REF 56 . ĠAs Ġwith Ġval ence , Ġthe ĠN RC ĠV AD Ġlex icon 's Ġdominance Ġdimension Ġcontains Ġreal - valued Ġscores Ġbetween Ġzero Ġand Ġone Ġfor Ġ20 , 000 ĠEnglish Ġwords . ĠHowever , Ġit Ġis Ġimportant Ġto Ġnote Ġthat Ġthe Ġdominance Ġlex icon Ġprimarily Ġcaptures Ġpower , Ġwhich Ġis Ġdistinct Ġfrom Ġbut Ġclosely Ġrelated Ġto Ġagency . ĠWhile Ġpower Ġrefers Ġto Ġone 's Ġcontrol Ġover Ġothers , Ġagency Ġrefers Ġto Ġone 's Ġcontrol Ġover Ġtheir Ġown Ġself . ĠWhile Ġthis Ġlex icon Ġis Ġa Ġproxy , Ġit Ġqual itatively Ġappears Ġto Ġcapture Ġsignals Ġof Ġdenial Ġof Ġagency ; Ġthe Ġhighest Ġdominance Ġwords Ġare Ġpowerful , Ġleadership , Ġsuccess , Ġand Ġgovern , Ġwhile Ġthe Ġlowest Ġdominance Ġwords Ġare Ġweak , Ġfrail , Ġempty , Ġand Ġpenn iless . ĠWe Ġthus Ġtake Ġthe Ġsame Ġapproach Ġas Ġin ĠSection ĠSEC REF 10 Ġwith Ġthe Ġsame Ġvector Ġspace Ġmodels . ĠHowever , Ġwe Ġnow Ġcalculate Ġthe Ġaverage Ġdominance Ġscore Ġof Ġthe Ġ1000 Ġnearest Ġneighbors Ġto Ġeach Ġgroup Ġlabel Ġvector Ġrepresentation . </s> As Ġin ĠSection ĠSEC REF 10 , Ġwe Ġalso Ġinduced Ġa Ġdominance Ġscore Ġdirectly Ġfrom Ġa Ġgroup Ġlabel 's Ġvector Ġrepresentation Ġby Ġadapting Ġthe Ġregression - based Ġsentiment Ġprediction Ġfrom ĠB IB REF 62 Ġfor Ġword Ġembed d ings . ĠMore Ġdetails Ġand Ġresults Ġfor Ġthis Ġtechnique Ġcan Ġbe Ġfound Ġin Ġthe ĠAppendix . </s> Oper ational izing ĠDe human ization Ġ: :: ĠMoral ĠDis g ust </s> In Ġorder Ġto Ġoperational ize Ġthe Ġmoral Ġdisgust Ġcomponent Ġof Ġdehuman ization Ġwith Ġlex ical Ġtechniques , Ġwe Ġdraw Ġinspiration Ġfrom ĠMoral ĠFound ations Ġtheory , Ġwhich Ġpost ulates Ġthat Ġthere Ġare Ġfive Ġdimensions Ġof Ġmoral Ġintu itions : Ġcare , Ġfairness / pro portion ality , Ġloyalty / ing roup , Ġauthority / respect , Ġand Ġsanct ity / p urity ĠB IB REF 63 . ĠThe Ġnegative Ġend Ġof Ġthe Ġsanct ity / p urity Ġdimension Ġcorresponds Ġto Ġmoral Ġdisgust . ĠWhile Ġwe Ġdo Ġnot Ġdirectly Ġincorporate ĠMoral ĠFound ations ĠTheory Ġin Ġour Ġframework Ġfor Ġdehuman ization , Ġwe Ġutilize Ġlex icons Ġcreated Ġby ĠB IB REF 64 Ġcorresponding Ġto Ġeach Ġmoral Ġfoundation . ĠThe Ġdictionary Ġfor Ġmoral Ġdisgust Ġincludes Ġover Ġthirty Ġwords Ġand Ġstems , Ġincluding Ġdisgust *, Ġsin , Ġper vert , Ġand Ġobsc en * Ġ( the Ġaster isks Ġindicate Ġthat Ġthe Ġdictionary Ġincludes Ġall Ġwords Ġcontaining Ġthe Ġpreceding Ġprefix , Ġsuch Ġas Ġboth Ġobscene Ġand Ġobsc en ity ). </s> We Ġopt Ġfor Ġa Ġvector Ġapproach Ġinstead Ġof Ġcounting Ġraw Ġfrequencies Ġof Ġmoral Ġdisgust - related Ġwords Ġbecause Ġwords Ġthat Ġexplicitly Ġinvoke Ġmoral Ġdisgust Ġare Ġvery Ġsparse Ġin Ġour Ġnews Ġcorpus . ĠFurthermore , Ġvectors Ġare Ġcapable Ġof Ġcapturing Ġassociations Ġwith Ġthe Ġsocial Ġgroup Ġlabel Ġitself , Ġwhile Ġcounts Ġin Ġparagraphs Ġand Ġfull Ġnews Ġarticles Ġwould Ġnot Ġdirectly Ġcapture Ġsuch Ġassociations . ĠUsing Ġthe Ġword Ġembed d ings Ġfrom ĠSection ĠSEC REF 10 , Ġwe Ġconstruct Ġa Ġvector Ġto Ġrepresent Ġthe Ġconcept Ġof Ġmoral Ġdisgust , Ġwhich Ġis Ġthe Ġaverage Ġof Ġthe Ġcorresponding Ġvectors Ġfor Ġall Ġwords Ġin Ġthe ĠâĢ ľ M oral ĠDis g ust " Ġdictionary , Ġweighted Ġby Ġword Ġfrequency . ĠThis Ġmethod Ġof Ġcreating Ġa Ġvector Ġfrom Ġthe ĠMoral ĠFound ations Ġdictionary Ġresembles Ġthat Ġused Ġby ĠB IB REF 65 . ĠWe Ġcan Ġthen Ġanalyze Ġimplicit Ġassociations Ġbetween Ġa Ġsocial Ġgroup Ġand Ġmoral Ġdisgust Ġby Ġcalculating Ġthe Ġcos ine Ġsimilarity Ġbetween Ġthe Ġgroup Ġlabel 's Ġvector Ġand Ġthe ĠMoral ĠDis g ust Ġconcept Ġvector , Ġwhere Ġa Ġhigher Ġsimilarity Ġsuggests Ġcloser Ġassociations Ġbetween Ġthe Ġsocial Ġgroup Ġand Ġmoral Ġdisgust . ĠAs Ġpreviously , Ġeach Ġgroup Ġlabel 's Ġvector Ġis Ġthe Ġaverage Ġof Ġits Ġmorph ological Ġvariants ' Ġvectors , Ġweighted Ġby Ġfrequency . </s> Oper ational izing ĠDe human ization Ġ: :: ĠVer min Ġas Ġa ĠDe human izing ĠMet aph or </s> Met aph ors Ġcomparing Ġhumans Ġto Ġver min Ġhave Ġbeen Ġespecially Ġprominent Ġin Ġdehuman izing Ġgroups Ġthroughout Ġhistory ĠB IB REF 4 , ĠB IB REF 15 . ĠEven Ġif Ġa Ġmarginalized Ġsocial Ġgroup Ġis Ġnot Ġdirectly Ġequ ated Ġto Ġver min Ġin Ġthe Ġpress , Ġthis Ġmetaphor Ġmay Ġbe Ġinvoked Ġin Ġmore Ġsubtle Ġways , Ġsuch Ġas Ġthrough Ġthe Ġuse Ġof Ġverbs Ġthat Ġare Ġalso Ġassociated Ġwith Ġver min Ġ( like Ġsc urry Ġas Ġopposed Ġto Ġthe Ġmore Ġneutral Ġhurry ) ĠB IB REF 66 . ĠWhile Ġthere Ġis Ġsome Ġnatural Ġlanguage Ġprocessing Ġwork Ġon Ġthe Ġcomplex Ġtask Ġof Ġmetaphor Ġdetection Ġ( e . g . ĠB IB REF 67 , ĠB IB REF 67 ), Ġsuch Ġsystems Ġwould Ġnot Ġbe Ġable Ġto Ġeasily Ġquantify Ġsuch Ġindirect Ġassociations . </s> We Ġthus Ġquantify Ġthe Ġmetaph orical Ġrelationship Ġbetween Ġa Ġsocial Ġgroup Ġand Ġver min Ġby Ġcalculating Ġsimilarities Ġbetween Ġthese Ġconcepts Ġin Ġa Ġdistribution al Ġsemantic Ġvector Ġspace , Ġas Ġwe Ġdid Ġwith Ġmoral Ġdisgust Ġin ĠSection ĠSEC REF 14 . ĠWe Ġcreate Ġa ĠVer min Ġconcept Ġvector Ġby Ġcalculating Ġthe Ġaverage Ġof Ġthe Ġfollowing Ġver min Ġwords ' Ġvectors , Ġweighted Ġby Ġfrequency : Ġver min , Ġrodent ( s ), Ġrat ( s ) Ġmice , Ġcock ro aches , Ġterm ite ( s ), Ġbed bug ( s ), Ġfle as Ġ. ĠWe Ġdo Ġnot Ġinclude Ġthe Ġsingular Ġmouse Ġor Ġfle a Ġbecause Ġnon - ver min Ġsenses Ġof Ġthose Ġwords Ġwere Ġmore Ġfrequent Ġ( computer Ġmouse Ġand Ġfle a Ġmarket , Ġrespectively ), Ġand Ġword 2 vec Ġdoes Ġnot Ġaccount Ġfor Ġpoly se my . ĠWe Ġcalculate Ġcos ine Ġsimilarity Ġbetween Ġeach Ġsocial Ġgroup Ġlabel Ġand Ġthe ĠVer min Ġconcept Ġvector . ĠA Ġlarge Ġcos ine Ġsimilarity Ġbetween Ġa Ġlabel Ġand Ġthe ĠVer min Ġvector Ġsuggests Ġthat Ġthe Ġsocial Ġgroup Ġis Ġclosely Ġassociated Ġwith Ġver min . </s> Table ĠT AB REF 18 Ġprovides Ġan Ġoverview Ġof Ġthe Ġfour Ġelements Ġof Ġdehuman ization Ġthat Ġwe Ġstudy Ġand Ġthe Ġlex ical Ġtechniques Ġused Ġto Ġquantify Ġthem . </s> Data </s> The Ġdata Ġfor Ġour Ġcase Ġstudy Ġspans Ġover Ġthirty Ġyears Ġof Ġarticles Ġfrom Ġthe ĠNew ĠYork ĠTimes , Ġfrom ĠJanuary Ġ1986 Ġto ĠDecember Ġ2015 , Ġand Ġwas Ġoriginally Ġcollected Ġby ĠB IB REF 68 ĠB IB REF 68 . ĠThe Ġarticles Ġcome Ġfrom Ġall Ġsections Ġof Ġthe Ġnewspaper , Ġsuch Ġas ĠâĢ ľ World ", ĠâĢ ľ New ĠYork Ġ& ĠRegion ", ĠâĢ ľ O pin ion ", ĠâĢ ľ Style ", Ġand ĠâĢ ľ Sports ". ĠOur Ġdistribution al Ġsemantic Ġmethods Ġrely Ġon Ġall Ġof Ġthe Ġavailable Ġdata Ġin Ġorder Ġto Ġobtain Ġthe Ġmost Ġfine - gr ained Ġunderstanding Ġof Ġthe Ġrelationships Ġbetween Ġwords Ġpossible . ĠFor Ġthe Ġother Ġtechniques , Ġwe Ġextract Ġparagraphs Ġcontaining Ġany Ġword Ġfrom Ġa Ġpredetermined Ġlist Ġof ĠLG TB Q Ġterms Ġ( shown Ġin ĠTable ĠT AB REF 19 ). </s> Each Ġacronym Ġlabel Ġwas Ġmatched Ġinsensitive Ġto Ġcase Ġand Ġpunct uation . ĠSome Ġcurrently Ġprominent ĠLGBTQ Ġterms , Ġsuch Ġas Ġqueer Ġand Ġtrans Ġwere Ġnot Ġincluded Ġin Ġthis Ġstudy , Ġas Ġother Ġsenses Ġof Ġthese Ġwords Ġwere Ġconsiderably Ġmore Ġfrequent Ġin Ġearlier Ġyears . ĠWe Ġfurther Ġfiltered Ġout Ġparagraphs Ġfrom Ġsections Ġthat Ġtypically Ġdid Ġnot Ġper tain Ġto Ġnews , Ġsuch Ġas ĠâĢ ľ Ar ts ", ĠâĢ ľ The ater ", Ġand ĠâĢ ľ M ovies ". ĠWhile Ġthese Ġsections Ġcould Ġprovide Ġvaluable Ġinformation , Ġwe Ġdecided Ġto Ġfocus Ġon Ġrepresentation Ġof ĠLGBTQ Ġgroups Ġin Ġmore Ġnews - related Ġcontexts . ĠFurthermore , Ġdue Ġto Ġthe Ġoriginal Ġnewspaper Ġlayout Ġand Ġsubsequent Ġdigital Ġprocessing , ĠArts Ġsections Ġoften Ġcontained Ġextremely Ġlong Ġparagraphs Ġcontaining Ġnumerous Ġlistings Ġand Ġschedules . ĠRem oving Ġthese Ġsections Ġsubstantially Ġsimplified Ġdata Ġprocessing . </s> A Ġchallenging Ġquestion Ġwhen Ġanalyzing Ġmass Ġmedia Ġfor Ġsubjective Ġattitudes Ġis Ġdeciding Ġwhose Ġperspective Ġwe Ġwant Ġto Ġcapture : Ġan Ġindividual Ġreporter , Ġthe Ġinstitution , Ġor Ġsociety Ġat Ġlarge ? ĠIn Ġthis Ġcase Ġstudy , Ġwe Ġaim Ġto Ġidentify Ġthe Ġinstitution 's Ġdehuman izing Ġattitudes Ġtowards ĠLGBTQ Ġpeople . ĠWe Ġrepresent Ġthe ĠNew ĠYork ĠTimes Ġinstitution Ġas Ġa Ġcombination Ġof Ġthe Ġjournalists ' Ġwords Ġin Ġnews Ġarticles , Ġdirect Ġquotes , Ġparaph r ases Ġfrom Ġinterviews , Ġand Ġpublished Ġopinion Ġarticles . ĠTherefore , Ġdespite Ġour Ġnews Ġfocus , Ġwe Ġinclude Ġdata Ġfrom ĠâĢ ľ O pin ion " Ġsections ; Ġwhile Ġopinion Ġarticles Ġare Ġstyl istically Ġdifferent Ġfrom Ġtraditional Ġjournalistic Ġreporting Ġdue Ġto Ġmore Ġovert Ġbiases Ġand Ġarguments , Ġthese Ġarticles Ġare Ġimportant Ġin Ġconstructing Ġthe Ġinstitution 's Ġperspective . ĠIn Ġaddition , Ġwe Ġconsider Ġall Ġtext Ġin Ġeach Ġrelevant Ġparagraph , Ġincluding Ġquotes Ġand Ġparaph r ases , Ġbecause Ġthey Ġare Ġimportant Ġto Ġa Ġnewspaper 's Ġframing Ġof Ġan Ġissue , Ġas Ġparticular Ġquotes Ġrepresenting Ġspecific Ġstances Ġare Ġintentionally Ġincluded Ġor Ġexcluded Ġfrom Ġany Ġgiven Ġarticle ĠB IB REF 69 . ĠHowever , Ġthis Ġapproach Ġis Ġsometimes Ġproblematic Ġwhen Ġthe Ġnewspaper Ġquotes Ġclearly Ġextreme Ġpositions Ġthat Ġthey Ġare Ġunlikely Ġto Ġagree Ġwith , Ġand Ġwe Ġdiscuss Ġthis Ġissue Ġthroughout Ġour Ġqualitative Ġanalysis . </s> We Ġrefer Ġto Ġthe Ġremaining Ġsubset Ġof Ġthe ĠNew ĠYork ĠTimes Ġdata Ġafter Ġfiltering Ġas Ġthe ĠLGBTQ Ġcorpus . ĠThe ĠLGBTQ Ġcorpus Ġconsists Ġof Ġ93 , 9 77 Ġparagraphs Ġand Ġ7 . 36 Ġmillion Ġtokens . ĠHowever , Ġa Ġlarge Ġincrease Ġin Ġreporting Ġon ĠLGBTQ - related Ġissues Ġhas Ġled Ġto Ġa Ġhighly Ġskewed Ġdistribution Ġin Ġthe Ġamount Ġof Ġdata Ġover Ġyears , Ġwith Ġ1986 Ġcontaining Ġthe Ġleast Ġdata Ġ( 11 44 Ġparagraphs Ġand Ġ73 , 549 Ġtokens ) Ġand Ġ2012 Ġcontaining Ġthe Ġmost Ġ( 59 24 Ġparagraphs Ġand Ġ465 , 254 Ġtokens ). </s> For Ġall Ġexperiments , Ġwe Ġalso Ġinclude Ġresults Ġfor Ġthe Ġterms ĠAmerican Ġand ĠAmericans . ĠOur Ġprimary Ġmotivation Ġfor Ġincluding ĠAmerican ( s ) Ġis Ġto Ġcontrast Ġchanges Ġin ĠLGBTQ Ġlabels ' Ġrepresentation Ġin Ġthe ĠNew ĠYork ĠTimes Ġwith Ġanother Ġsocial Ġgroup Ġlabel Ġthat Ġappears Ġin Ġsufficiently Ġdifferent Ġcontexts . ĠThis Ġensures Ġthat Ġthe Ġchanges Ġwe Ġfind Ġin Ġdehuman izing Ġlanguage Ġtowards ĠLGBTQ Ġgroups Ġdo Ġnot Ġapply Ġuniformly Ġto Ġall Ġsocial Ġgroups , Ġand Ġare Ġthus Ġnot Ġmerely Ġan Ġartifact Ġof Ġthe Ġpublication 's Ġoverall Ġlanguage Ġchange . ĠWhile Ġa Ġnatural ĠâĢ ľ control " Ġvariable Ġwould Ġbe Ġlabels Ġsuch Ġas Ġstraight Ġor Ġheterosexual , Ġwe Ġfound Ġthat Ġthese Ġterms Ġonly Ġoccurred Ġwithin Ġdiscussions Ġof ĠLGBTQ Ġcommunities , Ġlikely Ġbecause Ġthey Ġname Ġsocially Ġunmarked Ġcategories . ĠWe Ġalso Ġconsidered Ġcomparing ĠLGBTQ Ġlabels Ġto Ġperson / people , Ġbut Ġwe Ġfound Ġthat Ġthe Ġword Ġembed ding - based Ġexperiments Ġwere Ġparticularly Ġsensitive Ġto Ġsynt actic Ġforms , Ġso Ġwe Ġselected Ġa Ġword Ġthat Ġbehaves Ġmore Ġsimilarly Ġsynt act ically Ġto Ġgay Ġand Ġhomosexual , Ġparticularly Ġby Ġhaving Ġboth Ġnominal Ġand Ġadject ival Ġuses . ĠNevertheless , Ġit Ġis Ġimportant Ġto Ġnote Ġthat ĠAmerican ( s ) Ġis Ġby Ġno Ġmeans Ġa Ġneutral Ġcontrol Ġvariable . ĠBecause Ġof Ġits Ġin - group Ġstatus Ġfor Ġthe ĠNew ĠYork ĠTimes Ġ( a ĠU . S . Ġinstitution ), Ġwe Ġexpect Ġour Ġmeasurements Ġto Ġshow Ġthat ĠAmerican ( s ) Ġappears Ġin Ġmore Ġhuman izing Ġcontexts Ġthan ĠLGBTQ Ġgroup Ġlabels ; Ġhowever , Ġwe Ġdo Ġnot Ġexpect Ġto Ġfind Ġsubstantial Ġchanges Ġin Ġthe Ġuse Ġof ĠAmerican ( s ) Ġover Ġtime . </s> Figure ĠFIG REF 20 Ġshows Ġthe Ġcounts Ġof Ġgroup Ġlabels Ġfor Ġeach Ġyear Ġin Ġthe ĠNew ĠYork ĠTimes Ġfrom Ġ1986 Ġto Ġ2015 . ĠFor Ġvisualization Ġpurposes , Ġonly Ġwords Ġwith Ġa Ġtotal Ġcount Ġgreater Ġthan Ġ1000 Ġare Ġshown . ĠNotably , Ġthe Ġrelative Ġfrequency Ġof Ġhomosexual Ġdecreased Ġsubstantially Ġover Ġtime , Ġwhile Ġgay , Ġlesbian , Ġand Ġbisexual Ġare Ġmore Ġfrequent Ġin Ġlater Ġyears . ĠWe Ġalso Ġsee Ġthe Ġemergence Ġof Ġthe Ġterms Ġl g bt Ġand Ġtransgender Ġafter Ġ2000 . ĠAgg reg ated Ġcounts Ġover Ġall Ġyears Ġfor Ġeach ĠLGBTQ Ġlabel Ġcan Ġbe Ġfound Ġin Ġthe ĠAppendix . </s> Results Ġ: :: ĠWord ĠEmb edd ings </s> Using Ġall Ġof Ġthe ĠNew ĠYork ĠTimes Ġdata Ġavailable , Ġwe Ġcreated Ġvector Ġspace Ġmodels Ġfor Ġeach Ġyear Ġusing Ġthe Ġmethods Ġdescribed Ġin ĠSection ĠSEC REF 10 . ĠTable ĠT AB REF 22 Ġshows Ġthe Ġten Ġnearest Ġneighboring Ġwords Ġ( by Ġcos ine Ġsimilarity ) Ġto Ġour Ġvector Ġrepresentation Ġof Ġall ĠLGBTQ Ġterms , Ġwhich Ġis Ġthe Ġweighted Ġaverage Ġof Ġthe Ġembed d ings Ġof Ġthe ĠLGBTQ Ġterms Ġfrom ĠTable ĠT AB REF 19 . ĠFor Ġvisual Ġconvenience , Ġwords Ġthat Ġoccurred Ġfewer Ġthen Ġten Ġtimes Ġand Ġproper Ġnames Ġare Ġnot Ġshown . ĠWe Ġalso Ġfiltered Ġout Ġother ĠLGBTQ Ġlabels Ġand Ġforms Ġof Ġthe Ġword Ġheterosexual Ġfrom Ġthese Ġlists , Ġas Ġthese Ġwords Ġwere Ġcomm om Ġneighbors Ġfor Ġall Ġterms Ġacross Ġall Ġyears . </s> Table ĠT AB REF 22 Ġshows Ġthat Ġin Ġ1986 , ĠLGBTQ Ġgroups Ġwere Ġmost Ġhighly Ġassociated Ġwith Ġwords Ġthat Ġoften Ġconvey Ġa Ġsense Ġof Ġsexual Ġdev ian cy , Ġincluding Ġprom isc uity , Ġprom isc uous , Ġpolygamy , Ġbest ial ity , Ġand Ġpornography . ĠThese Ġassociations Ġsuggest Ġthat ĠLGBTQ Ġpeople Ġwere Ġdehuman ized Ġto Ġsome Ġextent Ġat Ġthis Ġtime , Ġand Ġtheir Ġidentities Ġwere Ġnot Ġfully Ġrecognized Ġor Ġvalued . ĠThis Ġshifted Ġby Ġ2000 , Ġwhere Ġwe Ġno Ġlonger Ġsee Ġassociations Ġbetween ĠLGBTQ Ġgroups Ġand Ġideas Ġthat Ġevoke Ġmoral Ġdisgust . ĠInstead , Ġthe Ġ2000 Ġvector Ġspace Ġshows Ġthat ĠLGBTQ Ġpeople Ġhave Ġbecome Ġmore Ġassociated Ġwith Ġcivil Ġrights Ġissues Ġ( suggest ed Ġby Ġinter racial , Ġhomophobia , Ġand Ġdiscrimination ). ĠThe Ġwords Ġord ination Ġand Ġord aining Ġlikely Ġappear Ġdue Ġto Ġmajor Ġcontroversies Ġthat Ġarose Ġat Ġthis Ġtime Ġabout Ġwhether ĠLGBTQ Ġpeople Ġshould Ġbe Ġpermitted Ġto Ġbe Ġordained . ĠWe Ġalso Ġsee Ġsome Ġindications Ġof Ġself - ident ification Ġwith Ġthe Ġterm Ġopenly . ĠFinally , Ġwe Ġsee Ġa Ġslight Ġshift Ġtowards Ġassociations Ġwith Ġidentity Ġin Ġ2015 , Ġwith Ġnearby Ġwords Ġincluding Ġnont rans gender , Ġcloset ed , Ġequality , Ġand Ġsexuality . ĠCur iously , Ġthe Ġword Ġabortion Ġis Ġa Ġnearby Ġterm Ġfor Ġall Ġthree Ġyears . ĠPerhaps Ġthis Ġis Ġbecause Ġopinions Ġtowards Ġabortion Ġand ĠLGBTQ Ġrights Ġseem Ġto Ġbe Ġdivided Ġalong Ġsimilar Ġpartisan Ġlines . </s> Table ĠT AB REF 23 Ġshows Ġthe Ġten Ġnearest Ġneighboring Ġwords Ġto Ġour Ġrepresentations Ġof Ġgay Ġand Ġhomosexual Ġ( which Ġare Ġweighted Ġaverages Ġof Ġtheir Ġsingular / ad ject ival Ġand Ġplural Ġforms ' Ġvectors ). ĠProper Ġnames , Ġwords Ġappearing Ġless Ġthan Ġten Ġtimes Ġthat Ġyear , Ġother ĠLGBTQ Ġterms , Ġand Ġforms Ġof Ġheterosexual Ġare Ġagain Ġfiltered Ġout Ġfor Ġconvenience Ġof Ġvisualization Ġand Ġinterpretation . ĠTable ĠT AB REF 23 Ġreveals Ġvariation Ġin Ġsocial Ġmeaning Ġbetween Ġgay Ġand Ġhomosexual Ġfrom Ġas Ġearly Ġas Ġ1986 Ġdespite Ġden ot ational Ġsimilarity , Ġand Ġthese Ġdifferences Ġintensify Ġover Ġtime . ĠIn Ġ1986 , Ġgay Ġis Ġassociated Ġwith Ġterms Ġof Ġdiscrimination , Ġcivil Ġrights Ġand Ġactivism , Ġsuch Ġas Ġhomophobia , Ġfeminist , Ġsuff rage , Ġsexism , Ġand Ġa . c . l . u Ġ( American ĠCivil ĠLiberties ĠUnion , Ġa Ġnonprofit Ġorganization ). ĠOn Ġthe Ġother Ġhand , Ġhomosexual Ġis Ġprimarily Ġassociated Ġwith Ġwords Ġrelated Ġto Ġsexual Ġactivity Ġ( e . g . Ġprom isc uity , Ġanal , Ġintercourse , Ġconsent ing ). </s> In Ġ1986 , Ġthis Ġpattern Ġmay Ġbe Ġlikely Ġbecause Ġof Ġdiscussions Ġof Ġsexual Ġactivity Ġin Ġthe Ġcontext Ġof Ġthe ĠAIDS Ġepidemic , Ġbut Ġwe Ġcontinue Ġto Ġobserve Ġthe Ġpe j oration Ġof Ġthe Ġword Ġhomosexual Ġover Ġtime Ġand Ġits Ġincreased Ġuse Ġin Ġdehuman izing Ġcontexts . ĠIn Ġ2000 , Ġnearest Ġneighbors Ġof Ġthe Ġword Ġhomosexual Ġinclude Ġdev iant , Ġimmoral , Ġand Ġcriminal izing . ĠWhile Ġgay Ġbecomes Ġmore Ġassociated Ġwith Ġissues Ġrelated Ġto Ġmarriage Ġequality Ġand Ġidentity Ġin Ġ2015 , Ġhomosexual Ġbecomes Ġextremely Ġassociated Ġwith Ġmoral Ġdisgust Ġand Ġillicit Ġactivity , Ġwith Ġnearest Ġneighbors Ġincluding Ġbest ial ity , Ġpedoph ilia , Ġadultery , Ġinf antic ide , Ġand Ġabhor rent . </s> This Ġqualitative Ġanalysis Ġof Ġword Ġembed ding Ġneighbors Ġreveals Ġsignificant Ġvariation Ġand Ġchange Ġin Ġthe Ġsocial Ġmeanings Ġassociated Ġwith ĠLGBTQ Ġgroup Ġlabels , Ġwith Ġclear Ġrelationships Ġto Ġdehuman izing Ġlanguage . ĠWe Ġwill Ġnow Ġpresent Ġour Ġquantitative Ġresults Ġfor Ġmeasuring Ġeach Ġcomponent Ġof Ġdehuman ization . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠPar agraph - level ĠVal ence ĠAnalysis Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 27 Ġshows Ġthe Ġaverage Ġval ence Ġfor Ġparagraphs Ġcontaining ĠLGBTQ Ġlabels Ġ( and ĠAmerican ( s ) Ġfor Ġcomparison ), Ġwhere Ġa Ġparagraph 's Ġval ence Ġis Ġsimply Ġthe Ġaverage Ġval ence Ġover Ġits Ġwords Ġ( or Ġle mm as ) Ġthat Ġappear Ġin Ġthe ĠN RC ĠV AD ĠVal ence ĠLex icon . ĠThe ĠN RC ĠV AD Ġlex icons Ġactually Ġcontain Ġseveral ĠLGBTQ Ġterms , Ġwhich Ġall Ġhave Ġlower Ġthan Ġthe Ġaverage Ġval ence Ġscore Ġof Ġ0 . 5 : Ġtranssexual Ġ( 0 . 264 ), Ġhomosexual Ġ( 0 . 333 ), Ġlesbian Ġ( 0 . 385 ), Ġgay Ġ( 0 . 388 ), Ġand Ġbisexual Ġ( 0 . 438 ). ĠThese Ġvalues Ġcontrast Ġstark ly Ġwith Ġmore Ġpositively - val enced Ġentries Ġin Ġthe Ġlex icon , Ġsuch Ġas Ġheterosexual Ġ( 0 . 561 ), Ġperson Ġ( 0 . 646 ), Ġhuman Ġ( 0 . 767 ), Ġman Ġ( 0 . 688 ), Ġand Ġwoman Ġ( 0 . 8 65 ). ĠThese Ġdisparities Ġlikely Ġreveal Ġbiases Ġamong Ġthe Ġhuman Ġannot ators Ġwhose Ġjudgments Ġwere Ġused Ġto Ġconstruct Ġthe ĠN RC ĠV AD Ġlex icon ĠB IB REF 56 . ĠWhile Ġthe Ġlex icon Ġmay Ġitself Ġbe Ġan Ġinteresting Ġartifact Ġof Ġdehuman izing Ġattitudes Ġtowards ĠLGBTQ Ġpeople , Ġwe Ġremoved Ġthese Ġterms Ġfrom Ġthe Ġlex icon Ġbefore Ġcalculating Ġaverage Ġval ence Ġover Ġeach Ġparagraph Ġin Ġthe ĠLGBTQ Ġcorpus Ġin Ġorder Ġto Ġisolate Ġlinguistic Ġsignals Ġin Ġthe ĠNew ĠYork ĠTimes Ġdata Ġfrom Ġannotation Ġbiases . ĠWithout Ġthis Ġpre processing Ġstep , Ġthe Ġtemporal Ġtrends Ġand Ġrelative Ġdifferences Ġbetween Ġall ĠLGBTQ Ġterms , Ġgay , Ġand Ġhomosexual Ġremain Ġroughly Ġthe Ġsame , Ġbut Ġeach Ġof Ġthe ĠLGBTQ Ġlabels Ġwould Ġoccur Ġin Ġsignificantly Ġmore Ġnegative Ġparagraphs Ġthan ĠAmerican . </s> Figure ĠFIG REF 27 Ġshows Ġthe Ġparagraph Ġaverages Ġover Ġfive - year Ġranges Ġfor Ġvisualization Ġpurposes Ġprimarily Ġdue Ġto Ġdata Ġsp arsity Ġin Ġlater Ġyears Ġfor Ġhomosexual Ġ( there Ġwere Ġjust Ġ208 Ġparagraphs Ġcontaining Ġhomosexual Ġin Ġ2014 , Ġrelative Ġto Ġ3 669 Ġcontaining Ġgay Ġin Ġthe Ġsame Ġyear ). ĠAnalysis Ġof Ġoverlapping Ġconfidence Ġintervals Ġand ĠWil co xon Ġsigned - rank Ġtests Ġover Ġthe Ġmeans Ġfor Ġeach Ġof Ġthe Ġthirty Ġyears Ġindicates Ġthat Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġoccur Ġin Ġsignificantly Ġmore Ġpositive Ġparagraphs Ġthan Ġhomosexual Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠHowever , ĠAmerican Ġactually Ġoccurs Ġin Ġmore Ġnegative Ġparagraphs Ġthan Ġall ĠLGBTQ Ġterms Ġor Ġgay Ġ($ p Ġ< Ġ0 . 05 $ ). ĠA Ġlinear Ġregression Ġanalysis Ġover Ġall Ġyears Ġreveals Ġthat Ġall ĠLGBTQ Ġterms , Ġgay , Ġand Ġhomosexual Ġall Ġsignificantly Ġincrease Ġin Ġparagraph - level Ġval ence Ġover Ġtime Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠHowever , Ġwhen Ġconsidering Ġjust Ġthe Ġlast Ġ15 Ġyears , Ġgay Ġstill Ġsignificantly Ġincreases Ġin Ġparagraph - level Ġval ence , Ġwhile Ġhomosexual Ġmay Ġbe Ġtrending Ġdownward , Ġalthough Ġthis Ġtrend Ġdoes Ġnot Ġreach Ġsignificance Ġin Ġour Ġdata Ġ($ p Ġ= Ġ0 . 07 8 $ ). </s> The Ġparagraph - level Ġval ence Ġanalysis Ġshown Ġin ĠFigure ĠFIG REF 27 Ġsuggests Ġthat ĠLGBTQ Ġgroups Ġhave Ġbecome Ġincreasingly Ġpositively Ġevaluated Ġover Ġtime , Ġand Ġhave Ġthus Ġlikely Ġbeen Ġless Ġdehuman ized Ġin Ġthe ĠNew ĠYork ĠTimes . ĠHowever , Ġthe Ġlack Ġof Ġa Ġstatistically Ġsignificant Ġincrease Ġ( rather , Ġa Ġslight Ġdownward Ġtrend ) Ġin Ġaverage Ġval ence Ġfor Ġparagraphs Ġcontaining Ġhomosexual Ġbetween Ġ2001 Ġand Ġ2015 Ġsuggests Ġthat Ġattitudes Ġtowards Ġand Ġevaluations Ġof Ġpeople Ġdescribed Ġas Ġhomosexual Ġhave Ġnot Ġimproved Ġin Ġthe Ġsame Ġway Ġas Ġthose Ġdescribed Ġby Ġother ĠLGBTQ Ġlabels . </s> Finally , Ġthis Ġmeasurement Ġdoes Ġnot Ġsupport Ġour Ġinitial Ġhypothesis Ġthat ĠLGBTQ Ġgroups Ġhave Ġbeen Ġmore Ġnegatively Ġevaluated Ġthan Ġthe Ġother Ġsocial Ġgroup ĠAmerican ( s ), Ġbut Ġdoes Ġreveal Ġthat Ġthe Ġobserved Ġtrend Ġfor ĠLGBTQ Ġlabels Ġis Ġnot Ġmerely Ġan Ġartifact Ġof Ġchanging Ġreporting Ġstyles , Ġsince Ġparagraphs Ġcontaining ĠAmerican ( s ) Ġshow Ġa Ġvery Ġdifferent Ġpattern . ĠOverall , Ġthis Ġresult Ġdemonstrates Ġsubstantial Ġlanguage Ġchange Ġin Ġthe ĠNew ĠYork ĠTimes 's Ġdiscussion Ġof ĠLGBTQ Ġpeople Ġas Ġwell Ġas Ġvariation Ġin Ġthe Ġcontexts Ġwhere Ġdifferent Ġgroup Ġlabels Ġappear , Ġparticularly Ġhomosexual . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠPar agraph - level ĠVal ence ĠAnalysis Ġ: :: ĠQual itative ĠAnalysis </s> How Ġwell Ġdoes Ġparagraph - level Ġval ence Ġanalysis Ġcapture Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġand Ġthus Ġa Ġmajor Ġcomponent Ġof Ġdehuman ization ? ĠTo Ġfacilitate Ġa Ġqualitative Ġevaluation Ġof Ġthis Ġtechnique , Ġwe Ġidentify Ġseveral Ġhundred Ġparagraphs Ġwith Ġthe Ġhighest Ġand Ġlowest Ġaverage Ġval ence . ĠIt Ġappears Ġthat Ġmost Ġparagraphs Ġwith Ġhigh Ġval ence Ġscores Ġdo Ġexpress Ġpositive Ġevaluations Ġof ĠLGBTQ Ġindividuals , Ġand Ġthose Ġwith Ġlow Ġscores Ġexpress Ġnegative Ġevaluations . </s> Table ĠT AB REF 29 Ġcontain Ġexamples Ġwith Ġextremely Ġhigh Ġand Ġlow Ġval ence . ĠWe Ġidentify Ġseveral Ġmajor Ġthemes Ġfrom Ġthese Ġresults , Ġwhich Ġare Ġincluded Ġin Ġthe ĠInterpret ation Ġcolumn Ġof ĠTable ĠT AB REF 29 . ĠMost Ġparagraphs Ġwith Ġhigh Ġval ence Ġscores Ġemphasize Ġequal Ġrights , Ġwhile Ġsome Ġfocus Ġon Ġthe Ġactivities Ġof Ġadvocacy Ġorganizations . ĠOn Ġthe Ġother Ġend , Ġnumerous Ġparagraphs Ġwith Ġextremely Ġlow Ġval ence Ġtend Ġto Ġfocus Ġon Ġviolence Ġagainst ĠLGBTQ Ġpeople , Ġdiscussions Ġof Ġdisease Ġ( especially ĠAIDS ), Ġand ĠLGBTQ Ġissues Ġinternationally . ĠOther Ġthemes Ġthat Ġemerged Ġin Ġlow - val ence Ġparagraphs Ġinclude Ġreports Ġon Ġ( and Ġdirect Ġquotes Ġfrom ) Ġpublic Ġfigures Ġwho Ġdehuman ized Ġpeople Ġas Ġwell Ġas Ġportray als Ġof ĠLGBTQ Ġpeople Ġas Ġreckless , Ġirresponsible , Ġand Ġangry . </s> While Ġthis Ġtechnique Ġaccurately Ġcaptures Ġthe Ġval ence Ġfor Ġmany Ġparagraphs , Ġwe Ġalso Ġidentify Ġseveral Ġshortcomings . ĠSome Ġof Ġthe Ġextreme Ġoutl iers Ġare Ġextremely Ġshort Ġparagraphs , Ġincluding Ġsubtitles Ġwithin Ġarticles Ġwhich Ġare Ġincluded Ġas Ġparagraphs Ġin Ġthe Ġdata . ĠTable ĠT AB REF 30 Ġshows Ġseveral Ġexamples Ġthat Ġwere Ġmis character ized Ġby Ġour Ġparagraph - level Ġval ence Ġanalysis Ġtechnique . ĠIn Ġaddition Ġto Ġthe Ġshort Ġsubtitles , Ġthere Ġare Ġseveral Ġparagraphs Ġwith Ġhighly Ġpositive Ġaverage Ġval ence Ġthat Ġseem Ġto Ġexpress Ġmore Ġnegative Ġevaluations Ġof ĠLGBTQ Ġpeople . ĠIn Ġthe Ġthird Ġparagraph Ġin ĠTable ĠT AB REF 30 , Ġthe Ġval ence Ġis Ġskewed Ġby Ġthe Ġpositive Ġwords Ġsupported Ġ( 0 . 8 53 ) Ġand Ġmarriage Ġ( 0 . 8 44 ) Ġeven Ġthough Ġthe Ġparagraph Ġis Ġactually Ġdiscussing Ġlow Ġsupport Ġfor Ġgay Ġmarriage . ĠWhile Ġthe Ġfourth Ġparagraph Ġargues Ġthat Ġgay Ġcouples Ġwould Ġbe Ġsub par Ġparents Ġrelative Ġto Ġstraight Ġcouples , Ġit Ġuses Ġpositive Ġterms Ġsuch Ġas Ġlove Ġand Ġideal . ĠFurthermore , Ġkins hip Ġterms Ġtend Ġto Ġbe Ġassigned Ġhighly Ġpositive Ġvalues Ġin Ġthe ĠN RC ĠV AD ĠVal ence ĠLex icon , Ġincluding Ġchild Ġ( 0 . 9 12 ), Ġchildren Ġ( 0 . 8 57 ), Ġand Ġfamily Ġ( 0 . 968 ). ĠSimilarly , Ġeven Ġthough Ġthe Ġfinal Ġexample Ġdescribes Ġdiscrimination Ġbased Ġon Ġsexual Ġorientation , Ġthe Ġparagraph 's Ġaverage Ġval ence Ġis Ġimpacted Ġby Ġpositive Ġkins hip Ġterms Ġsuch Ġas Ġfather Ġ( 0 . 8 12 ) Ġand Ġmother Ġ( 0 . 9 31 ) Ġ. </s> Overall , Ġour Ġqualitative Ġanalysis Ġof Ġparagraph - level Ġval ence Ġscores Ġshows Ġthat Ġhighly Ġpositive Ġval ence Ġoften Ġaccompanies Ġexpressions Ġof Ġpositive Ġevaluation Ġtowards ĠLGBTQ Ġgroups , Ġand Ġlow Ġval ence Ġoften Ġaccompanies Ġexpressions Ġof Ġnegative Ġevaluation . ĠHowever , Ġparagraph - level Ġval ence Ġscores Ġare Ġalso Ġimpacted Ġby Ġspecific Ġwords Ġc ued Ġby Ġvarious Ġtopics ; Ġparagraphs Ġabout Ġsame - sex Ġmarriage Ġtend Ġto Ġbe Ġmore Ġpositive Ġbecause Ġwords Ġlike Ġmarriage , Ġmarry , Ġand Ġcouple Ġhave Ġhigh Ġval ence Ġscores Ġ( great er Ġthan Ġ0 . 8 ), Ġwhile Ġparagraphs Ġreporting Ġon Ġhate Ġcrimes Ġtend Ġto Ġbe Ġmore Ġnegative Ġbecause Ġthey Ġcontain Ġmore Ġextremely Ġlow - val ence Ġwords Ġrelated Ġto Ġcrime , Ġviolence , Ġinjury , Ġand Ġdeath . ĠFurthermore , Ġwe Ġhave Ġfound Ġthat Ġthis Ġsimple Ġlex icon - based Ġmethod Ġhas Ġdifficulties Ġin Ġdis ent angling Ġperspectives Ġwithin Ġthe Ġtext . ĠFor Ġexample , Ġeven Ġthough Ġthere Ġare Ġlinguistic Ġsignals Ġof Ġdehuman ization Ġexpressed Ġin Ġarticles Ġreporting Ġon Ġanti - LGBT Q Ġviolence Ġand Ġhomophobic Ġspeech , Ġthese Ġdehuman izing Ġattitudes Ġare Ġnot Ġnecessarily Ġfrom Ġthe Ġviewpoint Ġof Ġthe Ġjournalist Ġor Ġthe Ġinstitution . ĠNevertheless , Ġthere Ġcould Ġbe Ġan Ġoverall Ġdehuman izing Ġeffect Ġif Ġthe Ġmedia 's Ġdiscussions Ġof Ġa Ġmarginalized Ġsocial Ġgroup Ġlargely Ġemphasizes Ġevents Ġthat Ġharm Ġpeople , Ġsuch Ġas Ġviolent Ġcrimes Ġand Ġdisease . ĠIt Ġis Ġpossible Ġthat Ġrepeated Ġassociations Ġbetween ĠLGBTQ Ġidentity Ġlabels Ġand Ġsuch Ġnegative Ġcontexts Ġcontribute Ġto Ġnegative Ġevaluations Ġof ĠLGBTQ Ġgroups . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠCon notation ĠFrames Ġof ĠPerspective Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 34 Ġshows Ġthe Ġwriter 's Ġaverage Ġperspective Ġ( val ence ) Ġtowards Ġnoun Ġphrases Ġcontaining Ġeither Ġany ĠLGBTQ Ġlabels , Ġgay ( s ), Ġhomosexual ( s ), Ġor Ġthe Ġcomparison Ġgroup ĠAmerican ( s ) Ġusing Ġthe ĠCon notation ĠFrames Ġlex icon ĠB IB REF 22 . ĠNote Ġthat Ġthe Ġwide Ġvariation , Ġparticularly Ġfor Ġhomosexual , Ġis Ġlikely Ġdue Ġto Ġsp arsity , Ġas Ġlimiting Ġthe Ġcon notation Ġframes Ġanalysis Ġto Ġverbs ' Ġimmediate Ġsubject Ġand Ġdirect Ġobject Ġnoun Ġphrase Ġdepend ents Ġ( cons isting Ġof Ġonly Ġdetermin ers , Ġadject ives , Ġand Ġnoun s ) Ġgreatly Ġreduced Ġthe Ġamount Ġof Ġdata Ġfor Ġeach Ġyear . ĠFor Ġexample , Ġthere Ġwere Ġonly Ġ39 Ġtri ples Ġfor Ġhomosexual Ġin Ġ2015 . ĠWe Ġthus Ġshow Ġresults Ġaggreg ated Ġover Ġfive - year Ġintervals , Ġas Ġin ĠFigure ĠFIG REF 27 . </s> As Ġwith Ġparagraph - level Ġval ence , Ġthe Ġwriter 's Ġperspective Ġtowards Ġthe Ġlabel Ġhomosexual Ġis Ġsignificantly Ġmore Ġnegative Ġthan Ġtowards Ġgay Ġ($ Ġp Ġ< Ġ0 . 001 $ ). ĠLinear Ġregression Ġindicates Ġthat Ġperspectives Ġtowards Ġnoun Ġphrases Ġnamed Ġby Ġany ĠLGBTQ Ġterm , Ġgay , Ġand ĠAmerican Ġhave Ġall Ġsignificantly Ġincreased Ġover Ġtime Ġ($ p Ġ< Ġ0 . 01 $ ). ĠHowever , Ġthe Ġtrends Ġare Ġstill Ġquite Ġdifferent , Ġas Ġthe Ġslopes Ġfor Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġare Ġan Ġorder Ġof Ġmagnitude Ġgreater Ġthan ĠAmerican Ġ($ m Ġ= ( 1 . 1 \ pm Ġ0 . 39 )\ times Ġ10 ^{ - 4 } $ Ġfor ĠAmerican , Ġ$ m =( 1 . 4 \ pm Ġ0 . 18 )\ times Ġ10 ^{ - 3 } $ Ġfor Ġall ĠLGBTQ Ġterms , Ġand Ġ$ m =( 1 . 1 \ pm Ġ0 . 22 )\ times Ġ10 ^{ - 3 } $ Ġfor Ġgay ). ĠFurthermore , Ġthe Ġwriter 's Ġperspective Ġtowards Ġnoun Ġphrases Ġcontaining Ġhomosexual Ġhave Ġsignificantly Ġdecreased Ġover Ġtime Ġ($ p Ġ< Ġ0 . 0001 $ ). </s> Overall , Ġcon notation Ġframes ' Ġperspective Ġscores Ġreveal Ġa Ġsimilar Ġpattern Ġas Ġthe Ġparagraph - level Ġval ence Ġanalysis , Ġwhere ĠLGBTQ Ġgroups Ġoverall Ġappear Ġto Ġbe Ġmore Ġpositively Ġevaluated Ġin Ġthe ĠNew ĠYork ĠTimes Ġover Ġtime . ĠUnlike Ġgay Ġand Ġthe Ġaggreg ated Ġall ĠLGBTQ Ġterms , Ġthe Ġlabel Ġhomosexual Ġundergo es Ġpe j oration , Ġas Ġhomosexual Ġbecomes Ġincreasingly Ġused Ġwhen Ġ( impl icit ly ) Ġexpressing Ġnegative Ġattitudes Ġtowards ĠLGBTQ Ġpeople . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠCon notation ĠFrames Ġof ĠPerspective Ġ: :: ĠQual itative ĠAnalysis </s> To Ġqual itatively Ġanalyze Ġhow Ġwell Ġthe Ġcon notation Ġframes ' Ġlex icon Ġcapture Ġthe Ġnegative Ġevaluation Ġof Ġa Ġtarget Ġgroup Ġcomponent Ġof Ġdehuman ization , Ġwe Ġidentify Ġsubject - verb - object Ġtu ples Ġwhere Ġthe Ġverb Ġindicates Ġthat Ġthe Ġwriter Ġhas Ġextremely Ġpositive Ġor Ġnegative Ġperspective Ġtowards Ġeither Ġthe Ġsubject Ġor Ġobject . ĠThe Ġfirst Ġtwo Ġparagraphs Ġin ĠTable ĠT AB REF 36 Ġwere Ġidentified Ġamong Ġthose Ġwith Ġthe Ġmost Ġnegative Ġwriters ' Ġperspectives Ġtowards Ġphrases Ġcontaining ĠLGBTQ Ġlabels . ĠThe Ġfirst Ġparagraph Ġ( within Ġa Ġquote ) Ġuses Ġthe Ġphrase Ġany Ġhomosexual Ġact Ġas Ġthe Ġdirect Ġobject Ġto Ġthe Ġverb Ġcommitted , Ġwhich Ġhas Ġthe Ġeffect Ġof Ġframing Ġhomosexuality Ġas Ġa Ġcrime Ġor Ġother Ġillicit Ġactivity . ĠBy Ġdeem ing Ġgay Ġcandidates Ġunworthy Ġof Ġthe Ġpriesthood , Ġthe Ġspeaker Ġclearly Ġnegatively Ġevaluates ĠLGBTQ Ġpeople . ĠOn Ġthe Ġopposite Ġend , Ġmany Ġof Ġthe Ġparagraphs Ġlabeled Ġby Ġour Ġmethod Ġas Ġcontaining Ġextremely Ġpositive Ġperspectives Ġtowards Ġphrases Ġcontaining ĠLGBTQ Ġlabels Ġdo Ġappear Ġto Ġhave Ġpositive Ġevaluations Ġof Ġthese Ġgroups . ĠThe Ġsecond Ġand Ġthird Ġparagraphs Ġof ĠTable ĠT AB REF 36 Ġillustrate Ġthis , Ġwhere Ġthe Ġgays Ġare Ġviewed Ġpositively Ġfor Ġhaving Ġsaved Ġa Ġtown , Ġand Ġgay Ġrights Ġadvocates Ġare Ġpraised Ġfor Ġtheir Ġwork . </s> However , Ġwe Ġfound Ġseveral Ġinstances Ġwhere Ġparagraphs Ġseem Ġto Ġhave Ġbeen Ġmis label ed , Ġwhich Ġare Ġshown Ġin ĠTable ĠT AB REF 37 . ĠIn Ġthe Ġfirst Ġparagraph Ġof ĠTable ĠT AB REF 37 , Ġour Ġtechnique Ġidentifies Ġgay Ġmarriage Ġas Ġthe Ġsubject Ġof Ġdependent Ġof Ġthe Ġnegative - pers pect ive Ġverb Ġharmed , Ġbut Ġdoes Ġnot Ġaccount Ġfor Ġthe Ġpreceding Ġtext , Ġwhich Ġactually Ġreveals Ġthat Ġthe Ġparagraph Ġcontradicts Ġthe Ġpremise Ġthat Ġgay Ġmarriage Ġcauses Ġharm , Ġand Ġthus Ġdoes Ġnot Ġcontain Ġovertly Ġnegative Ġevaluations Ġof ĠLGBTQ Ġgroups Ġ( although Ġthis Ġparticular Ġexample Ġreveals Ġthe Ġdifficulty Ġof Ġoperational izing Ġthis Ġcomponent Ġbecause ĠProtect Mar riage Ġgroups Ġstrongly Ġoppose Ġsame - sex Ġmarriage Ġand Ġmay Ġitself Ġhave Ġnegative Ġevaluations Ġof ĠLGBTQ Ġpeople ). ĠThe Ġsecond Ġexample Ġsimilarly Ġshows Ġthat Ġthis Ġsimple Ġmethod Ġdoes Ġnot Ġadequately Ġaccount Ġfor Ġvarious Ġforms Ġof Ġneg ation , Ġas Ġthe Ġpositive - pers pect ive Ġverb Ġprotect Ġis Ġactually Ġneg ated . ĠThe Ġlast Ġexample Ġin ĠTable ĠT AB REF 37 Ġpresents Ġa Ġcomplex Ġcase , Ġand Ġit Ġis Ġeven Ġqual itatively Ġchallenging Ġto Ġdetermine Ġthe Ġwriter 's Ġperspective Ġtowards ĠLGBTQ Ġpeople . ĠOur Ġmethod Ġidentifies Ġgays Ġas Ġthe Ġsubject Ġof Ġthe Ġverb Ġstrengthen , Ġeven Ġthough Ġthe Ġsubject Ġshould Ġbe Ġthe Ġger und Ġallowing Ġgays Ġ( into Ġthe Ġmilitary ), Ġand Ġthe Ġlex icon 's Ġentry Ġfor Ġthe Ġwriter 's Ġperspective Ġtowards Ġthe Ġsubject Ġof Ġof Ġstrengthen Ġis Ġa Ġhighly Ġpositive Ġ0 . 7 . ĠHowever , Ġthe Ġobject Ġof Ġthis Ġverb Ġis Ġthe Ġterrorist Ġorganization ĠAl ĠQaeda ; Ġour Ġbackground Ġknowledge Ġsuggests Ġthat Ġthe Ġcapacity Ġto Ġstrengthen ĠAl ĠQaeda Ġwould Ġreflect Ġnegative Ġperspectives . ĠHowever , Ġthis Ġadditional Ġcontext Ġprovided Ġby Ġthe Ġrest Ġof Ġthe Ġparagraph Ġindicates Ġthat Ġthe Ġwriter Ġis Ġbeing Ġsarcastic Ġand Ġconsiders Ġthe Ġproposition Ġthat Ġgays Ġhave Ġany Ġimpact Ġon Ġstrengthening ĠAl ĠQaeda Ġto Ġbe Ġridiculous . ĠFinally , Ġthe Ġwriter Ġemphasizes Ġtheir Ġown Ġstance Ġin Ġopposition Ġto Ġthe ĠMissouri Ġcongressional Ġcandidate Ġby Ġcalling Ġupon Ġcommon Ġstereotypes Ġof Ġgay Ġpeople Ġbeing Ġgood Ġat Ġdancing Ġand Ġaccess or izing . </s> Me asuring Ġthe Ġcon notation Ġframes ' Ġlex icon Ġperspective Ġscores Ġover Ġverbs ' Ġsubjects Ġand Ġdirect Ġobjects Ġcannot Ġleverage Ġas Ġmuch Ġcontext Ġor Ġdata Ġas Ġmeasuring Ġval ence Ġover Ġparagraphs Ġusing Ġthe ĠN RC ĠV AD Ġlex icon Ġlabeled Ġfor Ġ20 , 000 Ġwords . ĠHowever , Ġthis Ġtechnique Ġcan Ġmake Ġmore Ġfine - gr ained Ġdistinctions Ġregarding Ġthe Ġwriter 's Ġ( and Ġinstitution 's ) Ġattitudes Ġdirected Ġtowards ĠLGBTQ Ġpeople Ġand Ġis Ġnot Ġas Ġdramatically Ġimpacted Ġby Ġthe Ġemotional Ġval ence Ġof Ġthe Ġtopic Ġdiscussed . ĠHowever , Ġwith Ġboth Ġtechniques Ġpresented , Ġwe Ġhave Ġdifficulties Ġdis ent angling Ġthe Ġjournalist 's Ġperspective Ġfrom Ġthose Ġexpressed Ġby Ġothers Ġand Ġsimply Ġreported Ġby Ġthe Ġjournalist . ĠWhile Ġremoving Ġdirect Ġquotations Ġmay Ġpartially Ġaddress Ġthis Ġissue , Ġwe Ġdeliberately Ġdid Ġnot Ġremove Ġtext Ġfrom Ġdirect Ġquotes Ġor Ġparaph r ases . ĠThe Ġjournalists Ġand Ġnewspaper Ġmake Ġintentional Ġdecisions Ġabout Ġwhat Ġtext Ġto Ġinclude Ġand Ġexclude Ġfrom Ġquotations , Ġwhich Ġcould Ġstill Ġmeaning fully Ġrepresent Ġtheir Ġperspectives Ġand Ġvalues ĠB IB REF 69 . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠVal ence Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 40 Ġshows Ġthe Ġaverage Ġval ence Ġscores Ġof Ġthe Ġ1000 Ġnearest Ġneighbors Ġto Ġthe Ġvector Ġrepresentations Ġof Ġgay , Ġhomosexual , Ġall ĠLGBTQ Ġterms , Ġand ĠAmerican Ġfor Ġeach Ġyear . ĠCons istent Ġwith Ġother Ġresults Ġfor Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġthe Ġvector Ġrepresentations Ġfor Ġall ĠLGBTQ Ġterms Ġand Ġgay Ġshow Ġsimilar Ġpatterns , Ġalthough Ġthe ĠLGBTQ Ġvector Ġmore Ġclosely Ġresembles Ġthe Ġhomosexual Ġvector Ġin Ġearlier Ġyears Ġsince Ġhomosexual Ġwas Ġmore Ġfrequently Ġused Ġin Ġall Ġdiscussions Ġof ĠLGBTQ Ġcommunities Ġat Ġthat Ġtime . ĠIn Ġcontrast Ġto Ġour Ġother Ġtechniques Ġto Ġquantify Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġthis Ġmeasurement Ġnotably Ġshows Ġthat Ġthe Ġval ence Ġof ĠAmerican 's Ġneighboring Ġwords Ġis Ġsignificantly Ġgreater Ġthan Ġany Ġof Ġthe ĠLGBTQ Ġgroup Ġrepresentations ' Ġneighbors Ġover Ġall Ġyears Ġ( Wil co xon 's Ġsigned - rank Ġtest , Ġ$ p Ġ< Ġ0 . 0001 $ ), Ġindicating Ġthat ĠAmerican Ġis Ġused Ġin Ġmore Ġpositive Ġcontexts Ġthan ĠLGBTQ Ġterms . ĠFurthermore , Ġall ĠLGBTQ Ġvectors ' Ġneighbors Ġhave Ġan Ġaverage Ġval ence Ġbelow Ġthe Ġneutral Ġ0 . 5 . ĠHowever , Ġthe Ġaverage Ġval ence Ġof Ġthe Ġaggreg ated Ġall ĠLGBTQ Ġterms Ġrepresentation 's Ġneighbors Ġsignificantly Ġincreases Ġover Ġtime Ġ($ p Ġ< Ġ0 . 0001 $ ), Ġsuggesting Ġsome Ġincreasing Ġhuman ization Ġin Ġthe Ġlanguage Ġused Ġin Ġdiscussions Ġof ĠLGBTQ Ġpeople . </s> Figure ĠFIG REF 40 Ġalso Ġreveals Ġdramatic Ġcon not ational Ġdifferences Ġbetween Ġgay Ġand Ġhomosexual . ĠAs Ġshown Ġby Ġnon - over l apping Ġconfidence Ġintervals Ġand Ġa ĠWil co xon Ġsigned - rank Ġtest , Ġthe Ġaverage Ġval ence Ġfor Ġhomosexual 's Ġneighbors Ġis Ġsignificantly Ġlower Ġthan Ġgay 's Ġneighbors Ġover Ġall Ġyears Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠGay 's Ġaverage Ġneighbor Ġval ence Ġslightly Ġincreases Ġover Ġtime , Ġbut Ġdoes Ġnot Ġreach Ġsignificance Ġ($ p Ġ= Ġ0 . 060 $ ), Ġbut Ġhomosexual 's Ġneighboring Ġwords Ġbecome Ġsignificantly Ġmore Ġnegative Ġover Ġtime Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠAnaly zing Ġthe Ġval ence Ġof Ġthe Ġnearest Ġneighbors Ġindicates Ġthat Ġhomosexual Ġhas Ġlong Ġbeen Ġused Ġin Ġmore Ġnegative Ġ( and Ġpotentially Ġdehuman izing ) Ġcontexts Ġthan Ġgay , Ġand Ġthat Ġthese Ġwords ' Ġmeanings Ġhave Ġfurther Ġdiver ged Ġas Ġthe Ġlabel Ġhomosexual Ġhas Ġbeen Ġused Ġin Ġincreasingly Ġnegative Ġcontexts Ġover Ġtime . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠVal ence Ġ: :: ĠQual itative ĠAnalysis </s> Compared Ġto Ġthe Ġprevious Ġmethods , Ġone Ġlimitation Ġof Ġusing Ġword Ġembed d ings Ġto Ġquantify Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup Ġis Ġthat Ġthe Ġembed d ings Ġare Ġnot Ġeasily Ġinterpret able Ġby Ġanalyzing Ġa Ġsmall Ġsample Ġof Ġdata . ĠInstead , Ġwe Ġassess Ġthis Ġtechnique Ġby Ġidentifying ĠLGBTQ Ġterms ' Ġnearest Ġneighbors Ġin Ġseveral Ġout lier Ġyears . ĠTo Ġfacilitate Ġthis Ġqualitative Ġanalysis , Ġwe Ġidentify Ġa Ġset Ġof Ġunique Ġnearest Ġneighbors Ġfor Ġeach ĠLGBTQ Ġlabel Ġin Ġeach Ġout lier Ġyear , Ġwhere Ġa Ġword Ġis Ġa Ġunique Ġnearest Ġneighbor Ġfor Ġa Ġgiven ĠLGBTQ Ġterm Ġand Ġyear Ġif Ġit Ġis Ġnot Ġin Ġthat Ġterm 's Ġtop Ġ1000 Ġnearest Ġneighbors Ġin Ġany Ġother Ġyear . </s> Table ĠT AB REF 42 Ġcontains Ġseveral Ġexample Ġparagraphs Ġthat Ġillustrate Ġoverarching Ġthemes Ġfor Ġthe Ġout lier Ġyears Ġ1993 , Ġ1999 , Ġand Ġ2014 . ĠIn Ġthe Ġyear Ġ1999 , Ġgay , Ġhomosexual Ġand Ġthe Ġaggreg ated Ġrepresentation Ġall ĠLGBTQ Ġterms Ġwere Ġall Ġmore Ġmore Ġclosely Ġassociated Ġwith Ġlow - val ence Ġwords Ġthan Ġin Ġalmost Ġany Ġother Ġyear . ĠWe Ġconnected Ġthis Ġfinding Ġto Ġa Ġperiod Ġof Ġintense Ġreporting Ġin Ġthe Ġmonths Ġfollowing Ġthe ĠOctober Ġ1998 Ġmurder Ġof Ġa Ġgay ĠWyoming Ġcollege Ġstudent , ĠMatthew ĠShepard , Ġwhich Ġdrew Ġnational Ġattention Ġto Ġand Ġcondemnation Ġof Ġanti - LGBT Q Ġviolence . ĠBecause Ġmany Ġoccurrences Ġof ĠLGBTQ Ġgroup Ġlabels Ġappeared Ġin Ġsimilar Ġcontexts Ġas Ġcontent Ġabout Ġthis Ġincident , Ġterms Ġdirectly Ġrelated Ġto ĠMatthew ĠShepard 's Ġcase Ġhave Ġcloser Ġvector Ġrepresentations Ġto ĠLGBTQ Ġterms Ġin Ġthis Ġyear . ĠFor Ġexample , Ġgay Ġand Ġall ĠLGBTQ Ġterms 's Ġ1000 Ġnearest Ġneighbors Ġinclude Ġw y oming Ġin Ġ1998 Ġand Ġ1999 Ġand Ġshe pard Ġfrom Ġthe Ġyears Ġ1998 - 2001 . ĠUnique Ġnearest Ġneighbors Ġfor Ġgay Ġin Ġ1999 Ġinclude Ġother Ġterms Ġthat Ġcould Ġbe Ġconnected Ġto Ġthis Ġincident , Ġincluding Ġimprisoned , Ġslander ous , Ġdisgrace ful , Ġunethical , Ġand Ġunjust ifiable . ĠNot Ġonly Ġwas ĠMatthew ĠShepard 's Ġtragic Ġmurder Ġdeeply Ġrooted Ġin Ġthe Ġdehuman ization Ġof ĠLGBTQ Ġpeople , Ġbut ĠShepard Ġwas Ġfurther Ġdehuman ized Ġin Ġthe Ġmedia 's Ġemphasis Ġon Ġthe Ġgruesome Ġdetails Ġof Ġhis Ġdeath ĠB IB REF 71 . ĠB IB REF 71 Ġargue Ġthat Ġthe Ġmedia 's Ġframing Ġof Ġthis Ġcase Ġactually Ġfurther Ġsocially Ġstigmat ized ĠLGBTQ Ġpeople . </s> Sur prisingly , Ġmeasuring Ġa Ġgroup Ġlabel 's Ġval ence Ġby Ġthe Ġval ence Ġof Ġits Ġnearest Ġword Ġembed ding Ġneighbors Ġreveals Ġthat Ġthe Ġmost Ġnegative Ġyear Ġfor Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġsince Ġ1999 Ġwas Ġ2014 , Ġthe Ġsecond Ġmost - recent Ġyear Ġof Ġdata . ĠUnique Ġnearest Ġneighbors Ġfor Ġgay Ġin Ġ2014 Ġinclude Ġharassing , Ġintimidation , Ġand Ġstigma . ĠWe Ġidentified Ġseveral Ġmajor Ġthemes Ġin Ġ2014 Ġthat Ġco - occ urred Ġwith ĠLGBTQ Ġgroup Ġlabels Ġand Ġpossibly Ġled Ġto Ġthis Ġdistribution al Ġsemantic Ġpattern , Ġprimarily Ġreporting Ġon Ġanti - LGBT Q Ġlaws Ġand Ġattitudes Ġin ĠUganda Ġand ĠRussia Ġ( particularly Ġin Ġlight Ġof Ġthe Ġ2014 ĠWinter ĠOlympics Ġin ĠSochi ). ĠWe Ġalso Ġfound Ġthat Ġthe Ġterms Ġathletes Ġand Ġwinter oly mp ics Ġappeared Ġin Ġboth Ġgay Ġand Ġall ĠLGBTQ Ġterms 's Ġnearest Ġ1000 Ġneighbors Ġin Ġ2014 Ġ( ath letes Ġonly Ġappeared Ġas Ġa Ġnearby Ġneighbor Ġfor Ġtwo Ġother Ġyears : Ġ1994 Ġand Ġ2013 ). ĠIn Ġaddition , Ġthe Ġterms ĠUganda Ġand ĠUg andan Ġare Ġamong Ġall ĠLGBTQ Ġterms Ġand Ġgay 's Ġnearest Ġ1000 Ġneighbors Ġin Ġ2014 , Ġbut Ġno Ġother Ġyear . </s> Unlike Ġin Ġ1999 Ġand Ġ2014 , ĠLGBTQ Ġterms Ġin Ġthe Ġyear Ġ1993 Ġseem Ġto Ġbe Ġassociated Ġwith Ġhigher - val ence Ġwords , Ġespecially Ġhomosexual , Ġwhose Ġnearby Ġwords Ġhad Ġthe Ġhighest Ġaverage Ġval ence Ġin Ġ1993 Ġout Ġof Ġall Ġyears . ĠHom osexual 's Ġunique Ġnearest Ġneighbors Ġin Ġ1993 Ġinclude Ġthe Ġhigh - val ence Ġwords Ġmorale , Ġpledge , Ġcivilian , Ġcompromise , Ġforce , Ġand Ġreadiness . ĠThis Ġwords Ġappear Ġto Ġbe Ġconnected Ġwith Ġnumerous Ġstories Ġin Ġ1993 Ġcovering Ġthe Ġcontroversy Ġover Ġwhether Ġor Ġnot ĠLGBTQ Ġpeople Ġshould Ġbe Ġallowed Ġto Ġserve Ġin Ġthe Ġmilitary . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠDen ial Ġof ĠAgency Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 45 Ġshows Ġthe Ġagency Ġof Ġeach Ġgroup Ġlabel Ġbased Ġon Ġits Ġhead Ġverb 's Ġentry Ġin Ġthe ĠCon notation ĠFrames Ġlex icon Ġfor Ġagency Ġand Ġthe Ġgroup Ġlabel 's Ġposition Ġrelative Ġto Ġthat Ġverb ĠB IB REF 25 . ĠAs Ġin ĠFigure ĠFIG REF 34 Ġfor Ġperspective , Ġthere Ġis Ġlarge Ġvariance Ġfor Ġeach Ġyear Ġdue Ġto Ġdata Ġsp arsity Ġwhen Ġusing Ġthe ĠCon notation ĠFrames Ġlex icon , Ġparticularly Ġfor Ġhomosexual , Ġwhich Ġis Ġconsiderably Ġless Ġfrequent Ġthan Ġgay Ġor Ġother ĠLGBTQ Ġterms Ġin Ġlater Ġyears . ĠIn Ġextracting Ġsubject - verb Ġpairs Ġfor Ġthis Ġanalysis , Ġwe Ġdecided Ġto Ġmaximize Ġprecision Ġby Ġextracting Ġonly Ġthe Ġnoun s Ġand Ġtheir Ġimmediate Ġadject ival Ġmodifiers , Ġwhich Ġlimited Ġthe Ġamount Ġof Ġdata . ĠFor Ġvisualization Ġpurposes , Ġwe Ġthus Ġshow Ġaverage Ġagency Ġover Ġfive - year Ġintervals . </s> Overall , ĠWil co xon Ġsigned - rank Ġtests Ġon Ġthe Ġmeans Ġfor Ġall Ġgroup Ġlabels Ġfor Ġeach Ġof Ġthe Ġ30 Ġyears Ġindicates Ġthat Ġgay Ġoccurs Ġin Ġcontexts Ġwith Ġsignificantly Ġhigher Ġagency Ġthan Ġhomosexual Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠAll Ġfour Ġgroup Ġlabels Ġactually Ġsignificantly Ġdecrease Ġin Ġagency Ġover Ġtime Ġaccording Ġto Ġlinear Ġregress ions Ġover Ġall Ġ30 Ġyears Ġ($ p Ġ< Ġ0 . 001 $ ), Ġbut Ġthe Ġslope Ġfor Ġhomosexual Ġis Ġmuch Ġgreater Ġ( Ġ$ m =( - 7 . 9 \ pm Ġ1 . 3 )\ times Ġ10 ^{ - 3 } $ Ġfor Ġhomosexual , Ġcompared Ġto Ġ$ m =( - 3 . 9 \ pm Ġ. 55 )\ times Ġ10 ^{ - 3 } $ Ġfor Ġgay , Ġand Ġ$ m =( - 1 . 5 \ pm Ġ. 46 )\ times Ġ10 ^{ - 3 } $ Ġfor Ġall ĠLGBTQ Ġterms ). ĠFurthermore Ġin Ġthe Ġmost Ġrecent Ġ15 Ġyears , Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġshow Ġno Ġsignificant Ġchange Ġ($ p Ġ= Ġ0 . 09 7 $ Ġfor Ġgay Ġand Ġ$ p Ġ= Ġ0 . 14 $ Ġfor Ġall ĠLGBTQ Ġterms ), Ġbut Ġhomosexual Ġstill Ġdecreases Ġsignificantly Ġin Ġagency Ġ($ p Ġ< Ġ0 . 05 $ ). </s> Figure ĠFIG REF 45 Ġsuggests Ġthat ĠLGBTQ Ġgroups Ġexperience Ġgreater Ġdenial Ġof Ġagency Ġin Ġthe ĠNew ĠYork ĠTimes Ġthan Ġthe Ġinstitution 's Ġin - group Ġidentifier ĠAmerican . ĠFurthermore , Ġpeople Ġdescribed Ġas Ġhomosexual Ġexperience Ġeven Ġmore Ġdenial Ġof Ġagency Ġthan Ġpeople Ġwho Ġare Ġdescribed Ġas Ġgay , Ġillustrating Ġhow Ġthese Ġtwo Ġterms Ġdiffer Ġwith Ġrespect Ġto Ġdehuman ization . ĠUnlike Ġthe Ġimproving Ġattitudes Ġindicated Ġby Ġour Ġanalysis Ġof Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġit Ġappears Ġthat Ġdenial Ġof Ġagency Ġincreased Ġover Ġtime Ġfor Ġall ĠLGBTQ Ġgroups . ĠHowever , Ġthe Ġrelatively Ġrapid Ġchange Ġin Ġagency Ġfor Ġhomosexual Ġis Ġconsistent Ġwith Ġour Ġother Ġresults Ġof Ġthis Ġlabel 's Ġpe j oration . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠDen ial Ġof ĠAgency Ġ: :: ĠQual itative ĠAnalysis </s> We Ġagain Ġqual itatively Ġinvestigate Ġthe Ġlabels Ġassigned Ġby Ġthis Ġtechnique Ġfor Ġa Ġsample Ġof Ġparagraphs . ĠIn Ġgeneral , Ġthe Ġbinary Ġlabels Ġfor Ġpositive Ġand Ġnegative Ġagency Ġappear Ġto Ġbe Ġreasonably Ġaccurate , Ġas Ġshown Ġby Ġthe Ġfirst Ġfour Ġexample Ġin ĠTable ĠT AB REF 47 . ĠVer bs Ġthat Ġattribute Ġhigh Ġagency Ġto Ġthe Ġsubject Ġinclude Ġdevelop Ġand Ġendorse , Ġsuggesting Ġthat Ġthe ĠLGBTQ - aligned Ġsubjects Ġare Ġin Ġcontrol Ġand Ġactively Ġmaking Ġtheir Ġown Ġdecisions . ĠOn Ġthe Ġother Ġend , ĠLGBTQ Ġpeople Ġare Ġgiven Ġlow Ġagency Ġwhen Ġpresented Ġas Ġthe Ġsubjects Ġof Ġpassive Ġverbs Ġsuch Ġas Ġface Ġand Ġacknowledge . </s> The ĠCon notation ĠFrames ĠLex icon Ġlabels Ġfor Ġagency Ġseem Ġto Ġappropriately Ġdescribe ĠLGBTQ Ġpeople 's Ġagency Ġespecially Ġaccurately Ġfor Ġlow Ġagency ; Ġwe Ġcould Ġnot Ġreadily Ġidentify Ġcount ere x amples Ġin Ġour Ġsample Ġwhere ĠLGBTQ Ġpeople Ġactually Ġwere Ġportrayed Ġwith Ġhigh Ġagency Ġbut Ġwere Ġlabeled Ġwith Ġlow Ġagency . ĠHowever , Ġwe Ġfound Ġseveral Ġmis character izations Ġin Ġwhich ĠLGBTQ Ġpeople Ġwere Ġlabeled Ġas Ġhaving Ġa Ġhigh Ġdegree Ġof Ġagency Ġbut Ġdo Ġnot Ġqual itatively Ġseem Ġto Ġbe Ġportrayed Ġas Ġparticularly Ġagent ive Ġor Ġin Ġcontrol Ġof Ġtheir Ġown Ġactions . ĠThis ĠCon notation ĠFrames Ġtechnique Ġconsidered Ġthe Ġexample Ġbelow Ġto Ġattribute Ġhigh Ġagency Ġto ĠLGBTQ Ġpeople Ġbecause Ġthe Ġword Ġhomosexual Ġappeared Ġin Ġthe Ġsubject Ġof Ġthe Ġhigh - agency Ġverb Ġviolate ; Ġhowever , Ġhomosexual Ġactually Ġmod ifies Ġrelationships , Ġnot Ġpeople Ġthemselves . ĠFurthermore , Ġthis Ġdebate Ġwithin Ġreligion Ġappears Ġto Ġbe Ġdevoid Ġof Ġinput Ġfrom ĠLGBTQ Ġpeople Ġand Ġthus Ġdoes Ġnot Ġportray Ġthem Ġas Ġparticularly Ġagent ive . </s> At Ġthe Ġsame Ġtime , Ġit Ġundersc ored Ġa Ġstark Ġdivision Ġin ĠJudaism Ġover Ġthe Ġplace Ġof Ġhomosexuals Ġin Ġsociety . ĠOrthodox Ġrabb inical Ġgroups Ġbelieve Ġthat Ġhomosexual Ġrelationships Ġviolate ĠJewish Ġlaw ... Ġ( 1996 ) </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠDomin ance Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 51 Ġshows Ġthe Ġaverage Ġdominance Ġof Ġeach Ġgroup Ġlabel 's Ġword 2 vec Ġrepresentation 's Ġ1000 Ġnearest Ġneighbors Ġaccording Ġto Ġthe ĠN RC ĠV AD ĠDomin ance ĠLex icon ĠB IB REF 56 , Ġwhich Ġwe Ġuse Ġas Ġan Ġestimate Ġof Ġthe Ġdominance Ġassociated Ġwith Ġeach Ġgroup Ġlabel . ĠAmerican Ġis Ġclearly Ġsignificantly Ġassociated Ġwith Ġgreater Ġdominance Ġthan Ġgay , Ġhomosexual , Ġand Ġall ĠLGBTQ Ġterms Ġ( Wil co xon Ġsigned - rank Ġtest ; Ġ$ p Ġ< Ġ0 . 0001 $ ), Ġand Ġgay Ġhas Ġsignificantly Ġhigher Ġdominance Ġthan Ġhomosexual Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠWhile Ġthe Ġdominance Ġassociated Ġwith Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġsignificantly Ġincreased Ġover Ġthe Ġthree Ġdecades Ġ($ p Ġ< Ġ0 . 01 $ ), Ġdominance Ġsignificantly Ġdecreased Ġfor Ġhomosexual Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠFurthermore , Ġwhile Ġthe Ġincrease Ġin Ġaverage Ġnearest Ġneighbor Ġdominance Ġdoes Ġnot Ġreach Ġsignificance Ġfor Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġin Ġthe Ġmost Ġrecent Ġ15 Ġyears Ġ($ p Ġ= Ġ0 . 47 $ Ġfor Ġall ĠLGBTQ Ġterms Ġand Ġ$ p Ġ= Ġ0 . 0 66 $ Ġfor Ġgay ), Ġhomosexual Ġsignificantly Ġdecreases Ġin Ġthat Ġtime Ġspan Ġ($ p Ġ< Ġ0 . 0001 $ ). </s> Even Ġthough Ġdominance Ġmay Ġmore Ġdirectly Ġencode Ġpower Ġrather Ġthan Ġagency , Ġthe ĠN RC ĠV AD ĠDomin ance ĠLex icon Ġis Ġa Ġuseful Ġresource Ġfor Ġoperational izing Ġdenial Ġof Ġagency Ġbecause Ġof Ġthe Ġclose Ġrelationship Ġbetween Ġthese Ġconcepts . ĠAs Ġwith Ġthe ĠCon notation ĠFrames ' Ġagency Ġmeasurements Ġshown Ġin ĠFigure ĠFIG REF 45 , Ġour Ġanalysis Ġof Ġthe Ġdominance Ġof Ġeach Ġlabel 's Ġnearest Ġword 2 vec Ġneighbors Ġsuggest Ġthat ĠLGBTQ Ġgroups Ġexperience Ġgreater Ġdenial Ġof Ġagency Ġthan Ġthe ĠNew ĠYork ĠTimes 's Ġin - group ĠAmerican . ĠBoth Ġtechniques Ġshow Ġdifferences Ġbetween Ġthe Ġlabels Ġgay Ġand Ġhomosexual , Ġwhere Ġhomosexual Ġis Ġconsistently Ġassociated Ġwith Ġlower Ġagency Ġthan Ġgay Ġand Ġfurther Ġdecreases Ġover Ġtime . ĠHowever , Ġthese Ġtwo Ġmeasurements Ġsuggest Ġslightly Ġdifferent Ġtemporal Ġdynamics Ġfor Ġthe Ġdenial Ġof Ġagency Ġof ĠLGBTQ Ġpeople Ġin Ġthe Ġearliest Ġhalf Ġof Ġthe Ġdata ; Ġwe Ġobserve Ġa Ġdecrease Ġin Ġagency Ġfor ĠLGBTQ Ġgroups Ġwith ĠCon notation ĠFrames , Ġbut Ġa Ġslight Ġincrease Ġwith Ġnearest Ġneighbor Ġdominance . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠDomin ance Ġ: :: ĠQual itative ĠAnalysis </s> Using Ġthe ĠV AD ĠDomin ance ĠLex icon Ġto Ġcalculate Ġaverage Ġdominance Ġof Ġeach Ġsocial Ġgroup Ġlabel Ġappears Ġto Ġcorrespond Ġwell Ġto Ġour Ġnotion Ġof Ġdenial Ġof Ġagency . ĠBecause Ġgay 's Ġnearest Ġneighbors Ġhave Ġa Ġmuch Ġhigher Ġaverage Ġdominance Ġthan Ġhomosexual 's Ġfor Ġmost Ġyears , Ġwe Ġqual itatively Ġevaluate Ġthis Ġtechnique Ġby Ġcomparing Ġwords Ġthat Ġappear Ġas Ġa Ġnearby Ġneighbor Ġfor Ġgay Ġand Ġnot Ġhomosexual Ġfor Ġa Ġgiven Ġyear , Ġand Ġvice Ġversa . ĠWords Ġthat Ġappear Ġwithin Ġthe Ġ1000 Ġwords Ġnearest Ġto Ġgay Ġand Ġnot Ġhomosexual Ġin Ġthe Ġ2015 Ġword 2 vec Ġspace Ġinclude Ġhigh - agency Ġwords Ġsuch Ġas Ġbrave , Ġfreedom , Ġand Ġadvocate , Ġwhich Ġhave Ġdominance Ġscores Ġof Ġ0 . 9 17 , Ġ0 . 905 , Ġand Ġ0 . 8 18 Ġrespectively Ġaccording Ġto Ġthe ĠV AD Ġlex icon . ĠOn Ġthe Ġother Ġhand , Ġwords Ġnearby Ġto Ġhomosexual Ġand Ġnot Ġgay Ġin Ġ2015 Ġinclude Ġlow - agency Ġwords Ġsuch Ġas Ġsub missive Ġ( 0 . 173 ), Ġunprotected Ġ( 0 . 197 ), Ġand Ġignorant Ġ( 0 . 223 ). ĠWe Ġfind Ġsimilar Ġresults Ġcomparing Ġgay Ġand Ġhomosexual Ġin Ġthe Ġword 2 vec Ġmodel Ġtrained Ġon Ġ1986 Ġdata ; Ġhigh - agency Ġwords Ġsuch Ġas Ġliberation Ġ( 0 . 8 57 ) Ġand Ġmobilization Ġ( 0 . 787 ) Ġare Ġnearby Ġneighbors Ġfor Ġgay Ġand Ġnot Ġhomosexual , Ġwhile Ġlow - agency Ġwords Ġsuch Ġas Ġdepressive Ġ( 0 . 202 ) Ġand Ġinvoluntary Ġ( 0 . 231 ) Ġare Ġnearby Ġneighbors Ġfor Ġhomosexual Ġand Ġnot Ġgay . </s> We Ġadditionally Ġqual itatively Ġinvestigate Ġthe Ġvector Ġspace Ġmodels Ġcorresponding Ġto Ġseveral Ġout lier Ġyears . ĠIn Ġ1993 , Ġhomosexual 's Ġneighbors Ġhave Ġby Ġfar Ġthe Ġhighest Ġaverage Ġdominance Ġcompared Ġto Ġany Ġother Ġyear Ġfor Ġthis Ġlabel . ĠSimilar Ġto Ġwhat Ġwe Ġsaw Ġwith Ġneighbor Ġval ence , Ġthis Ġis Ġlikely Ġdue Ġto Ġa Ġlarge Ġvolume Ġof Ġdiscourse Ġabout Ġthe Ġmilitary Ġin Ġdebates Ġsurrounding Ġthe ĠâĢ ľ Don 't ĠAsk , ĠDon 't ĠTell " Ġlegislation . ĠSome Ġhigh - dom inance Ġwords Ġthat Ġare Ġunique Ġto Ġhomosexual 's Ġnearest Ġneighbors Ġin Ġ1993 Ġinclude Ġdirective Ġ( 0 . 9 18 ), Ġforce Ġ( 0 . 905 ), Ġenforce Ġ( 0 . 8 36 ) Ġand Ġtroops Ġ( 0 . 804 ). ĠAlso Ġsimilar Ġto Ġthe Ġneighbor Ġval ence Ġresults , Ġgay 's Ġneighbors ' Ġin Ġ1999 Ġhave Ġthe Ġlowest Ġaverage Ġdominance Ġthan Ġany Ġother Ġyear . ĠThis Ġcould Ġalso Ġlikely Ġbe Ġconnected Ġto ĠMatthew ĠShepard 's Ġdeath Ġand Ġthe Ġsubsequent Ġoutrage ; Ġunique Ġneighbors Ġfor Ġgay Ġin Ġ1999 Ġinclude Ġdisgrace ful Ġ( 0 . 240 ), Ġirresponsible Ġ( 0 . 241 ), Ġunethical Ġ( 0 . 269 ), Ġimprisoned Ġ( 0 . 302 ), Ġand Ġslander ous Ġ( 0 . 337 ). </s> Results Ġ: :: ĠMoral ĠDis g ust Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 55 Ġshows Ġthe Ġchanging Ġrelationships Ġbetween Ġall ĠLGBTQ Ġterms , Ġgay , Ġhomosexual Ġand Ġthe Ġdehuman izing Ġconcept Ġof ĠMoral ĠDis g ust . ĠBecause Ġthe Ġcos ine Ġdistance Ġbetween ĠAmerican Ġand ĠMoral ĠDis g ust Ġis Ġsignificantly Ġgreater Ġover Ġall Ġyears Ġthan Ġany ĠLGBTQ Ġrepresentation Ġ( Wil co xon Ġsigned - rank Ġtest ; Ġ$ p Ġ< Ġ0 . 0001 $ ), ĠAmerican Ġis Ġthe Ġgroup Ġlabel Ġleast Ġassociated Ġwith ĠMoral ĠDis g ust . ĠFurthermore , Ġthe Ġcos ine Ġdistance Ġbetween Ġgay Ġand ĠMoral ĠDis g ust Ġis Ġsignificantly Ġgreater Ġthan Ġthe Ġdistance Ġbetween Ġhomosexual Ġand ĠMoral ĠDis g ust Ġfor Ġevery Ġyear Ġ($ p Ġ< Ġ0 . 0001 $ ), Ġindicating Ġthat Ġhomosexual Ġis Ġmore Ġclosely Ġassociated Ġwith ĠMoral ĠDis g ust Ġthan Ġgay Ġis . ĠLinear Ġregression Ġanalyses Ġshow Ġthat ĠAmerican Ġsignificantly Ġdecreases Ġin Ġcos ine Ġdistance Ġover Ġthe Ġthirty Ġyears Ġ($ p Ġ< Ġ0 . 0001 $ ), Ġwhile Ġall ĠLGBTQ Ġterms Ġand Ġgay Ġsignificantly Ġincrease Ġin Ġcos ine Ġdistance Ġ($ p Ġ< Ġ0 . 0001 $ ), Ġindicated Ġweakening Ġassociations Ġbetween ĠLGBTQ Ġpeople Ġand Ġmoral Ġdisgust Ġover Ġtime . ĠUnlike Ġthe Ġaggreg ated Ġrepresentation Ġfor Ġall ĠLGBTQ Ġlabels Ġand Ġgay , Ġthe Ġincreasing Ġcos ine Ġdistance Ġbetween Ġhomosexual Ġand ĠMoral ĠDis g ust Ġdoes Ġnot Ġchange Ġsignificantly Ġover Ġtime Ġ($ p Ġ= Ġ0 . 54 $ ), Ġbut Ġdoes Ġsignificantly Ġdecrease Ġin Ġthe Ġlast Ġ15 Ġyears Ġ($ p Ġ< Ġ0 . 05 $ ). </s> Overall , Ġthese Ġmeasurements Ġof Ġassociations Ġbetween ĠLGBTQ Ġpeople Ġand ĠMoral ĠDis g ust Ġare Ġlargely Ġconsistent Ġwith Ġthe Ġother Ġoperational izations Ġof Ġdehuman ization . ĠAll ĠLGBTQ Ġgroup Ġlabels Ġare Ġmore Ġclosely Ġassociated Ġwith ĠMoral ĠDis g ust Ġthan Ġthe Ġnewspaper 's Ġin - group Ġterm ĠAmerican , Ġbut Ġthese Ġassociations Ġweaken Ġover Ġtime Ġwhich Ġsuggests Ġincreased Ġhuman ization . ĠNotably , Ġthe Ġterm Ġhomosexual Ġhas Ġalways Ġbeen Ġmore Ġassociated Ġwith ĠMoral ĠDis g ust Ġthan Ġthe Ġden otation ally - similar Ġterm Ġgay , Ġand Ġhomosexual Ġactually Ġbecomes Ġmore Ġclosely Ġassociated Ġwith Ġthis Ġdehuman izing Ġconcept Ġin Ġrecent Ġyears . </s> Results Ġ: :: ĠMoral ĠDis g ust Ġ: :: ĠQual itative ĠAnalysis </s> From Ġinvestigating Ġword Ġembed ding Ġneighbors Ġin ĠTable ĠT AB REF 23 , Ġwe Ġsaw Ġthat Ġhomosexual Ġhas Ġbecome Ġincreasingly Ġassociated Ġwith Ġimmoral Ġconcepts , Ġsuggesting Ġthat Ġmoral Ġdisgust Ġis Ġa Ġmechanism Ġby Ġwhich ĠLGBTQ Ġpeople Ġare Ġdehuman ized . ĠAlthough Ġrarely Ġimplicitly Ġinvoked , Ġthe Ġconnection Ġbetween ĠLGBTQ Ġpeople Ġand Ġdisgust Ġis Ġsupported Ġby Ġseveral Ġexamples Ġfrom Ġthe Ġdata , Ġas Ġshown Ġin ĠTable ĠT AB REF 57 . ĠThe Ġmost Ġdramatic Ġchange Ġover Ġtime Ġoccurs Ġin Ġthe Ġlate Ġ1980 s Ġand Ġearly Ġ1990 s , Ġwhen Ġgay , Ġhomosexual , Ġand Ġall ĠLGBTQ Ġterms Ġbecome Ġrapidly Ġless Ġassociated Ġwith ĠMoral ĠDis g ust Ġbased Ġon Ġtheir Ġincreasing Ġcos ine Ġdistance . ĠBecause Ġa Ġlot Ġof Ġdisease - related Ġwords Ġare Ġincluded Ġin Ġthe ĠMoral ĠDis g ust Ġcomponent Ġof Ġthe ĠMoral ĠFound ations ĠDictionary ĠB IB REF 64 , Ġthis Ġis Ġlikely Ġdue Ġto Ġdecreasing Ġattention Ġto Ġthe ĠAIDS Ġepidemic . </s> Results Ġ: :: ĠVer min Ġas Ġa ĠDe human izing ĠMet aph or Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 60 Ġshows Ġthe Ġrelationships Ġbetween ĠLGBTQ Ġlabels Ġ( and ĠAmerican ) Ġand Ġthe Ġdehuman izing Ġver min Ġmetaphor , Ġquant ified Ġas Ġthe Ġcos ine Ġdistance Ġbetween Ġthe Ġlabels ' Ġword 2 vec Ġvectors Ġand Ġa ĠVer min Ġconcept Ġvector Ġrepresentation , Ġcalculated Ġas Ġthe Ġcent roid Ġof Ġmultiple Ġver min - related Ġwords . ĠAs Ġwith ĠMoral ĠDis g ust Ġin ĠFigure ĠFIG REF 55 , Ġthe Ġin - group Ġterm ĠAmerican Ġis Ġsignificantly Ġfurther Ġaway Ġfrom ĠVer min Ġover Ġall Ġyears Ġthan Ġany Ġof Ġthe ĠLGBTQ Ġrepresentations Ġ( Wil co xon Ġsigned - rank Ġtest ; Ġ$ p Ġ< Ġ0 . 0001 $ ). ĠThe Ġcos ine Ġdistance Ġbetween Ġgay Ġand ĠVer min Ġis Ġalso Ġsignificantly Ġgreater Ġthan Ġthe Ġdistance Ġbetween Ġhomosexual Ġand ĠVer min Ġ($ p Ġ< Ġ0 . 0001 $ ), Ġindicating Ġthat Ġhomosexual Ġis Ġmore Ġclosely Ġassociated Ġwith Ġthe Ġdehuman izing Ġver min Ġmetaphor Ġthan Ġgay Ġis . ĠFurthermore , Ġwhile Ġall ĠLGBTQ Ġterms Ġand Ġgay Ġsignificantly Ġincrease Ġin Ġcos ine Ġdistance Ġ( bec ome Ġless Ġassociated Ġwith ĠVer min ) Ġover Ġtime Ġ( linear Ġregression ; Ġ$ p Ġ< Ġ0 . 0001 $ ), Ġhomosexual Ġslightly Ġdecreases Ġin Ġcos ine Ġdistance Ġ( great er Ġassociation Ġwith ĠVer min ), Ġbut Ġthis Ġdecrease Ġdoes Ġnot Ġreach Ġsignificance Ġ($ p Ġ= Ġ0 . 13 $ ). </s> Our Ġmeasurement Ġof Ġthe Ġimplicit Ġver min Ġmetaphor Ġshows Ġa Ġsimilar Ġpattern Ġas Ġour Ġmeasurements Ġfor Ġmoral Ġdisgust , Ġdenial Ġof Ġagency , Ġand Ġnegative Ġevaluations Ġof Ġthe Ġtarget Ġgroup . ĠOverall , ĠLGBTQ Ġgroups Ġare Ġmore Ġimplicitly Ġassociated Ġwith Ġver min Ġthan Ġthe Ġcomparison Ġsocial Ġgroup ĠAmerican , Ġbut Ġthis Ġassociation Ġis Ġweakening Ġover Ġtime , Ġsuggesting Ġincreased Ġhuman ization . ĠUnlike Ġthe Ġlabels Ġgay Ġand Ġthe Ġaggreg ated Ġrepresentation Ġof Ġall ĠLGBTQ Ġterms , Ġthe Ġassociation Ġbetween Ġver min Ġand Ġthe Ġlabel Ġhomosexual Ġdoes Ġnot Ġsignificantly Ġchange Ġover Ġtime . </s> Results Ġ: :: ĠVer min Ġas Ġa ĠDe human izing ĠMet aph or Ġ: :: ĠQual itative ĠAnalysis </s> Met aph ors Ġcomparing Ġhumans Ġto Ġver min Ġhave Ġbeen Ġespecially Ġprominent Ġin Ġdehuman izing Ġgroups Ġthroughout Ġhistory ĠB IB REF 4 , ĠB IB REF 15 . ĠAlthough Ġno ĠNew ĠYork ĠTimes Ġwriters Ġdirectly Ġcompare ĠLGBTQ Ġpeople Ġto Ġver min , Ġthis Ġmetaphor Ġmay Ġbe Ġinvoked Ġin Ġmore Ġsubtle Ġways . ĠFurthermore , Ġseveral Ġparagraphs Ġin Ġthe Ġdata Ġdemonstrate Ġthat Ġpeople Ġexplicitly Ġmention Ġthe Ġmetaphor Ġin Ġorder Ġto Ġargue Ġagainst Ġit . ĠThese Ġexamples , Ġshown Ġin ĠTable ĠT AB REF 62 , Ġfurther Ġpoint Ġto Ġthe Ġexistence Ġof ĠLGBTQ Ġpeople - as - ver min Ġmetaphors , Ġeven Ġif Ġthey Ġare Ġnot Ġfrequently Ġdirectly Ġinvoked . </s> Discussion </s> Our Ġframework Ġfor Ġthe Ġcomputational Ġlinguistic Ġanalysis Ġof Ġdehuman ization Ġinvolves Ġidentifying Ġmajor Ġdimensions Ġof Ġdehuman ization Ġfrom Ġsocial Ġpsychology Ġliterature , Ġproposing Ġlinguistic Ġcorrelates Ġfor Ġeach Ġdimension , Ġand Ġdeveloping Ġrobust Ġand Ġinterpret able Ġcomputational Ġmethods Ġto Ġquantify Ġthese Ġcorrelates . ĠWe Ġapplied Ġthis Ġframework Ġto Ġstudy Ġthe Ġdehuman ization Ġof ĠLGBTQ Ġpeople Ġin Ġthe ĠNew ĠYork ĠTimes Ġover Ġthe Ġcourse Ġof Ġ30 Ġyears , Ġfrom Ġ1986 Ġto Ġ2015 . ĠWe Ġmeasured Ġfour Ġdimensions Ġof Ġdehuman ization : Ġnegative Ġevaluations Ġof Ġthe Ġtarget Ġgroup , Ġdenial Ġof Ġagency , Ġmoral Ġdisgust , Ġand Ġ( impl icit ) Ġinv ocations Ġof Ġver min Ġmetaphors . ĠOverall , Ġwe Ġfound Ġincreasingly Ġhuman izing Ġdescriptions Ġof ĠLGBTQ Ġpeople Ġover Ġtime . ĠLGBTQ Ġpeople Ġhave Ġbecome Ġmore Ġassociated Ġwith Ġpositive Ġemotional Ġlanguage , Ġsuggesting Ġthat Ġnegative Ġevaluations Ġof Ġthe Ġtarget Ġgroup Ġhave Ġdiminished . ĠLGBTQ Ġpeople Ġhave Ġbecome Ġmore Ġassociated Ġwith Ġhigher - dom inance Ġwords , Ġsuggesting Ġdecreasing Ġdenial Ġof Ġagency , Ġalthough Ġthis Ġfinding Ġwas Ġnot Ġreplicated Ġwith Ġthe Ġverb - centric ĠâĢ ľ Con notation ĠFrames " Ġmeasurement . ĠFurthermore , Ġlabels Ġfor ĠLGBTQ Ġpeople Ġhave Ġmoved Ġfurther Ġaway Ġfrom Ġthe Ġconcepts Ġof Ġmoral Ġdisgust Ġand Ġver min Ġwithin Ġdistribution al Ġsemantic Ġrepresentations , Ġsuggesting Ġthat Ġharmful Ġassociations Ġbetween ĠLGBTQ Ġpeople Ġand Ġthese Ġdehuman izing Ġconcepts Ġhave Ġweakened Ġover Ġtime . </s> Despite Ġthese Ġbroad Ġtrends , Ġwe Ġfound Ġthat Ġthe Ġlabels Ġgay Ġand Ġhomosexual Ġexhibit Ġstrikingly Ġdifferent Ġpatterns Ġamong Ġthese Ġfour Ġcomponents Ġof Ġdehuman ization . ĠHom osexual Ġis Ġassociated Ġwith Ġmore Ġnegative Ġ( low Ġval ence ) Ġlanguage Ġthan Ġgay , Ġsuggesting Ġthat Ġthere Ġare Ġmore Ġnegative Ġevaluations Ġof Ġpeople Ġdescribed Ġas Ġhomosexual Ġthan Ġgay . ĠThe Ġlabel Ġhomosexual Ġis Ġalso Ġassociated Ġwith Ġgreater Ġdenial Ġof Ġagency , Ġand Ġhas Ġstronger Ġconnections Ġto Ġmoral Ġdisgust Ġand Ġver min Ġthan Ġgay Ġdoes . ĠFurthermore , Ġunlike Ġfor ĠLGBTQ Ġlabels Ġas Ġa Ġwhole , Ġdiscussions Ġof Ġhomosexual Ġpeople Ġdo Ġnot Ġseem Ġto Ġhave Ġbecome Ġmore Ġhuman izing Ġover Ġtime . ĠOn Ġthe Ġcontrary , Ġseveral Ġtechniques Ġsuggested Ġthat Ġlanguage Ġused Ġalongside Ġthe Ġlabel Ġhomosexual Ġhas Ġactually Ġbecome Ġmore Ġdehuman izing Ġafter Ġabout Ġ2000 . ĠThrough Ġits Ġrepeated Ġuse Ġin Ġthese Ġcontexts , Ġthe Ġuse Ġof Ġthe Ġword Ġhomosexual Ġappears Ġto Ġhave Ġemerged Ġas Ġan Ġindex Ġof Ġmore Ġdehuman izing Ġattitudes Ġtowards ĠLGBTQ Ġpeople Ġthan Ġother Ġlabels . ĠDespite Ġthe Ġden ot ational Ġsimilarity Ġbetween Ġhomosexual Ġand Ġgay , Ġthese Ġcomputational Ġtechniques Ġcan Ġtrack Ġthe Ġstark Ġdifferences Ġand Ġdiver g ences Ġin Ġsocial Ġmeanings . </s> In Ġthis Ġwork , Ġwe Ġrestricted Ġour Ġanalysis Ġto Ġthe Ġlex ical Ġlevel Ġfor Ġease Ġof Ġinterpret ability , Ġand Ġlever aged Ġa Ġdiverse Ġarray Ġof Ġexisting Ġresources , Ġincluding Ġthe ĠN RC ĠV AD Ġlex icon ĠB IB REF 56 , ĠCon notation ĠFrames Ġlex icons ĠB IB REF 22 , ĠB IB REF 25 , Ġand Ġthe ĠMoral ĠFound ations ĠDictionary ĠB IB REF 64 . ĠFor Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup Ġand Ġdenial Ġof Ġagency , Ġwe Ġpropose Ġmultiple Ġdifferent Ġtechniques Ġthat Ġvary Ġin Ġaccuracy Ġand Ġinterpret ability . ĠWhile Ġword - count ing Ġmethods Ġare Ġoften Ġinaccurate Ġdue Ġto Ġtheir Ġsimplicity , Ġtheir Ġresults Ġcan Ġbe Ġeasily Ġunderstood , Ġwhile Ġembed ding - based Ġmethods Ġtend Ġto Ġsuffer Ġthe Ġopposite Ġproblem . ĠWe Ġdo Ġnot Ġclaim Ġto Ġhave Ġfound Ġthe Ġoptimal Ġbalance Ġof Ġmodel Ġquality Ġand Ġinterpret ability Ġin Ġthis Ġstudy , Ġbut Ġcarefully Ġconsidering Ġthis Ġtrade off Ġis Ġespecially Ġimportant Ġin Ġwork Ġthat Ġseeks Ġto Ġcharacterize Ġcomplex Ġand Ġsensitive Ġsocial Ġphenomena Ġsuch Ġas Ġdehuman ization . </s> Discussion Ġ: :: ĠLim itations Ġand ĠFuture ĠWork </s> As Ġthe Ġfirst Ġattempt Ġto Ġcomput ationally Ġanalyze Ġdehuman ization , Ġthis Ġwork Ġhas Ġmany Ġlimitations . ĠWhile Ġour Ġqualitative Ġanalysis Ġdemonstrates Ġthat Ġthe Ġproposed Ġtechniques Ġcapture Ġsome Ġlingu istics Ġsignals Ġof Ġdehuman ization , Ġother Ġaspects Ġof Ġthe Ġdata Ġerrone ously Ġimpact Ġour Ġmeasurements Ġas Ġwell . ĠFor Ġexample , Ġwe Ġstill Ġcannot Ġentirely Ġdis ent angle Ġa Ġjournalist 's Ġperspective Ġfrom Ġthe Ġtopics Ġand Ġevents Ġin Ġthe Ġstories Ġthey Ġdiscuss . ĠThese Ġtechniques Ġwould Ġpredict Ġthat Ġarticles Ġemphasizing Ġanti - LGBT Q Ġviolence Ġare Ġdehuman izing ; Ġin Ġreality , Ġthese Ġstories Ġmay Ġexpress Ġdehuman izing Ġattitudes Ġbut Ġthose Ġviews Ġare Ġnot Ġnecessarily Ġheld Ġby Ġthe Ġjournalist . ĠAn Ġexciting Ġarea Ġof Ġfuture Ġwork Ġcould Ġinvolve Ġdeveloping Ġmore Ġsophisticated Ġcomputational Ġmethods Ġthat Ġcan Ġbetter Ġdis amb ig uate Ġthe Ġwriter 's Ġattitudes , Ġattitudes Ġof Ġpeople Ġmentioned Ġin Ġthe Ġtext , Ġand Ġtopics Ġand Ġevents , Ġwhile Ġrecognizing Ġthat Ġeach Ġof Ġthese Ġcould Ġcontribute Ġto Ġthe Ġoverall Ġrepresentation Ġof Ġmarginalized Ġgroups Ġin Ġthe Ġmedia . ĠFurthermore , Ġthe Ġpresent Ġwork Ġuses Ġword 2 vec Ġsince Ġall Ġknown Ġaffect ive Ġlex icons Ġare Ġtype - level , Ġbut Ġcontextual ized Ġembed ding - based Ġmethods Ġhave Ġgreat Ġpotential Ġfor Ġmore Ġnuanced Ġanalyses Ġof Ġdehuman izing Ġlanguage Ġby Ġleveraging Ġtoken - level Ġrepresentations ĠB IB REF 72 , ĠB IB REF 73 . </s> Another Ġlimitation Ġof Ġthis Ġstudy Ġis Ġthe Ġlack Ġof Ġground Ġtruth Ġlabels Ġfor Ġwhat Ġcontent Ġis Ġdehuman izing , Ġwhich Ġhas Ġprevented Ġus Ġfrom Ġdeveloping Ġsupervised Ġmethods Ġand Ġquantitative Ġevaluations . ĠWhile Ġit Ġis Ġchallenging Ġto Ġdirectly Ġsolicit Ġopinions Ġfrom Ġhuman Ġannot ators Ġabout Ġsuch Ġa Ġcomplex Ġand Ġsubjective Ġconcept Ġsuch Ġas Ġdehuman ization , Ġannotation Ġstrategies Ġsuch Ġas Ġbest - worst Ġscaling Ġcan Ġbe Ġpromising Ġapproaches ĠB IB REF 74 , Ġparticularly Ġif Ġthey Ġprimarily Ġconsider Ġopinions Ġfrom Ġmembers Ġof Ġthe Ġsocial Ġgroups Ġof Ġinterest . ĠThese Ġground - truth Ġlabels Ġcould Ġenable Ġfuture Ġwork Ġto Ġdevelop Ġmodels Ġto Ġun ify Ġall Ġof Ġthese Ġdistinct Ġcomponents Ġof Ġdehuman ization Ġinto Ġa Ġsingle Ġmeasurement Ġfor Ġa Ġgiven Ġtext . </s> Our Ġframework Ġcould Ġalso Ġbe Ġgreatly Ġexpanded Ġto Ġinclude Ġmore Ġinsights Ġfrom Ġdehuman ization Ġtheory . ĠBeyond Ġthe Ġfour Ġelements Ġof Ġdehuman ization Ġthat Ġwe Ġdiscuss Ġin Ġthis Ġarticle , Ġsocial Ġpsychology Ġresearch Ġhas Ġidentified Ġmany Ġother Ġcognitive Ġprocesses Ġthat Ġcontribute Ġto Ġdehuman ization , Ġsuch Ġas Ġpsychological Ġdist ancing , Ġessential ism Ġ( the Ġperception Ġthat Ġthe Ġtarget Ġgroup Ġhas Ġsome Ġessence Ġthat Ġmakes Ġthem Ġcategor ically Ġand Ġfundamentally Ġdifferent ), Ġand Ġdenial Ġof Ġsubject ivity Ġ( neg lect Ġof Ġa Ġtarget Ġgroup Ġmember 's Ġpersonal Ġfeelings Ġand Ġexperiences ). ĠB IB REF 75 , ĠB IB REF 21 , ĠB IB REF 76 , ĠB IB REF 3 . ĠScholars Ġalso Ġoften Ġdifferentiate Ġbetween Ġtwo Ġforms Ġof Ġdehuman ization , Ġanimal istic Ġ( lik ening Ġhumans Ġto Ġanimals ) Ġand Ġmechan istic Ġ( lik ening Ġhumans Ġto Ġin animate Ġobjects Ġor Ġmachines ), Ġand Ġthe Ġlinguistic Ġsignals Ġunderpin ning Ġeach Ġof Ġthese Ġforms Ġcould Ġsubstantially Ġdiffer Ġfrom Ġone Ġanother ĠB IB REF 4 . </s> For Ġsimplicity Ġand Ġease Ġof Ġinterpretation , Ġwe Ġfocus Ġon Ġquant ifying Ġlex ical Ġcues Ġof Ġdehuman ization . ĠHowever , Ġour Ġunderstanding Ġof Ġthe Ġlanguage Ġof Ġdehuman ization Ġwould Ġbe Ġenriched Ġby Ġconsidering Ġlinguistic Ġfeatures Ġbeyond Ġthe Ġlex icon . ĠFor Ġexample , ĠB IB REF 77 Ġhas Ġshown Ġthat Ġdefinite Ġplur als Ġin ĠEnglish Ġ( e . g . Ġthe Ġgays ) Ġhave Ġa Ġunique Ġsocial Ġand Ġpragmatic Ġeffect Ġcompared Ġto Ġbare Ġplur als Ġ( e . g . Ġgays ) Ġby Ġpackaging Ġindividual Ġentities Ġinto Ġone Ġmon olith Ġand Ġsetting Ġthis Ġgroup Ġapart Ġfrom Ġthe Ġspeaker . ĠIndex ing Ġa Ġspeaker 's Ġnon - members hip Ġin Ġthe Ġgroup Ġbeing Ġdiscussed Ġcreates Ġsocial Ġdistance Ġbetween Ġthe Ġspeaker Ġand Ġgroup ĠB IB REF 77 , Ġwhich Ġmakes Ġit Ġlikely Ġthat Ġusing Ġdefinite Ġplur als Ġto Ġlabel Ġmarginalized Ġsocial Ġgroups Ġplays Ġan Ġimportant Ġrole Ġin Ġdehuman ization . ĠSimilarly , Ġexamining Ġnon - lex ical Ġsignals Ġcould Ġhelp Ġus Ġcapture Ġelements Ġof Ġdehuman ization Ġthat Ġwe Ġcould Ġnot Ġeasily Ġidentify Ġonly Ġthrough Ġlex ical Ġresources . ĠFor Ġexample , Ġthe Ġpart Ġof Ġspeech Ġof Ġa Ġgroup Ġlabel Ġ( such Ġas Ġgay Ġas Ġa Ġnoun Ġor Ġadjective ) Ġcould Ġhave Ġimplications Ġfor Ġessential ism , Ġas Ġadject ives Ġsimply Ġname Ġattributes Ġto Ġsome Ġentity , Ġwhile Ġnoun s Ġexplicitly Ġstate Ġthe Ġentity 's Ġcategory Ġmembership Ġand Ġencaps ulates Ġother Ġstereotypes Ġassociated Ġwith Ġthat Ġcategory ĠB IB REF 78 , ĠB IB REF 79 , ĠB IB REF 76 , ĠB IB REF 80 . ĠWe Ġfurthermore Ġbelieve Ġthat Ġincorporating Ġdiscourse - level Ġanalysis , Ġsuch Ġas Ġexamining Ġthe Ġrole Ġof Ġdirect Ġquotes Ġin Ġan Ġarticle Ġand Ġwho Ġis Ġbeing Ġquoted , Ġcould Ġprovide Ġinformative Ġinsights Ġthat Ġcould Ġaddress Ġsome Ġlimitations Ġdiscussed Ġearlier . </s> We Ġsupported Ġour Ġproposed Ġframework Ġwith Ġa Ġcase Ġstudy Ġof ĠLGBTQ Ġrepresentation Ġin Ġthe ĠNew ĠYork ĠTimes . ĠThis Ġcase Ġstudy Ġhad Ġa Ġnumber Ġof Ġlimitations Ġas Ġan Ġanalysis Ġof Ġthe Ġdehuman ization Ġof ĠLGBTQ Ġpeople Ġin Ġthe Ġmedia . ĠWe Ġonly Ġlooked Ġat Ġone Ġdata Ġsource , Ġwhich Ġmay Ġhave Ġits Ġown Ġbiases Ġand Ġdoes Ġnot Ġcapture Ġthe Ġentirety Ġof Ġmedia Ġdiscourse Ġabout ĠLGBTQ Ġpeople . ĠFurthermore , Ġwe Ġonly Ġstudied Ġnewspaper Ġarticles Ġwritten Ġin Ġ( Standard ) ĠAmerican ĠEnglish . ĠFuture Ġwork Ġcould Ġassess Ġhow Ġwell Ġthese Ġlinguistic Ġcues Ġwe Ġmeasure Ġgeneral ize Ġto Ġother Ġlanguages Ġand Ġfocus Ġon Ġcross - ling u istic Ġcomparisons Ġof Ġdehuman izing Ġlanguage . ĠFinally , Ġmost Ġof Ġour Ġcase Ġstudy Ġinvolved Ġanalysis Ġof Ġan Ġaggreg ated ĠLGBTQ Ġrepresentation , with Ġa Ġspecific Ġfocus Ġon Ġthe Ġlabels Ġgay Ġand Ġhomosexual , Ġprimarily Ġdue Ġto Ġthe Ġamount Ġof Ġdata Ġavailable . ĠHowever , Ġas Ġa Ġresult , Ġwe Ġstill Ġhave Ġlittle Ġunderstanding Ġabout Ġthe Ġnuanced Ġdifferences Ġand Ġchanges Ġin Ġthe Ġrepresentation Ġof ĠLGBTQ Ġpeople Ġwho Ġdon 't Ġidentify Ġwith Ġ( or Ġaren 't Ġidentified Ġas ) Ġthese Ġlabels . </s> Discussion Ġ: :: ĠEth ical ĠImpl ications </s> Our Ġhope Ġin Ġpursuing Ġthis Ġwork Ġis Ġto Ġdraw Ġattention Ġto Ġissues Ġof Ġmedia Ġrepresentation Ġof Ġmarginalized Ġgroups Ġand Ġto Ġeventually Ġhelp Ġmake Ġthe Ġonline Ġworld Ġa Ġsafer Ġand Ġkind er Ġplace . ĠAn Ġimportant Ġpart Ġof Ġthis Ġmission Ġis Ġto Ġacknowledge Ġthe Ġethical Ġimplications Ġand Ġpotential Ġissues Ġof Ġour Ġown Ġwork . </s> The Ġmethods Ġthat Ġwe Ġuse Ġto Ġquantify Ġdehuman ization Ġare Ġthemselves Ġbiased Ġand Ġpotentially Ġharmful . ĠFor Ġexample , Ġwe Ġshowed Ġin ĠSection ĠSEC REF 25 Ġthat Ġthe Ġlex icon Ġthat Ġused Ġto Ġmeasure Ġval ence Ġcontains Ġits Ġown Ġanti - LGBT Q Ġbiases Ġby Ġconsidering ĠLGBTQ Ġgroup Ġlabels Ġto Ġbe Ġprimarily Ġnegative / un pleasant . ĠWe Ġalso Ġtrained Ġword 2 vec Ġmodels Ġon ĠNew ĠYork ĠTimes Ġdata , Ġwhich Ġpresents Ġbiases . ĠWhile Ġit Ġis Ġtypically Ġconcerning Ġwhen Ġsuch Ġmodels Ġare Ġtrained Ġon Ġbiased Ġdata Ġdue Ġto Ġharmful Ġdownstream Ġeffects ĠB IB REF 37 , Ġwe Ġleverage Ġthis Ġdata Ġas Ġa Ġuseful Ġresource Ġfor Ġuncover ing Ġhuman Ġbiases Ġand Ġunderstanding Ġhow Ġbiases Ġemerge Ġin Ġmedia Ġdiscourse Ġabout ĠLGBTQ Ġpeople . </s> Another Ġconcern Ġof Ġthis Ġwork Ġis Ġthat Ġwe Ġused Ġcomputational Ġmethods Ġto Ġrepresent Ġhuman Ġbeings Ġin Ġorder Ġto Ġanalyze Ġdehuman ization Ġon Ġa Ġlarge Ġscale . ĠHowever , Ġrepresenting Ġpeople Ġas Ġsequences Ġof Ġnumbers Ġ( especially Ġin Ġour Ġvector - based Ġexperiments ) Ġis Ġinherently Ġdehuman izing Ġthem . ĠWhile Ġwe Ġhope Ġthat Ġthis Ġwork Ġwill Ġhuman ize Ġand Ġempower Ġmarginalized Ġgroups , Ġwe Ġacknowledge Ġthat Ġit Ġalso Ġhas Ġthis Ġopposite Ġeffect . </s> Other Ġethical Ġimplications Ġof Ġthis Ġproject Ġappear Ġwithin Ġour Ġcase Ġstudy . ĠDue Ġto Ġthe Ġtime Ġscale Ġof Ġour Ġstudy Ġ( begin ning Ġin Ġ1986 ), Ġwe Ġdid Ġnot Ġinclude ĠLGBTQ Ġlabels Ġsuch Ġas Ġqueer Ġor Ġtrans , Ġwhich Ġoften Ġhad Ġdifferent Ġmeanings Ġand Ġwere Ġoften Ġfound Ġin Ġcontexts Ġunrelated Ġto ĠLGBTQ Ġidentities Ġin Ġearlier Ġyears . ĠFurthermore , Ġmuch Ġof Ġour Ġanalysis Ġfocused Ġon Ġan Ġaggreg ated Ġrepresentation Ġfor ĠLGBTQ Ġpeople , Ġwhich Ġunintentionally Ġminimized Ġthe Ġvast Ġdiversity Ġof Ġsocial Ġidentities Ġencomp assed Ġwithin Ġthis Ġumbrella . ĠWe Ġhighlighted Ġstriking Ġtemporal Ġchanges Ġand Ġdifferences Ġbetween Ġthe Ġlabels Ġgay Ġand Ġhomosexual , Ġwhich Ġwere Ġprimarily Ġchosen Ġbecause Ġthese Ġlabels Ġwere Ġwell - represented Ġthroughout Ġall Ġyears Ġof Ġdata . ĠHowever , Ġemphasizing Ġthese Ġlabels Ġat Ġthe Ġexpense Ġof Ġothers Ġmay Ġcontribute Ġto Ġthe Ġcontinued Ġer asure Ġof Ġpeople Ġwho Ġare Ġmarginalized Ġeven Ġwithin ĠLGBTQ Ġcommunities . </s> Conclusion </s> This Ġwork Ġis Ġthe Ġfirst Ġknown Ġcomputational Ġlinguistic Ġstudy Ġof Ġdehuman ization , Ġand Ġprovides Ġcontributions Ġto Ġmultiple Ġfields . ĠThe Ġproposed Ġframework Ġand Ġtechniques Ġto Ġquantify Ġsalient Ġcomponents Ġof Ġdehuman ization Ġcan Ġshed Ġlight Ġon Ġlinguistic Ġvariation Ġand Ġchange Ġin Ġdisc ourses Ġsurrounding Ġmarginalized Ġgroups . ĠFurthermore , Ġthese Ġtools Ġfor Ġlarge - scale Ġanalysis Ġhave Ġpotential Ġto Ġcomplement Ġsmaller - scale Ġpsychological Ġstudies Ġof Ġdehuman ization . ĠFinally , Ġthis Ġwork Ġhas Ġimplications Ġfor Ġautomatically Ġdetecting Ġand Ġunderstanding Ġmedia Ġbias Ġand Ġabusive Ġlanguage Ġonline . </s> Acknowled gements </s> We Ġwould Ġlike Ġto Ġthank ĠRob ĠVo ig t , ĠChris ĠPot ts , ĠPenny ĠEck ert , ĠDavid ĠJ urg ens , ĠC eren ĠBud ak , ĠAnna - Marie ĠSp re ng er , Ġand ĠIs ma el ĠMend o za Ġfor Ġtheir Ġinsightful Ġcontributions Ġand Ġfeedback . ĠWe Ġwould Ġalso Ġlike Ġto Ġthank Ġour Ġaudience Ġat ĠNW AV - 46 , Ġwho Ġprovided Ġhelpful Ġfeedback Ġon Ġan Ġearlier Ġversion Ġof Ġthis Ġproject . </s> App endix Ġ: :: ĠVal ence Ġprediction Ġresults </s> In Ġaddition Ġto Ġquant ifying Ġthe Ġnegative Ġevaluation Ġof Ġa Ġtarget Ġgroup Ġby Ġcalculating Ġthe Ġaverage Ġval ence Ġscore Ġof Ġa Ġgroup Ġlabel 's Ġvector Ġrepresentation 's Ġnearest Ġneighbors Ġaccording Ġto Ġthe ĠN RC ĠV AD Ġlex icon , Ġwe Ġalso Ġdirectly Ġinduced Ġa
Keywords: computational sociolinguistics, dehumanization, lexical variation, language change, media, New York Times, LGBTQ Warning: this paper contains material that some may find offensive or upsetting. Introduction Despite the American public's increasing acceptance of LGBTQ people and recent legal successes, LGBTQ individuals frequently remain the targets of hate and violence BIBREF0, BIBREF1, BIBREF2. At the core of this issue is dehumanization, “the act of perceiving or treating people as less than human” BIBREF3, a process that heavily contributes to extreme intergroup bias BIBREF4. Language is central to studying this phenomenon; like other forms of bias BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, dehumanizing attitudes are expressed through subtle linguistic manipulations, even in carefully-edited texts. It is crucial to understand the use of such linguistic signals in mainstream media, as the media's representation of marginalized social groups has far-reaching implications for social acceptance, policy, and safety. While small-scale studies of dehumanization and media representation of marginalized communities provide valuable insights (e.g. BIBREF10), there exist no known large-scale analyses, likely due to difficulties in quantifying such a subjective and multidimensional psychological process. However, the ability to do large-scale analysis is crucial for understanding how dehumanizing attitudes have evolved over long periods of time. Furthermore, by being able to account for a greater amount of media discourse at once, large-scale techniques can provide a more complete view of the media environment to which the public is exposed. Linguistics and computer science offer valuable methods and insights on which such large-scale techniques might be developed for the study of dehumanization. By leveraging more information about the contexts in which marginalized groups are discussed, computational linguistic methods not only enable large-scale study of a complex psychological phenomenon, but can even reveal linguistic variations and changes that are not easily identifiable through qualitative analysis alone. In this work, we develop a computational linguistic framework for analyzing dehumanizing language, with a focus on lexical signals of dehumanization. Social psychologists have identified numerous components of dehumanization, such as negative evaluations of a target group, denial of agency, moral disgust, and likening members of a target group to non-human entities such as vermin. Drawing upon this rich body of literature, we first identify clear linguistic analogs for these components and propose several computational techniques to measure these linguistic correlates. We then apply this general framework to explore changing representations of LGBTQ groups in the New York Times over the span of three decades. We additionally use this lens of dehumanization to investigate differences in social meaning between the denotationally-similar labels gay and homosexual. This paper aims to bridge the gaps between computational modeling, sociolinguistics, and dehumanization research with implications for several disciplines. In addition to enabling large-scale studies of dehumanizing language and media representation of marginalized social groups, these techniques can be built into systems that seek to capture both conscious and unconscious biases in text. Furthermore, this work has implications for improving machines' abilities to automatically detect hate speech and abusive language online, which are typically underpinned by dehumanizing language. Finally, our case study demonstrates that such computational analyses of discussions about marginalized groups can provide unique insights into language variation and change within sensitive sociopolitical contexts, and help us understand how people (and institutions) use language to express their ideologies and attitudes towards certain social groups. Background ::: Dehumanization Our lexical semantic analysis involves quantifying linguistic correlates of component psychological processes that contribute to dehumanization. Our approaches are informed by social psychology research on dehumanization, which is briefly summarized here. Prior work has identified numerous related processes that comprise dehumanization BIBREF4. One such component is likening members of the target group to non-human entities, such as machines or animals BIBREF4, BIBREF11, BIBREF12. By perceiving members of a target group to be non-human, they are “outside the boundary in which moral values, rules, and considerations of fairness apply" BIBREF13, which thus leads to violence and other forms of abuse. Metaphors and imagery relating target groups to vermin are particularly insidious and played a prominent role in the genocide of Jews in Nazi Germany and Tutsis in Rwanda BIBREF14. More recently, the vermin metaphor has been invoked by the media to discuss terrorists and political leaders of majority-Muslim countries after September 11 BIBREF15. According to BIBREF16, the vermin metaphor is particularly powerful because it conceptualizes the target group as “engaged in threatening behavior, but devoid of thought or emotional desire". Disgust underlies the dehumanizing nature of these metaphors and is itself another important element of dehumanization. Disgust contributes to members of target groups being perceived as less-than-human and of negative social value BIBREF17. It is often evoked (both in real life and experimental settings) through likening a target group to animals. BIBREF18 find that priming participants to feel disgust facilitates “moral exclusion of out-groups". Experiments by BIBREF17 and BIBREF19 similarly find that disgust is a predictor of dehumanizing perceptions of a target group. Both moral disgust towards a particular social group and the invocation of non-human metaphors are facilitated by essentialist beliefs about groups, which BIBREF4 presents as a necessary component of dehumanization. In order to distinguish between human and non-human, dehumanization requires an exaggerated perception of intergroup differences. Essentialist thinking thus contributes to dehumanization by leading to the perception of social groups as categorically distinct, which in turn emphasizes intergroup differences BIBREF4 According to BIBREF4, much of the prior research describes “extremely negative evaluations of others" as a major component of dehumanization. This is especially pronounced in BIBREF20's account of delegitimization, which involves using negative characteristics to categorize groups that are “excluded from the realm of acceptable norms and values" BIBREF20. While BIBREF20 defines delegitimization as a distinct process, he considers dehumanization to be one means of delegitimization. BIBREF13 also discusses broader processes of moral exclusion, one of which is dehumanization. A closely related process is psychological distancing, in which one perceives others to be objects or nonexistent BIBREF13. BIBREF21 identifies elements that contribute to the objectification (and thus dehumanization) of women, one of which is denial of subjectivity, or the habitual neglect of one's experiences, emotions, and feelings. Another component of dehumanization is the denial of agency to members of the target group BIBREF4. According to BIBREF16, there are three types of agency: "the ability to (1) experience emotion and feel pain (affective mental states), (2) act and produce an effect on their environment (behavioral potential), and (3) think and hold beliefs (cognitive mental states) BIBREF16. Dehumanization typically involves the denial of one or more of these types of agency BIBREF16. In Section SECREF4, we introduce computational linguistic methods to quantify several of these components of dehumanization. Background ::: Related Computational Work While this is the first known computational work that focuses on dehumanization, we draw upon a growing body of literature at the intersection of natural language processing and social science research. We are particularly inspired by the area of automatically detecting subjective language, which was largely pioneered by Janyce Wiebe and colleagues, who developed novel lexical resources and algorithms for this task based on word and phrase distributions BIBREF5. These resources have been used as linguistically-informed features in machine learning classification of biased language BIBREF7. Other work has expanded this lexicon-based approach to account for the role of syntactic form for identifying the writer's perspective towards different entities mentioned in a text BIBREF6, BIBREF22. These methods have been used and expanded to analyze pernicious, but often implicit social biases BIBREF23. For example, BIBREF8 analyzed racial bias in police transcripts by training machine learning classifiers with linguistic features informed by politeness theory BIBREF8, and BIBREF24 investigated historical racial biases in mass media through changing word embeddings BIBREF24. Other work has looked at words in particular syntactic contexts to analyze power and agency, and how these concepts relate to gender bias in movies BIBREF25 and in mass media articles about the #MeToo movement BIBREF26. There has also been a growing focus on the analysis of subtle manifestations of social biases, such as condescension BIBREF27, microagressions BIBREF9, and “othering" language BIBREF28, BIBREF29. In addition, our focus on dehumanization is closely related to the detection and analysis of hate speech and abusive language BIBREF30, BIBREF31. Gender and racial bias have also been identified within widely-deployed NLP systems, for tasks including toxicity detection BIBREF32, sentiment analysis BIBREF33, coreference resolution BIBREF34, language identification BIBREF35, and in many other areas BIBREF36. Given the biases captured, reproduced, and perpetuated in NLP systems, there is also a growing interest in mitigating subjective biases BIBREF36, with approaches including modifying embedding spaces BIBREF37, BIBREF38, augmenting datasets BIBREF39, and adapting natural language generation methods to “neutralize" text BIBREF40. A related line of research has developed computational approaches to investigate language use and variation in discourse about complex sociopolitical issues in the media. For example, some work has drawn upon political communication theory to automatically detect the framing of an issue BIBREF41, BIBREF42, BIBREF43 through both supervised classification BIBREF44, BIBREF45 and unsupervised methods, such as topic modeling and lexicon induction BIBREF46, BIBREF47, BIBREF48. Scholars have also developed computational methods to identify lexical cues of partisan political speech, political slant in mass media, and polarization in social media BIBREF49, BIBREF50, BIBREF48. Background ::: Attitudes Towards LGBTQ Communities in the United States Because we apply our framework to study dehumanizing language in discussions about LGBTQ communities in the New York Times, some background is necessary. Overall, the American public has become more accepting of LGBTQ people and supportive of their rights. In 1977, equal percentages of respondents (43%) agreed and disagreed with the statement that gay or lesbian relations between consenting adults should be legal BIBREF2. Approval of gay and lesbian relations then decreased in the 1980s; in 1986, only 32% of respondents believed they should be legal. According to Gallup, attitudes have become increasingly positive since the 1990s, and in 2019, 73% responded that gay or lesbian relations should be legal. The Pew Research center began surveying Americans about their beliefs about same-sex marriage in 2001 and found similar trends BIBREF51. Between 2001 and 2019, support for same-sex marriage among respondents jumped from 35% to 61%. In addition to the public's overall attitudes, it is important to consider variation and change in the specific words used to refer to LGBTQ people. Because these labels potentially convey many different social meanings and have different relationships with dehumanization in the media, a primary focus of this study involves comparing different LGBTQ labels, specifically gay and homosexual. The Gallup survey asked for opinions on legality of “homosexual relations" until 2008, but then changed the wording to “gay and lesbian relations". This was likely because many people who identify as gay and lesbian find the word homosexual to be outdated and derogatory. According to the LGBTQ media monitoring organization GLAAD, homosexual's offensiveness originates in the word's dehumanizing clinical history, which had falsely suggested that “people attracted to the same sex are somehow diseased or psychologically/emotionally disordered" . Beyond its outdated clinical associations, some argue that the word homosexual is more closely associated with sex and all of its negative connotations simply by virtue of containing the word sex, while terms such as gay and lesbian avoid such connotations BIBREF52. Most newspapers, including the New York Times, almost exclusively used the word homosexual in articles about gay and lesbian people until the late 1980s BIBREF53. The New York Times began using the word gay in non-quoted text in 1987. Many major newspapers began restricting the use of the word homosexual in 2006 BIBREF52. As of 2013, the New York Times has confined the use of homosexual to specific references to sexual activity or clinical orientation, in addition to direct quotes and paraphrases . Beyond differences in how LGBTQ people perceive the terms gay or lesbian relative to homosexual, it has been shown that the specific choice of label can also have an effect on attitudes towards LGBTQ people. In 2012, BIBREF54 asked survey respondents about either “gay and lesbian rights" or “homosexual rights". Respondents who read the word “homosexual" tended to show less support for LGBTQ rights. This effect was primarily driven by high authoritarians, people who show high sensitivity to intergroup distinctions. The authors posit that homosexual makes social group distinctions more blatant than gay or lesbian, which then leads to a greater degree of psychological distancing, thus enabling participants to further remove LGBTQ people from their realm of moral consideration BIBREF54. Based on prior research and evolving media guidelines, we expect our computational analysis to show that homosexual occurs in more dehumanizing contexts than other LGBTQ labels, such as gay. Operationalizing Dehumanization In Section SECREF1, we briefly discussed multiple elements of dehumanization that have been identified in social psychology literature. Here we introduce and quantify lexical correlates to operationalize four of these components: negative evaluations of a target group, denial of agency, moral disgust, and use of non-human metaphors (particularly vermin). Operationalizing Dehumanization ::: Negative Evaluation of a Target Group One prominent aspect of dehumanization is extremely negative evaluations of members of a target group BIBREF4. Attribution of negative characteristics to members of a target group in order to exclude that group from “the realm of acceptable norms and values" is specifically the key component of delegitimization, a process of moral exclusion closely related to dehumanization. We hypothesize that this negative evaluation of a target group can be realized by words and phrases whose connotations have extremely low valence, where the valence of a word refers to the dimension of meaning corresponding to positive/negative (or pleasure/displeasure) BIBREF55, BIBREF56. Thus, we propose several valence lexicon-based approaches to measure this component: paragraph-level valence analysis, Connotation Frames, and word embedding neighbor valence. Each of these techniques has different advantages and disadvantages regarding precision and interpretability. Operationalizing Dehumanization ::: Negative Evaluation of a Target Group ::: Paragraph-level Valence Analysis As we mentioned above, one important dimension of affective meaning is valence, which corresponds to an individual's evaluation of an event or concept, ranging from negative/unpleasant to positive/pleasant BIBREF55, BIBREF57. A straightforward lexical approach to quantify negative evaluations of a target group is thus to measure the average valence over all words in corpora containing discussions of the target group. We quantify this by using the valence dimension from the NRC VAD lexicon, which contains real-valued scores ranging from zero to one for valence, arousal and dominance for 20,000 English words, where a score of zero is the lowest valence (most negative emotion) and a score of one is the highest possible valence (most positive emotion) BIBREF56. Words with the highest valence in the NRC VAD Lexicon include love, happy, and happily, while words with the lowest valence include toxic, nightmare, and shit. In our case study, we qualitatively found that paragraphs were the optimal level of context for analysis; full articles often focused on topics unrelated to LGBTQ communities, even if an LGBTQ label was mentioned somewhere in the article, while single sentences did not provide enough discussion to get a sense of how LGBTQ groups are represented by the newspaper. Thus, we calculate paragraph-level scores by taking the average valence score over all words in the paragraph that appear (or whose lemmas appear) in the NRC VAD lexicon. Operationalizing Dehumanization ::: Negative Evaluation of a Target Group ::: Connotation Frames While paragraph-level valence analysis is quick and simple, it is sometimes too coarse because we aim to understand the sentiment directed towards the target group, not just nearby in the text. For example, suppose the target group is named “B". A sentence such as “A violently attacked B" would likely have extremely negative valence, but the writer may not feel negatively towards the victim, “B". We address this by using BIBREF22's Connotation Frames Lexicon, which contains rich annotations for 900 English verbs (BIBREF22). Among other things, for each verb, the Connotation Frames Lexicon provides scores (ranging from -0.87 to 0.8) for the writer's perspective towards the verb's subject and object. In the example above for the verb attack, the lexicon lists the writer's perspective towards the subject “A", the attacker, as -0.6 (strongly negative) and the object “B" as 0.23 (weakly positive). We extract all subject-verb-object tuples containing at least one target group label using the Spacy dependency parser . For each subject and object, we capture the noun and the modifying adjectives, as group labels (such as gay) can often take either nominal or adjectival forms. For each tuple, we use the connotation frame lexicon to determine the writer's perspective either towards the subject if the group label appears in the subject noun phrase, or perspective towards the object if the label appears in the object noun phrase. We then average perspective scores over all tuples. Operationalizing Dehumanization ::: Negative Evaluation of a Target Group ::: Word Embedding Neighbor Valence While a Connotation Frames approach can be more precise than word-counting valence analysis, it limits us to analyzing SVO triples, which excludes a large portion of the available data about the target groups. This reveals a conundrum: broader context can provide valuable insights into the implicit evaluations of a social group, but we also want to directly probe attitudes towards the group itself. We address this tension by training vector space models to represent the data, in which each unique word in a large corpus is represented by a vector (embedding) in high-dimensional space. The geometry of the resulting vector space captures many semantic relations between words. Furthermore, prior work has shown that vector space models trained on corpora from different time periods can capture semantic change BIBREF58, BIBREF59. For example, diachronic word embeddings reveal that the word gay meant “cheerful" or “dapper" in the early 20th century, but shifted to its current meaning of sexual orientation by the 1970s. Because word embeddings are created from real-world data, they contain real-world biases. For example, BIBREF37 demonstrated that gender stereotypes are deeply ingrained in these systems. Though problematic for the widespread use of these models in computational systems, these revealed biases indicate that word embeddings can actually be used to identify stereotypes about social groups and understand how they change over time BIBREF24. This technique can similarly be applied to understand how a social group is negatively evaluated within a large text corpus. If the vector corresponding to a social group label is located in the semantic embedding space near negative words or terms with clearly negative evaluations, that group is likely negatively evaluated (and possibly dehumanized) in the text. These vector space models can be used in a diachronic study to capture subtle changes in the connotations of a group label over time, even when the denotational meaning of the label has remained constant. Furthermore, comparing differences in what words are most closely associated with related group labels can reveal meaning variation between these labels, even if they refer to similar populations. We first preprocess the data by lowercasing, removing numbers, and removing punctuation. We then use the word2vec skip-gram model to create word embeddings BIBREF60. The resulting vector spaces have 100 dimensions, and we specify the following additional parameters to the model: context window of size 10, minimum frequency of 5 for a word to be included, 10 negative samples, 10 iterations, and a sampling rate of $10^{-4}$. We then use cosine similarity to determine a group label's nearest neighbors in the vector space. For our diachronic analysis, we first train word2vec on the entire corpus, and then use the resulting vectors to initialize word2vec models for each year of data in order to encourage coherence and stability across years. After training word2vec, we zero-center and normalize all embeddings to alleviate the hubness problem BIBREF61. We then identify vectors for group labels by taking the centroid of all morphological forms of the label, weighted by frequency. For example, the vector representation for the label gay is actually the weighted centroid of the words gay and gays. This enables us to simultaneously account for adjectival, singular nominal, and plural nominal forms for each social group label with a single vector. Finally, we estimate the valence for each group label by identifying its 1000 nearest neighbors via cosine similarity (1000 was chosen arbitrarily), and calculating the average valence of all neighbors that appear in the NRC VAD valence lexicon. We also tried using a group label's vector representation to induce a valence score directly, instead of using nearest neighbors as a proxy, by adapting the regression-based sentiment prediction from BIBREF62 for word embeddings. We found that this approach yielded similar results as analyzing nearest neighbor valence but was difficult to qualitatively interpret. More details for and results from this technique can be found in the Appendix. Operationalizing Dehumanization ::: Denial of Agency Denial of agency refers to the lack of attributing a target group member with the ability to control their own actions or decisions BIBREF16. Automatically detecting the extent to which a writer attributes cognitive abilities to a target group member is an extraordinarily challenging computational task. Fortunately, the same lexicons used to operationalize negative evaluations provide resources for measuring lexical signals of denial of agency. Operationalizing Dehumanization ::: Denial of Agency ::: Connotation Frames As in Section SECREF6, we use Connotation Frames to quantify the amount of agency attributed to a target group. We use BIBREF25's extension of Connotation Frames for agency BIBREF25. Following BIBREF25's interpretation, entities with high agency exert a high degree of control over their own decisions and are active decision-makers, while entities with low agency are more passive BIBREF25. This contrast is particularly apparent in example sentences such as X searched for Y and X waited for Y, where the verb searched gives X high agency and waited gives X low agency BIBREF25. Additionally, BIBREF25's released lexicon for agency indicates that the subjects of verbs such as attack and praise are given high agency, while the subjects of doubts, needs, and owes are given low agency BIBREF25. This lexicon considers the agency attributed to subjects of nearly 2000 transitive and intransitive verbs. To use this lexicon to quantify denial of agency in our corpus, we extract all sentences' head verbs and their corresponding subjects, where the subject noun phrase contains a target group label. Unlike BIBREF22's real-valued Connotation Frames lexicon for perspective, the agency lexicon only provides binary labels, so we calculate the fraction of subject-verb pairs where a subject containing a group label was given high agency by its head verb. Operationalizing Dehumanization ::: Denial of Agency ::: Word Embedding Neighbor Dominance The NRC VAD lexicon's dominance annotations provide another resource for quantifying dehumanization BIBREF56. As with valence, the NRC VAD lexicon's dominance dimension contains real-valued scores between zero and one for 20,000 English words. However, it is important to note that the dominance lexicon primarily captures power, which is distinct from but closely related to agency. While power refers to one's control over others, agency refers to one's control over their own self. While this lexicon is a proxy, it qualitatively appears to capture signals of denial of agency; the highest dominance words are powerful, leadership, success, and govern, while the lowest dominance words are weak, frail, empty, and penniless. We thus take the same approach as in Section SECREF10 with the same vector space models. However, we now calculate the average dominance score of the 1000 nearest neighbors to each group label vector representation. As in Section SECREF10, we also induced a dominance score directly from a group label's vector representation by adapting the regression-based sentiment prediction from BIBREF62 for word embeddings. More details and results for this technique can be found in the Appendix. Operationalizing Dehumanization ::: Moral Disgust In order to operationalize the moral disgust component of dehumanization with lexical techniques, we draw inspiration from Moral Foundations theory, which postulates that there are five dimensions of moral intuitions: care, fairness/proportionality, loyalty/ingroup, authority/respect, and sanctity/purity BIBREF63. The negative end of the sanctity/purity dimension corresponds to moral disgust. While we do not directly incorporate Moral Foundations Theory in our framework for dehumanization, we utilize lexicons created by BIBREF64 corresponding to each moral foundation. The dictionary for moral disgust includes over thirty words and stems, including disgust*, sin, pervert, and obscen* (the asterisks indicate that the dictionary includes all words containing the preceding prefix, such as both obscene and obscenity). We opt for a vector approach instead of counting raw frequencies of moral disgust-related words because words that explicitly invoke moral disgust are very sparse in our news corpus. Furthermore, vectors are capable of capturing associations with the social group label itself, while counts in paragraphs and full news articles would not directly capture such associations. Using the word embeddings from Section SECREF10, we construct a vector to represent the concept of moral disgust, which is the average of the corresponding vectors for all words in the “Moral Disgust" dictionary, weighted by word frequency. This method of creating a vector from the Moral Foundations dictionary resembles that used by BIBREF65. We can then analyze implicit associations between a social group and moral disgust by calculating the cosine similarity between the group label's vector and the Moral Disgust concept vector, where a higher similarity suggests closer associations between the social group and moral disgust. As previously, each group label's vector is the average of its morphological variants' vectors, weighted by frequency. Operationalizing Dehumanization ::: Vermin as a Dehumanizing Metaphor Metaphors comparing humans to vermin have been especially prominent in dehumanizing groups throughout history BIBREF4, BIBREF15. Even if a marginalized social group is not directly equated to vermin in the press, this metaphor may be invoked in more subtle ways, such as through the use of verbs that are also associated with vermin (like scurry as opposed to the more neutral hurry) BIBREF66. While there is some natural language processing work on the complex task of metaphor detection (e.g. BIBREF67, BIBREF67), such systems would not be able to easily quantify such indirect associations. We thus quantify the metaphorical relationship between a social group and vermin by calculating similarities between these concepts in a distributional semantic vector space, as we did with moral disgust in Section SECREF14. We create a Vermin concept vector by calculating the average of the following vermin words' vectors, weighted by frequency: vermin, rodent(s), rat(s) mice, cockroaches, termite(s), bedbug(s), fleas . We do not include the singular mouse or flea because non-vermin senses of those words were more frequent (computer mouse and flea market, respectively), and word2vec does not account for polysemy. We calculate cosine similarity between each social group label and the Vermin concept vector. A large cosine similarity between a label and the Vermin vector suggests that the social group is closely associated with vermin. Table TABREF18 provides an overview of the four elements of dehumanization that we study and the lexical techniques used to quantify them. Data The data for our case study spans over thirty years of articles from the New York Times, from January 1986 to December 2015, and was originally collected by BIBREF68 BIBREF68. The articles come from all sections of the newspaper, such as “World", “New York & Region", “Opinion", “Style", and “Sports". Our distributional semantic methods rely on all of the available data in order to obtain the most fine-grained understanding of the relationships between words possible. For the other techniques, we extract paragraphs containing any word from a predetermined list of LGTBQ terms (shown in Table TABREF19). Each acronym label was matched insensitive to case and punctuation. Some currently prominent LGBTQ terms, such as queer and trans were not included in this study, as other senses of these words were considerably more frequent in earlier years. We further filtered out paragraphs from sections that typically did not pertain to news, such as “Arts", “Theater", and “Movies". While these sections could provide valuable information, we decided to focus on representation of LGBTQ groups in more news-related contexts. Furthermore, due to the original newspaper layout and subsequent digital processing, Arts sections often contained extremely long paragraphs containing numerous listings and schedules. Removing these sections substantially simplified data processing. A challenging question when analyzing mass media for subjective attitudes is deciding whose perspective we want to capture: an individual reporter, the institution, or society at large? In this case study, we aim to identify the institution's dehumanizing attitudes towards LGBTQ people. We represent the New York Times institution as a combination of the journalists' words in news articles, direct quotes, paraphrases from interviews, and published opinion articles. Therefore, despite our news focus, we include data from “Opinion" sections; while opinion articles are stylistically different from traditional journalistic reporting due to more overt biases and arguments, these articles are important in constructing the institution's perspective. In addition, we consider all text in each relevant paragraph, including quotes and paraphrases, because they are important to a newspaper's framing of an issue, as particular quotes representing specific stances are intentionally included or excluded from any given article BIBREF69. However, this approach is sometimes problematic when the newspaper quotes clearly extreme positions that they are unlikely to agree with, and we discuss this issue throughout our qualitative analysis. We refer to the remaining subset of the New York Times data after filtering as the LGBTQ corpus. The LGBTQ corpus consists of 93,977 paragraphs and 7.36 million tokens. However, a large increase in reporting on LGBTQ-related issues has led to a highly skewed distribution in the amount of data over years, with 1986 containing the least data (1144 paragraphs and 73,549 tokens) and 2012 containing the most (5924 paragraphs and 465,254 tokens). For all experiments, we also include results for the terms American and Americans. Our primary motivation for including American(s) is to contrast changes in LGBTQ labels' representation in the New York Times with another social group label that appears in sufficiently different contexts. This ensures that the changes we find in dehumanizing language towards LGBTQ groups do not apply uniformly to all social groups, and are thus not merely an artifact of the publication's overall language change. While a natural “control" variable would be labels such as straight or heterosexual, we found that these terms only occurred within discussions of LGBTQ communities, likely because they name socially unmarked categories. We also considered comparing LGBTQ labels to person/people, but we found that the word embedding-based experiments were particularly sensitive to syntactic forms, so we selected a word that behaves more similarly syntactically to gay and homosexual, particularly by having both nominal and adjectival uses. Nevertheless, it is important to note that American(s) is by no means a neutral control variable. Because of its in-group status for the New York Times (a U.S. institution), we expect our measurements to show that American(s) appears in more humanizing contexts than LGBTQ group labels; however, we do not expect to find substantial changes in the use of American(s) over time. Figure FIGREF20 shows the counts of group labels for each year in the New York Times from 1986 to 2015. For visualization purposes, only words with a total count greater than 1000 are shown. Notably, the relative frequency of homosexual decreased substantially over time, while gay, lesbian, and bisexual are more frequent in later years. We also see the emergence of the terms lgbt and transgender after 2000. Aggregated counts over all years for each LGBTQ label can be found in the Appendix. Results ::: Word Embeddings Using all of the New York Times data available, we created vector space models for each year using the methods described in Section SECREF10. Table TABREF22 shows the ten nearest neighboring words (by cosine similarity) to our vector representation of all LGBTQ terms, which is the weighted average of the embeddings of the LGBTQ terms from Table TABREF19. For visual convenience, words that occurred fewer then ten times and proper names are not shown. We also filtered out other LGBTQ labels and forms of the word heterosexual from these lists, as these words were commom neighbors for all terms across all years. Table TABREF22 shows that in 1986, LGBTQ groups were most highly associated with words that often convey a sense of sexual deviancy, including promiscuity, promiscuous, polygamy, bestiality, and pornography. These associations suggest that LGBTQ people were dehumanized to some extent at this time, and their identities were not fully recognized or valued. This shifted by 2000, where we no longer see associations between LGBTQ groups and ideas that evoke moral disgust. Instead, the 2000 vector space shows that LGBTQ people have become more associated with civil rights issues (suggested by interracial, homophobia, and discrimination). The words ordination and ordaining likely appear due to major controversies that arose at this time about whether LGBTQ people should be permitted to be ordained. We also see some indications of self-identification with the term openly. Finally, we see a slight shift towards associations with identity in 2015, with nearby words including nontransgender, closeted, equality, and sexuality. Curiously, the word abortion is a nearby term for all three years. Perhaps this is because opinions towards abortion and LGBTQ rights seem to be divided along similar partisan lines. Table TABREF23 shows the ten nearest neighboring words to our representations of gay and homosexual (which are weighted averages of their singular/adjectival and plural forms' vectors). Proper names, words appearing less than ten times that year, other LGBTQ terms, and forms of heterosexual are again filtered out for convenience of visualization and interpretation. Table TABREF23 reveals variation in social meaning between gay and homosexual from as early as 1986 despite denotational similarity, and these differences intensify over time. In 1986, gay is associated with terms of discrimination, civil rights and activism, such as homophobia, feminist, suffrage, sexism, and a.c.l.u (American Civil Liberties Union, a nonprofit organization). On the other hand, homosexual is primarily associated with words related to sexual activity (e.g. promiscuity, anal, intercourse, consenting). In 1986, this pattern may be likely because of discussions of sexual activity in the context of the AIDS epidemic, but we continue to observe the pejoration of the word homosexual over time and its increased use in dehumanizing contexts. In 2000, nearest neighbors of the word homosexual include deviant, immoral, and criminalizing. While gay becomes more associated with issues related to marriage equality and identity in 2015, homosexual becomes extremely associated with moral disgust and illicit activity, with nearest neighbors including bestiality, pedophilia, adultery, infanticide, and abhorrent. This qualitative analysis of word embedding neighbors reveals significant variation and change in the social meanings associated with LGBTQ group labels, with clear relationships to dehumanizing language. We will now present our quantitative results for measuring each component of dehumanization. Results ::: Negative Evaluation Towards Target Group ::: Paragraph-level Valence Analysis ::: Quantitative Results Figure FIGREF27 shows the average valence for paragraphs containing LGBTQ labels (and American(s) for comparison), where a paragraph's valence is simply the average valence over its words (or lemmas) that appear in the NRC VAD Valence Lexicon. The NRC VAD lexicons actually contain several LGBTQ terms, which all have lower than the average valence score of 0.5: transsexual (0.264), homosexual (0.333), lesbian (0.385), gay (0.388), and bisexual (0.438). These values contrast starkly with more positively-valenced entries in the lexicon, such as heterosexual (0.561), person (0.646), human (0.767), man (0.688), and woman (0.865). These disparities likely reveal biases among the human annotators whose judgments were used to construct the NRC VAD lexicon BIBREF56. While the lexicon may itself be an interesting artifact of dehumanizing attitudes towards LGBTQ people, we removed these terms from the lexicon before calculating average valence over each paragraph in the LGBTQ corpus in order to isolate linguistic signals in the New York Times data from annotation biases. Without this preprocessing step, the temporal trends and relative differences between all LGBTQ terms, gay, and homosexual remain roughly the same, but each of the LGBTQ labels would occur in significantly more negative paragraphs than American. Figure FIGREF27 shows the paragraph averages over five-year ranges for visualization purposes primarily due to data sparsity in later years for homosexual (there were just 208 paragraphs containing homosexual in 2014, relative to 3669 containing gay in the same year). Analysis of overlapping confidence intervals and Wilcoxon signed-rank tests over the means for each of the thirty years indicates that gay and all LGBTQ terms occur in significantly more positive paragraphs than homosexual ($p < 0.0001$). However, American actually occurs in more negative paragraphs than all LGBTQ terms or gay ($p < 0.05$). A linear regression analysis over all years reveals that all LGBTQ terms, gay, and homosexual all significantly increase in paragraph-level valence over time ($p < 0.0001$). However, when considering just the last 15 years, gay still significantly increases in paragraph-level valence, while homosexual may be trending downward, although this trend does not reach significance in our data ($p = 0.078$). The paragraph-level valence analysis shown in Figure FIGREF27 suggests that LGBTQ groups have become increasingly positively evaluated over time, and have thus likely been less dehumanized in the New York Times. However, the lack of a statistically significant increase (rather, a slight downward trend) in average valence for paragraphs containing homosexual between 2001 and 2015 suggests that attitudes towards and evaluations of people described as homosexual have not improved in the same way as those described by other LGBTQ labels. Finally, this measurement does not support our initial hypothesis that LGBTQ groups have been more negatively evaluated than the other social group American(s), but does reveal that the observed trend for LGBTQ labels is not merely an artifact of changing reporting styles, since paragraphs containing American(s) show a very different pattern. Overall, this result demonstrates substantial language change in the New York Times's discussion of LGBTQ people as well as variation in the contexts where different group labels appear, particularly homosexual. Results ::: Negative Evaluation Towards Target Group ::: Paragraph-level Valence Analysis ::: Qualitative Analysis How well does paragraph-level valence analysis capture negative evaluations of a target group, and thus a major component of dehumanization? To facilitate a qualitative evaluation of this technique, we identify several hundred paragraphs with the highest and lowest average valence. It appears that most paragraphs with high valence scores do express positive evaluations of LGBTQ individuals, and those with low scores express negative evaluations. Table TABREF29 contain examples with extremely high and low valence. We identify several major themes from these results, which are included in the Interpretation column of Table TABREF29. Most paragraphs with high valence scores emphasize equal rights, while some focus on the activities of advocacy organizations. On the other end, numerous paragraphs with extremely low valence tend to focus on violence against LGBTQ people, discussions of disease (especially AIDS), and LGBTQ issues internationally. Other themes that emerged in low-valence paragraphs include reports on (and direct quotes from) public figures who dehumanized people as well as portrayals of LGBTQ people as reckless, irresponsible, and angry. While this technique accurately captures the valence for many paragraphs, we also identify several shortcomings. Some of the extreme outliers are extremely short paragraphs, including subtitles within articles which are included as paragraphs in the data. Table TABREF30 shows several examples that were mischaracterized by our paragraph-level valence analysis technique. In addition to the short subtitles, there are several paragraphs with highly positive average valence that seem to express more negative evaluations of LGBTQ people. In the third paragraph in Table TABREF30, the valence is skewed by the positive words supported (0.853) and marriage (0.844) even though the paragraph is actually discussing low support for gay marriage. While the fourth paragraph argues that gay couples would be subpar parents relative to straight couples, it uses positive terms such as love and ideal. Furthermore, kinship terms tend to be assigned highly positive values in the NRC VAD Valence Lexicon, including child (0.912), children (0.857), and family (0.968). Similarly, even though the final example describes discrimination based on sexual orientation, the paragraph's average valence is impacted by positive kinship terms such as father (0.812) and mother (0.931) . Overall, our qualitative analysis of paragraph-level valence scores shows that highly positive valence often accompanies expressions of positive evaluation towards LGBTQ groups, and low valence often accompanies expressions of negative evaluation. However, paragraph-level valence scores are also impacted by specific words cued by various topics; paragraphs about same-sex marriage tend to be more positive because words like marriage, marry, and couple have high valence scores (greater than 0.8), while paragraphs reporting on hate crimes tend to be more negative because they contain more extremely low-valence words related to crime, violence, injury, and death. Furthermore, we have found that this simple lexicon-based method has difficulties in disentangling perspectives within the text. For example, even though there are linguistic signals of dehumanization expressed in articles reporting on anti-LGBTQ violence and homophobic speech, these dehumanizing attitudes are not necessarily from the viewpoint of the journalist or the institution. Nevertheless, there could be an overall dehumanizing effect if the media's discussions of a marginalized social group largely emphasizes events that harm people, such as violent crimes and disease. It is possible that repeated associations between LGBTQ identity labels and such negative contexts contribute to negative evaluations of LGBTQ groups. Results ::: Negative Evaluation Towards Target Group ::: Connotation Frames of Perspective ::: Quantitative Results Figure FIGREF34 shows the writer's average perspective (valence) towards noun phrases containing either any LGBTQ labels, gay(s), homosexual(s), or the comparison group American(s) using the Connotation Frames lexicon BIBREF22. Note that the wide variation, particularly for homosexual, is likely due to sparsity, as limiting the connotation frames analysis to verbs' immediate subject and direct object noun phrase dependents (consisting of only determiners, adjectives, and nouns) greatly reduced the amount of data for each year. For example, there were only 39 triples for homosexual in 2015. We thus show results aggregated over five-year intervals, as in Figure FIGREF27. As with paragraph-level valence, the writer's perspective towards the label homosexual is significantly more negative than towards gay ($ p < 0.001$). Linear regression indicates that perspectives towards noun phrases named by any LGBTQ term, gay, and American have all significantly increased over time ($p < 0.01$). However, the trends are still quite different, as the slopes for gay and all LGBTQ terms are an order of magnitude greater than American ($m =(1.1\pm 0.39)\times 10^{-4}$ for American, $m=(1.4\pm 0.18)\times 10^{-3}$ for all LGBTQ terms, and $m=(1.1\pm 0.22)\times 10^{-3}$ for gay). Furthermore, the writer's perspective towards noun phrases containing homosexual have significantly decreased over time ($p < 0.0001$). Overall, connotation frames' perspective scores reveal a similar pattern as the paragraph-level valence analysis, where LGBTQ groups overall appear to be more positively evaluated in the New York Times over time. Unlike gay and the aggregated all LGBTQ terms, the label homosexual undergoes pejoration, as homosexual becomes increasingly used when (implicitly) expressing negative attitudes towards LGBTQ people. Results ::: Negative Evaluation Towards Target Group ::: Connotation Frames of Perspective ::: Qualitative Analysis To qualitatively analyze how well the connotation frames' lexicon capture the negative evaluation of a target group component of dehumanization, we identify subject-verb-object tuples where the verb indicates that the writer has extremely positive or negative perspective towards either the subject or object. The first two paragraphs in Table TABREF36 were identified among those with the most negative writers' perspectives towards phrases containing LGBTQ labels. The first paragraph (within a quote) uses the phrase any homosexual act as the direct object to the verb committed, which has the effect of framing homosexuality as a crime or other illicit activity. By deeming gay candidates unworthy of the priesthood, the speaker clearly negatively evaluates LGBTQ people. On the opposite end, many of the paragraphs labeled by our method as containing extremely positive perspectives towards phrases containing LGBTQ labels do appear to have positive evaluations of these groups. The second and third paragraphs of Table TABREF36 illustrate this, where the gays are viewed positively for having saved a town, and gay rights advocates are praised for their work. However, we found several instances where paragraphs seem to have been mislabeled, which are shown in Table TABREF37. In the first paragraph of Table TABREF37, our technique identifies gay marriage as the subject of dependent of the negative-perspective verb harmed, but does not account for the preceding text, which actually reveals that the paragraph contradicts the premise that gay marriage causes harm, and thus does not contain overtly negative evaluations of LGBTQ groups (although this particular example reveals the difficulty of operationalizing this component because ProtectMarriage groups strongly oppose same-sex marriage and may itself have negative evaluations of LGBTQ people). The second example similarly shows that this simple method does not adequately account for various forms of negation, as the positive-perspective verb protect is actually negated. The last example in Table TABREF37 presents a complex case, and it is even qualitatively challenging to determine the writer's perspective towards LGBTQ people. Our method identifies gays as the subject of the verb strengthen, even though the subject should be the gerund allowing gays (into the military), and the lexicon's entry for the writer's perspective towards the subject of of strengthen is a highly positive 0.7. However, the object of this verb is the terrorist organization Al Qaeda; our background knowledge suggests that the capacity to strengthen Al Qaeda would reflect negative perspectives. However, this additional context provided by the rest of the paragraph indicates that the writer is being sarcastic and considers the proposition that gays have any impact on strengthening Al Qaeda to be ridiculous. Finally, the writer emphasizes their own stance in opposition to the Missouri congressional candidate by calling upon common stereotypes of gay people being good at dancing and accessorizing. Measuring the connotation frames' lexicon perspective scores over verbs' subjects and direct objects cannot leverage as much context or data as measuring valence over paragraphs using the NRC VAD lexicon labeled for 20,000 words. However, this technique can make more fine-grained distinctions regarding the writer's (and institution's) attitudes directed towards LGBTQ people and is not as dramatically impacted by the emotional valence of the topic discussed. However, with both techniques presented, we have difficulties disentangling the journalist's perspective from those expressed by others and simply reported by the journalist. While removing direct quotations may partially address this issue, we deliberately did not remove text from direct quotes or paraphrases. The journalists and newspaper make intentional decisions about what text to include and exclude from quotations, which could still meaningfully represent their perspectives and values BIBREF69. Results ::: Negative Evaluation Towards Target Group ::: Word Embedding Neighbor Valence ::: Quantitative Results Figure FIGREF40 shows the average valence scores of the 1000 nearest neighbors to the vector representations of gay, homosexual, all LGBTQ terms, and American for each year. Consistent with other results for negative evaluations of a target group, the vector representations for all LGBTQ terms and gay show similar patterns, although the LGBTQ vector more closely resembles the homosexual vector in earlier years since homosexual was more frequently used in all discussions of LGBTQ communities at that time. In contrast to our other techniques to quantify negative evaluations of a target group, this measurement notably shows that the valence of American's neighboring words is significantly greater than any of the LGBTQ group representations' neighbors over all years (Wilcoxon's signed-rank test, $p < 0.0001$), indicating that American is used in more positive contexts than LGBTQ terms. Furthermore, all LGBTQ vectors' neighbors have an average valence below the neutral 0.5. However, the average valence of the aggregated all LGBTQ terms representation's neighbors significantly increases over time ($p < 0.0001$), suggesting some increasing humanization in the language used in discussions of LGBTQ people. Figure FIGREF40 also reveals dramatic connotational differences between gay and homosexual. As shown by non-overlapping confidence intervals and a Wilcoxon signed-rank test, the average valence for homosexual's neighbors is significantly lower than gay's neighbors over all years ($p < 0.0001$). Gay's average neighbor valence slightly increases over time, but does not reach significance ($p = 0.060$), but homosexual's neighboring words become significantly more negative over time ($p < 0.0001$). Analyzing the valence of the nearest neighbors indicates that homosexual has long been used in more negative (and potentially dehumanizing) contexts than gay, and that these words' meanings have further diverged as the label homosexual has been used in increasingly negative contexts over time. Results ::: Negative Evaluation Towards Target Group ::: Word Embedding Neighbor Valence ::: Qualitative Analysis Compared to the previous methods, one limitation of using word embeddings to quantify negative evaluations of a target group is that the embeddings are not easily interpretable by analyzing a small sample of data. Instead, we assess this technique by identifying LGBTQ terms' nearest neighbors in several outlier years. To facilitate this qualitative analysis, we identify a set of unique nearest neighbors for each LGBTQ label in each outlier year, where a word is a unique nearest neighbor for a given LGBTQ term and year if it is not in that term's top 1000 nearest neighbors in any other year. Table TABREF42 contains several example paragraphs that illustrate overarching themes for the outlier years 1993, 1999, and 2014. In the year 1999, gay, homosexual and the aggregated representation all LGBTQ terms were all more more closely associated with low-valence words than in almost any other year. We connected this finding to a period of intense reporting in the months following the October 1998 murder of a gay Wyoming college student, Matthew Shepard, which drew national attention to and condemnation of anti-LGBTQ violence. Because many occurrences of LGBTQ group labels appeared in similar contexts as content about this incident, terms directly related to Matthew Shepard's case have closer vector representations to LGBTQ terms in this year. For example, gay and all LGBTQ terms's 1000 nearest neighbors include wyoming in 1998 and 1999 and shepard from the years 1998-2001. Unique nearest neighbors for gay in 1999 include other terms that could be connected to this incident, including imprisoned, slanderous, disgraceful, unethical, and unjustifiable. Not only was Matthew Shepard's tragic murder deeply rooted in the dehumanization of LGBTQ people, but Shepard was further dehumanized in the media's emphasis on the gruesome details of his death BIBREF71. BIBREF71 argue that the media's framing of this case actually further socially stigmatized LGBTQ people. Surprisingly, measuring a group label's valence by the valence of its nearest word embedding neighbors reveals that the most negative year for gay and all LGBTQ terms since 1999 was 2014, the second most-recent year of data. Unique nearest neighbors for gay in 2014 include harassing, intimidation, and stigma. We identified several major themes in 2014 that co-occurred with LGBTQ group labels and possibly led to this distributional semantic pattern, primarily reporting on anti-LGBTQ laws and attitudes in Uganda and Russia (particularly in light of the 2014 Winter Olympics in Sochi). We also found that the terms athletes and winterolympics appeared in both gay and all LGBTQ terms's nearest 1000 neighbors in 2014 (athletes only appeared as a nearby neighbor for two other years: 1994 and 2013). In addition, the terms Uganda and Ugandan are among all LGBTQ terms and gay's nearest 1000 neighbors in 2014, but no other year. Unlike in 1999 and 2014, LGBTQ terms in the year 1993 seem to be associated with higher-valence words, especially homosexual, whose nearby words had the highest average valence in 1993 out of all years. Homosexual's unique nearest neighbors in 1993 include the high-valence words morale, pledge, civilian, compromise, force, and readiness. This words appear to be connected with numerous stories in 1993 covering the controversy over whether or not LGBTQ people should be allowed to serve in the military. Results ::: Negative Evaluation Towards Target Group ::: Denial of Agency ::: Quantitative Results Figure FIGREF45 shows the agency of each group label based on its head verb's entry in the Connotation Frames lexicon for agency and the group label's position relative to that verb BIBREF25. As in Figure FIGREF34 for perspective, there is large variance for each year due to data sparsity when using the Connotation Frames lexicon, particularly for homosexual, which is considerably less frequent than gay or other LGBTQ terms in later years. In extracting subject-verb pairs for this analysis, we decided to maximize precision by extracting only the nouns and their immediate adjectival modifiers, which limited the amount of data. For visualization purposes, we thus show average agency over five-year intervals. Overall, Wilcoxon signed-rank tests on the means for all group labels for each of the 30 years indicates that gay occurs in contexts with significantly higher agency than homosexual ($p < 0.0001$). All four group labels actually significantly decrease in agency over time according to linear regressions over all 30 years ($p < 0.001$), but the slope for homosexual is much greater ( $m=(-7.9\pm 1.3)\times 10^{-3}$ for homosexual, compared to $m=(-3.9\pm .55)\times 10^{-3}$ for gay, and $m=(-1.5\pm .46)\times 10^{-3}$ for all LGBTQ terms). Furthermore in the most recent 15 years, gay and all LGBTQ terms show no significant change ($p = 0.097$ for gay and $p = 0.14$ for all LGBTQ terms), but homosexual still decreases significantly in agency ($p < 0.05$). Figure FIGREF45 suggests that LGBTQ groups experience greater denial of agency in the New York Times than the institution's in-group identifier American. Furthermore, people described as homosexual experience even more denial of agency than people who are described as gay, illustrating how these two terms differ with respect to dehumanization. Unlike the improving attitudes indicated by our analysis of negative evaluations of a target group, it appears that denial of agency increased over time for all LGBTQ groups. However, the relatively rapid change in agency for homosexual is consistent with our other results of this label's pejoration. Results ::: Negative Evaluation Towards Target Group ::: Denial of Agency ::: Qualitative Analysis We again qualitatively investigate the labels assigned by this technique for a sample of paragraphs. In general, the binary labels for positive and negative agency appear to be reasonably accurate, as shown by the first four example in Table TABREF47. Verbs that attribute high agency to the subject include develop and endorse, suggesting that the LGBTQ-aligned subjects are in control and actively making their own decisions. On the other end, LGBTQ people are given low agency when presented as the subjects of passive verbs such as face and acknowledge. The Connotation Frames Lexicon labels for agency seem to appropriately describe LGBTQ people's agency especially accurately for low agency; we could not readily identify counterexamples in our sample where LGBTQ people actually were portrayed with high agency but were labeled with low agency. However, we found several mischaracterizations in which LGBTQ people were labeled as having a high degree of agency but do not qualitatively seem to be portrayed as particularly agentive or in control of their own actions. This Connotation Frames technique considered the example below to attribute high agency to LGBTQ people because the word homosexual appeared in the subject of the high-agency verb violate; however, homosexual actually modifies relationships, not people themselves. Furthermore, this debate within religion appears to be devoid of input from LGBTQ people and thus does not portray them as particularly agentive. At the same time, it underscored a stark division in Judaism over the place of homosexuals in society. Orthodox rabbinical groups believe that homosexual relationships violate Jewish law... (1996) Results ::: Negative Evaluation Towards Target Group ::: Word Embedding Neighbor Dominance ::: Quantitative Results Figure FIGREF51 shows the average dominance of each group label's word2vec representation's 1000 nearest neighbors according to the NRC VAD Dominance Lexicon BIBREF56, which we use as an estimate of the dominance associated with each group label. American is clearly significantly associated with greater dominance than gay, homosexual, and all LGBTQ terms (Wilcoxon signed-rank test; $p < 0.0001$), and gay has significantly higher dominance than homosexual ($p < 0.0001$). While the dominance associated with gay and all LGBTQ terms significantly increased over the three decades ($p < 0.01$), dominance significantly decreased for homosexual ($p < 0.0001$). Furthermore, while the increase in average nearest neighbor dominance does not reach significance for gay and all LGBTQ terms in the most recent 15 years ($p = 0.47$ for all LGBTQ terms and $p = 0.066$ for gay), homosexual significantly decreases in that time span ($p < 0.0001$). Even though dominance may more directly encode power rather than agency, the NRC VAD Dominance Lexicon is a useful resource for operationalizing denial of agency because of the close relationship between these concepts. As with the Connotation Frames' agency measurements shown in Figure FIGREF45, our analysis of the dominance of each label's nearest word2vec neighbors suggest that LGBTQ groups experience greater denial of agency than the New York Times's in-group American. Both techniques show differences between the labels gay and homosexual, where homosexual is consistently associated with lower agency than gay and further decreases over time. However, these two measurements suggest slightly different temporal dynamics for the denial of agency of LGBTQ people in the earliest half of the data; we observe a decrease in agency for LGBTQ groups with Connotation Frames, but a slight increase with nearest neighbor dominance. Results ::: Negative Evaluation Towards Target Group ::: Word Embedding Neighbor Dominance ::: Qualitative Analysis Using the VAD Dominance Lexicon to calculate average dominance of each social group label appears to correspond well to our notion of denial of agency. Because gay's nearest neighbors have a much higher average dominance than homosexual's for most years, we qualitatively evaluate this technique by comparing words that appear as a nearby neighbor for gay and not homosexual for a given year, and vice versa. Words that appear within the 1000 words nearest to gay and not homosexual in the 2015 word2vec space include high-agency words such as brave, freedom, and advocate, which have dominance scores of 0.917, 0.905, and 0.818 respectively according to the VAD lexicon. On the other hand, words nearby to homosexual and not gay in 2015 include low-agency words such as submissive (0.173), unprotected (0.197), and ignorant (0.223). We find similar results comparing gay and homosexual in the word2vec model trained on 1986 data; high-agency words such as liberation (0.857) and mobilization (0.787) are nearby neighbors for gay and not homosexual, while low-agency words such as depressive (0.202) and involuntary (0.231) are nearby neighbors for homosexual and not gay. We additionally qualitatively investigate the vector space models corresponding to several outlier years. In 1993, homosexual's neighbors have by far the highest average dominance compared to any other year for this label. Similar to what we saw with neighbor valence, this is likely due to a large volume of discourse about the military in debates surrounding the “Don't Ask, Don't Tell" legislation. Some high-dominance words that are unique to homosexual's nearest neighbors in 1993 include directive (0.918), force (0.905), enforce (0.836) and troops (0.804). Also similar to the neighbor valence results, gay's neighbors' in 1999 have the lowest average dominance than any other year. This could also likely be connected to Matthew Shepard's death and the subsequent outrage; unique neighbors for gay in 1999 include disgraceful (0.240), irresponsible (0.241), unethical (0.269), imprisoned (0.302), and slanderous (0.337). Results ::: Moral Disgust ::: Quantitative Results Figure FIGREF55 shows the changing relationships between all LGBTQ terms, gay, homosexual and the dehumanizing concept of Moral Disgust. Because the cosine distance between American and Moral Disgust is significantly greater over all years than any LGBTQ representation (Wilcoxon signed-rank test; $p < 0.0001$), American is the group label least associated with Moral Disgust. Furthermore, the cosine distance between gay and Moral Disgust is significantly greater than the distance between homosexual and Moral Disgust for every year ($p < 0.0001$), indicating that homosexual is more closely associated with Moral Disgust than gay is. Linear regression analyses show that American significantly decreases in cosine distance over the thirty years ($p < 0.0001$), while all LGBTQ terms and gay significantly increase in cosine distance ($p < 0.0001$), indicated weakening associations between LGBTQ people and moral disgust over time. Unlike the aggregated representation for all LGBTQ labels and gay, the increasing cosine distance between homosexual and Moral Disgust does not change significantly over time ($p = 0.54$), but does significantly decrease in the last 15 years ($p < 0.05$). Overall, these measurements of associations between LGBTQ people and Moral Disgust are largely consistent with the other operationalizations of dehumanization. All LGBTQ group labels are more closely associated with Moral Disgust than the newspaper's in-group term American, but these associations weaken over time which suggests increased humanization. Notably, the term homosexual has always been more associated with Moral Disgust than the denotationally-similar term gay, and homosexual actually becomes more closely associated with this dehumanizing concept in recent years. Results ::: Moral Disgust ::: Qualitative Analysis From investigating word embedding neighbors in Table TABREF23, we saw that homosexual has become increasingly associated with immoral concepts, suggesting that moral disgust is a mechanism by which LGBTQ people are dehumanized. Although rarely implicitly invoked, the connection between LGBTQ people and disgust is supported by several examples from the data, as shown in Table TABREF57. The most dramatic change over time occurs in the late 1980s and early 1990s, when gay, homosexual, and all LGBTQ terms become rapidly less associated with Moral Disgust based on their increasing cosine distance. Because a lot of disease-related words are included in the Moral Disgust component of the Moral Foundations Dictionary BIBREF64, this is likely due to decreasing attention to the AIDS epidemic. Results ::: Vermin as a Dehumanizing Metaphor ::: Quantitative Results Figure FIGREF60 shows the relationships between LGBTQ labels (and American) and the dehumanizing vermin metaphor, quantified as the cosine distance between the labels' word2vec vectors and a Vermin concept vector representation, calculated as the centroid of multiple vermin-related words. As with Moral Disgust in Figure FIGREF55, the in-group term American is significantly further away from Vermin over all years than any of the LGBTQ representations (Wilcoxon signed-rank test; $p < 0.0001$). The cosine distance between gay and Vermin is also significantly greater than the distance between homosexual and Vermin ($p < 0.0001$), indicating that homosexual is more closely associated with the dehumanizing vermin metaphor than gay is. Furthermore, while all LGBTQ terms and gay significantly increase in cosine distance (become less associated with Vermin) over time (linear regression; $p < 0.0001$), homosexual slightly decreases in cosine distance (greater association with Vermin), but this decrease does not reach significance ($p = 0.13$). Our measurement of the implicit vermin metaphor shows a similar pattern as our measurements for moral disgust, denial of agency, and negative evaluations of the target group. Overall, LGBTQ groups are more implicitly associated with vermin than the comparison social group American, but this association is weakening over time, suggesting increased humanization. Unlike the labels gay and the aggregated representation of all LGBTQ terms, the association between vermin and the label homosexual does not significantly change over time. Results ::: Vermin as a Dehumanizing Metaphor ::: Qualitative Analysis Metaphors comparing humans to vermin have been especially prominent in dehumanizing groups throughout history BIBREF4, BIBREF15. Although no New York Times writers directly compare LGBTQ people to vermin, this metaphor may be invoked in more subtle ways. Furthermore, several paragraphs in the data demonstrate that people explicitly mention the metaphor in order to argue against it. These examples, shown in Table TABREF62, further point to the existence of LGBTQ people-as-vermin metaphors, even if they are not frequently directly invoked. Discussion Our framework for the computational linguistic analysis of dehumanization involves identifying major dimensions of dehumanization from social psychology literature, proposing linguistic correlates for each dimension, and developing robust and interpretable computational methods to quantify these correlates. We applied this framework to study the dehumanization of LGBTQ people in the New York Times over the course of 30 years, from 1986 to 2015. We measured four dimensions of dehumanization: negative evaluations of the target group, denial of agency, moral disgust, and (implicit) invocations of vermin metaphors. Overall, we found increasingly humanizing descriptions of LGBTQ people over time. LGBTQ people have become more associated with positive emotional language, suggesting that negative evaluations of the target group have diminished. LGBTQ people have become more associated with higher-dominance words, suggesting decreasing denial of agency, although this finding was not replicated with the verb-centric “Connotation Frames" measurement. Furthermore, labels for LGBTQ people have moved further away from the concepts of moral disgust and vermin within distributional semantic representations, suggesting that harmful associations between LGBTQ people and these dehumanizing concepts have weakened over time. Despite these broad trends, we found that the labels gay and homosexual exhibit strikingly different patterns among these four components of dehumanization. Homosexual is associated with more negative (low valence) language than gay, suggesting that there are more negative evaluations of people described as homosexual than gay. The label homosexual is also associated with greater denial of agency, and has stronger connections to moral disgust and vermin than gay does. Furthermore, unlike for LGBTQ labels as a whole, discussions of homosexual people do not seem to have become more humanizing over time. On the contrary, several techniques suggested that language used alongside the label homosexual has actually become more dehumanizing after about 2000. Through its repeated use in these contexts, the use of the word homosexual appears to have emerged as an index of more dehumanizing attitudes towards LGBTQ people than other labels. Despite the denotational similarity between homosexual and gay, these computational techniques can track the stark differences and divergences in social meanings. In this work, we restricted our analysis to the lexical level for ease of interpretability, and leveraged a diverse array of existing resources, including the NRC VAD lexicon BIBREF56, Connotation Frames lexicons BIBREF22, BIBREF25, and the Moral Foundations Dictionary BIBREF64. For negative evaluations of a target group and denial of agency, we propose multiple different techniques that vary in accuracy and interpretability. While word-counting methods are often inaccurate due to their simplicity, their results can be easily understood, while embedding-based methods tend to suffer the opposite problem. We do not claim to have found the optimal balance of model quality and interpretability in this study, but carefully considering this tradeoff is especially important in work that seeks to characterize complex and sensitive social phenomena such as dehumanization. Discussion ::: Limitations and Future Work As the first attempt to computationally analyze dehumanization, this work has many limitations. While our qualitative analysis demonstrates that the proposed techniques capture some linguistics signals of dehumanization, other aspects of the data erroneously impact our measurements as well. For example, we still cannot entirely disentangle a journalist's perspective from the topics and events in the stories they discuss. These techniques would predict that articles emphasizing anti-LGBTQ violence are dehumanizing; in reality, these stories may express dehumanizing attitudes but those views are not necessarily held by the journalist. An exciting area of future work could involve developing more sophisticated computational methods that can better disambiguate the writer's attitudes, attitudes of people mentioned in the text, and topics and events, while recognizing that each of these could contribute to the overall representation of marginalized groups in the media. Furthermore, the present work uses word2vec since all known affective lexicons are type-level, but contextualized embedding-based methods have great potential for more nuanced analyses of dehumanizing language by leveraging token-level representations BIBREF72, BIBREF73. Another limitation of this study is the lack of ground truth labels for what content is dehumanizing, which has prevented us from developing supervised methods and quantitative evaluations. While it is challenging to directly solicit opinions from human annotators about such a complex and subjective concept such as dehumanization, annotation strategies such as best-worst scaling can be promising approaches BIBREF74, particularly if they primarily consider opinions from members of the social groups of interest. These ground-truth labels could enable future work to develop models to unify all of these distinct components of dehumanization into a single measurement for a given text. Our framework could also be greatly expanded to include more insights from dehumanization theory. Beyond the four elements of dehumanization that we discuss in this article, social psychology research has identified many other cognitive processes that contribute to dehumanization, such as psychological distancing, essentialism (the perception that the target group has some essence that makes them categorically and fundamentally different), and denial of subjectivity (neglect of a target group member's personal feelings and experiences). BIBREF75, BIBREF21, BIBREF76, BIBREF3. Scholars also often differentiate between two forms of dehumanization, animalistic (likening humans to animals) and mechanistic (likening humans to inanimate objects or machines), and the linguistic signals underpinning each of these forms could substantially differ from one another BIBREF4. For simplicity and ease of interpretation, we focus on quantifying lexical cues of dehumanization. However, our understanding of the language of dehumanization would be enriched by considering linguistic features beyond the lexicon. For example, BIBREF77 has shown that definite plurals in English (e.g. the gays) have a unique social and pragmatic effect compared to bare plurals (e.g. gays) by packaging individual entities into one monolith and setting this group apart from the speaker. Indexing a speaker's non-membership in the group being discussed creates social distance between the speaker and group BIBREF77, which makes it likely that using definite plurals to label marginalized social groups plays an important role in dehumanization. Similarly, examining non-lexical signals could help us capture elements of dehumanization that we could not easily identify only through lexical resources. For example, the part of speech of a group label (such as gay as a noun or adjective) could have implications for essentialism, as adjectives simply name attributes to some entity, while nouns explicitly state the entity's category membership and encapsulates other stereotypes associated with that category BIBREF78, BIBREF79, BIBREF76, BIBREF80. We furthermore believe that incorporating discourse-level analysis, such as examining the role of direct quotes in an article and who is being quoted, could provide informative insights that could address some limitations discussed earlier. We supported our proposed framework with a case study of LGBTQ representation in the New York Times. This case study had a number of limitations as an analysis of the dehumanization of LGBTQ people in the media. We only looked at one data source, which may have its own biases and does not capture the entirety of media discourse about LGBTQ people. Furthermore, we only studied newspaper articles written in (Standard) American English. Future work could assess how well these linguistic cues we measure generalize to other languages and focus on cross-linguistic comparisons of dehumanizing language. Finally, most of our case study involved analysis of an aggregated LGBTQ representation,with a specific focus on the labels gay and homosexual, primarily due to the amount of data available. However, as a result, we still have little understanding about the nuanced differences and changes in the representation of LGBTQ people who don't identify with (or aren't identified as) these labels. Discussion ::: Ethical Implications Our hope in pursuing this work is to draw attention to issues of media representation of marginalized groups and to eventually help make the online world a safer and kinder place. An important part of this mission is to acknowledge the ethical implications and potential issues of our own work. The methods that we use to quantify dehumanization are themselves biased and potentially harmful. For example, we showed in Section SECREF25 that the lexicon that used to measure valence contains its own anti-LGBTQ biases by considering LGBTQ group labels to be primarily negative/unpleasant. We also trained word2vec models on New York Times data, which presents biases. While it is typically concerning when such models are trained on biased data due to harmful downstream effects BIBREF37, we leverage this data as a useful resource for uncovering human biases and understanding how biases emerge in media discourse about LGBTQ people. Another concern of this work is that we used computational methods to represent human beings in order to analyze dehumanization on a large scale. However, representing people as sequences of numbers (especially in our vector-based experiments) is inherently dehumanizing them. While we hope that this work will humanize and empower marginalized groups, we acknowledge that it also has this opposite effect. Other ethical implications of this project appear within our case study. Due to the time scale of our study (beginning in 1986), we did not include LGBTQ labels such as queer or trans, which often had different meanings and were often found in contexts unrelated to LGBTQ identities in earlier years. Furthermore, much of our analysis focused on an aggregated representation for LGBTQ people, which unintentionally minimized the vast diversity of social identities encompassed within this umbrella. We highlighted striking temporal changes and differences between the labels gay and homosexual, which were primarily chosen because these labels were well-represented throughout all years of data. However, emphasizing these labels at the expense of others may contribute to the continued erasure of people who are marginalized even within LGBTQ communities. Conclusion This work is the first known computational linguistic study of dehumanization, and provides contributions to multiple fields. The proposed framework and techniques to quantify salient components of dehumanization can shed light on linguistic variation and change in discourses surrounding marginalized groups. Furthermore, these tools for large-scale analysis have potential to complement smaller-scale psychological studies of dehumanization. Finally, this work has implications for automatically detecting and understanding media bias and abusive language online. Acknowledgements We would like to thank Rob Voigt, Chris Potts, Penny Eckert, David Jurgens, Ceren Budak, Anna-Marie Sprenger, and Ismael Mendoza for their insightful contributions and feedback. We would also like to thank our audience at NWAV-46, who provided helpful feedback on an earlier version of this project. Appendix ::: Valence prediction results In addition to quantifying the negative evaluation of a target group by calculating the average valence score of a group label's vector representation's nearest neighbors according to the NRC VAD lexicon, we also directly induced a valence score from the vector representation itself. We use the zero-centered, normalized, word embeddings created for each year as features in a regression model to directly predict valence (Field et al., 2019). Specifically, we train ridge regression models for each year, where each year's Word2Vec representation for words from the NRC VAD lexicon are features and the lexicon's valence scores are labels. 85% of words from the VAD lexicon were kept as the training set, and the remaining 15% was used as a test set to evaluate performance. We then use this set of regression models to predict a group label's valence from its vector representation. Figure FIGREF67 shows the directly-induced valence score for each set of group labels from the ridge regression fit to the NRC VAD valence lexicon. Because we trained a different Word2Vec model for each year, we trained a different ridge regression model for each year. Over all thirty years, the Pearson correlation between predicted valence and actual valence on the test set ranged from 0.617 to 0.675, and $R^2$ values ranged from 0.423 to 0.451.The predicted scores show similar trends to the average neighbor valence. Homosexual has the most negative valence for every year, followed by gay and the aggregate over all LGBTQ terms, followed by American with the most positive valence. American is significantly more positive than all LGBTQ labels over all years (Wilcoxon signed-rank test; $p < 0.0001$), and gay is significantly more positive than homosexual for every year (Wilcoxon signed-rank test; $p < 0.0001$). Despite the stability in differences between these terms across experiments, the regression analysis suggests different temporal dynamics. Figure FIGREF67 shows that the predicted valence for gay, homosexual, and all LGBTQ terms all increase over time, but homosexual's predicted valence decreases from 2001 to 2015 ($p < 0.01$). This result is consistent with the other findings in this article in illustrating the pejoration of homosexual in recent years. Appendix ::: Agency prediction results Because we use the NRC VAD dominance lexicon to quantify denial of agency in the same way we use the valence lexicon to quantify negative evaluations of a target group, we again directly induce scores directly from target group label representation. We train another set of ridge regression models with word embedding features for each year, but now we fit the model to the NRC VAD dominance lexicon's scores rather than the valence scores. Figure FIGREF69 shows the predicted dominance for each group label, which is calculated by fitting ridge regression models to the NRC VAD Dominance Lexicon using the lexicon's words' Word2Vec representations as features for each year. Pearson correlations between predicted and actual dominance scores for all regression models ranged from 0.561 to 0.614 on the test set, and $R^2$ values range from 0.338 to 0.361. Consistent with the average neighbor dominance approach , American has significantly greater dominance than any of the other LGBTQ terms (Wilcoxon signed-rank test; $p < 0.0001$). However a Wilcoxon signed-rank test over each year's means shows that there is no significant difference between the terms gay and homosexual ($p = 0.21$). The predicted dominance of all LGBTQ terms and gay significantly decrease ($p < 0.0001$), but not in the last 15 years ($p = 0.85$ for all LGBTQ terms and $p = 0.51$ for gay). Homosexual does not significantly change in predicted dominance in either the full 30 years ($p = 0.96$) or in the last 15 years ($p = 0.89$). Why do gay and homosexual show such different patterns in directly-induced predicted dominance from the regression than average dominance based on their neighbors' entries in the NRC VAD lexicon? While the average dominance over the nearest neighbors showed significant differences, they were small in magnitude (often corresponding to differences of less than 0.025 points on a scale from 0 to 1). Perhaps because the word2vec features could only predict just over a third of the variance in dominance scores, they were not able to capture subtle semantic distinctions that could characterize differences in dominance scores
How do they identify discussions of LGBTQ people in the New York Times?
d41e20ec716b5904a272938e5a8f5f3f15a7779e
How Ġdo Ġthey Ġidentify Ġdiscussions Ġof ĠLGBTQ Ġpeople Ġin Ġthe ĠNew ĠYork ĠTimes ?
act Ġparagraphs Ġcontaining Ġany Ġword Ġfrom Ġa Ġpredetermined Ġlist Ġof ĠLG TB Q Ġterms Ġ
<s> How Ġdo Ġthey Ġidentify Ġdiscussions Ġof ĠLGBTQ Ġpeople Ġin Ġthe ĠNew ĠYork ĠTimes ? </s> Key words : </s> com put ational Ġsoc iol ingu istics , Ġdehuman ization , Ġlex ical Ġvariation , Ġlanguage Ġchange , Ġmedia , ĠNew ĠYork ĠTimes , ĠLGBTQ </s> Warning : Ġthis Ġpaper Ġcontains Ġmaterial Ġthat Ġsome Ġmay Ġfind Ġoffensive Ġor Ġupsetting . </s> Introduction </s> Despite Ġthe ĠAmerican Ġpublic 's Ġincreasing Ġacceptance Ġof ĠLGBTQ Ġpeople Ġand Ġrecent Ġlegal Ġsuccesses , ĠLGBTQ Ġindividuals Ġfrequently Ġremain Ġthe Ġtargets Ġof Ġhate Ġand Ġviolence ĠB IB REF 0 , ĠB IB REF 1 , ĠB IB REF 2 . ĠAt Ġthe Ġcore Ġof Ġthis Ġissue Ġis Ġdehuman ization , ĠâĢ ľ the Ġact Ġof Ġperce iving Ġor Ġtreating Ġpeople Ġas Ġless Ġthan Ġhuman âĢ Ŀ ĠB IB REF 3 , Ġa Ġprocess Ġthat Ġheavily Ġcontributes Ġto Ġextreme Ġinter group Ġbias ĠB IB REF 4 . ĠLanguage Ġis Ġcentral Ġto Ġstudying Ġthis Ġphenomenon ; Ġlike Ġother Ġforms Ġof Ġbias ĠB IB REF 5 , ĠB IB REF 6 , ĠB IB REF 7 , ĠB IB REF 8 , ĠB IB REF 9 , Ġdehuman izing Ġattitudes Ġare Ġexpressed Ġthrough Ġsubtle Ġlinguistic Ġmanip ulations , Ġeven Ġin Ġcarefully - edited Ġtexts . ĠIt Ġis Ġcrucial Ġto Ġunderstand Ġthe Ġuse Ġof Ġsuch Ġlinguistic Ġsignals Ġin Ġmainstream Ġmedia , Ġas Ġthe Ġmedia 's Ġrepresentation Ġof Ġmarginalized Ġsocial Ġgroups Ġhas Ġfar - reaching Ġimplications Ġfor Ġsocial Ġacceptance , Ġpolicy , Ġand Ġsafety . </s> While Ġsmall - scale Ġstudies Ġof Ġdehuman ization Ġand Ġmedia Ġrepresentation Ġof Ġmarginalized Ġcommunities Ġprovide Ġvaluable Ġinsights Ġ( e . g . ĠB IB REF 10 ), Ġthere Ġexist Ġno Ġknown Ġlarge - scale Ġanalyses , Ġlikely Ġdue Ġto Ġdifficulties Ġin Ġquant ifying Ġsuch Ġa Ġsubjective Ġand Ġmult id imensional Ġpsychological Ġprocess . ĠHowever , Ġthe Ġability Ġto Ġdo Ġlarge - scale Ġanalysis Ġis Ġcrucial Ġfor Ġunderstanding Ġhow Ġdehuman izing Ġattitudes Ġhave Ġevolved Ġover Ġlong Ġperiods Ġof Ġtime . ĠFurthermore , Ġby Ġbeing Ġable Ġto Ġaccount Ġfor Ġa Ġgreater Ġamount Ġof Ġmedia Ġdiscourse Ġat Ġonce , Ġlarge - scale Ġtechniques Ġcan Ġprovide Ġa Ġmore Ġcomplete Ġview Ġof Ġthe Ġmedia Ġenvironment Ġto Ġwhich Ġthe Ġpublic Ġis Ġexposed . </s> L ingu istics Ġand Ġcomputer Ġscience Ġoffer Ġvaluable Ġmethods Ġand Ġinsights Ġon Ġwhich Ġsuch Ġlarge - scale Ġtechniques Ġmight Ġbe Ġdeveloped Ġfor Ġthe Ġstudy Ġof Ġdehuman ization . ĠBy Ġleveraging Ġmore Ġinformation Ġabout Ġthe Ġcontexts Ġin Ġwhich Ġmarginalized Ġgroups Ġare Ġdiscussed , Ġcomputational Ġlinguistic Ġmethods Ġnot Ġonly Ġenable Ġlarge - scale Ġstudy Ġof Ġa Ġcomplex Ġpsychological Ġphenomenon , Ġbut Ġcan Ġeven Ġreveal Ġlinguistic Ġvariations Ġand Ġchanges Ġthat Ġare Ġnot Ġeasily Ġidentifiable Ġthrough Ġqualitative Ġanalysis Ġalone . </s> In Ġthis Ġwork , Ġwe Ġdevelop Ġa Ġcomputational Ġlinguistic Ġframework Ġfor Ġanalyzing Ġdehuman izing Ġlanguage , Ġwith Ġa Ġfocus Ġon Ġlex ical Ġsignals Ġof Ġdehuman ization . ĠSocial Ġpsychologists Ġhave Ġidentified Ġnumerous Ġcomponents Ġof Ġdehuman ization , Ġsuch Ġas Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġdenial Ġof Ġagency , Ġmoral Ġdisgust , Ġand Ġlik ening Ġmembers Ġof Ġa Ġtarget Ġgroup Ġto Ġnon - human Ġentities Ġsuch Ġas Ġver min . ĠDrawing Ġupon Ġthis Ġrich Ġbody Ġof Ġliterature , Ġwe Ġfirst Ġidentify Ġclear Ġlinguistic Ġanalog s Ġfor Ġthese Ġcomponents Ġand Ġpropose Ġseveral Ġcomputational Ġtechniques Ġto Ġmeasure Ġthese Ġlinguistic Ġcorrelates . ĠWe Ġthen Ġapply Ġthis Ġgeneral Ġframework Ġto Ġexplore Ġchanging Ġrepresentations Ġof ĠLGBTQ Ġgroups Ġin Ġthe ĠNew ĠYork ĠTimes Ġover Ġthe Ġspan Ġof Ġthree Ġdecades . ĠWe Ġadditionally Ġuse Ġthis Ġlens Ġof Ġdehuman ization Ġto Ġinvestigate Ġdifferences Ġin Ġsocial Ġmeaning Ġbetween Ġthe Ġden otation ally - similar Ġlabels Ġgay Ġand Ġhomosexual . </s> This Ġpaper Ġaims Ġto Ġbridge Ġthe Ġgaps Ġbetween Ġcomputational Ġmodeling , Ġsoc iol ingu istics , Ġand Ġdehuman ization Ġresearch Ġwith Ġimplications Ġfor Ġseveral Ġdisciplines . ĠIn Ġaddition Ġto Ġenabling Ġlarge - scale Ġstudies Ġof Ġdehuman izing Ġlanguage Ġand Ġmedia Ġrepresentation Ġof Ġmarginalized Ġsocial Ġgroups , Ġthese Ġtechniques Ġcan Ġbe Ġbuilt Ġinto Ġsystems Ġthat Ġseek Ġto Ġcapture Ġboth Ġconscious Ġand Ġunconscious Ġbiases Ġin Ġtext . ĠFurthermore , Ġthis Ġwork Ġhas Ġimplications Ġfor Ġimproving Ġmachines ' Ġabilities Ġto Ġautomatically Ġdetect Ġhate Ġspeech Ġand Ġabusive Ġlanguage Ġonline , Ġwhich Ġare Ġtypically Ġunderpin ned Ġby Ġdehuman izing Ġlanguage . ĠFinally , Ġour Ġcase Ġstudy Ġdemonstrates Ġthat Ġsuch Ġcomputational Ġanalyses Ġof Ġdiscussions Ġabout Ġmarginalized Ġgroups Ġcan Ġprovide Ġunique Ġinsights Ġinto Ġlanguage Ġvariation Ġand Ġchange Ġwithin Ġsensitive Ġsociop olitical Ġcontexts , Ġand Ġhelp Ġus Ġunderstand Ġhow Ġpeople Ġ( and Ġinstitutions ) Ġuse Ġlanguage Ġto Ġexpress Ġtheir Ġideologies Ġand Ġattitudes Ġtowards Ġcertain Ġsocial Ġgroups . </s> Background Ġ: :: ĠDe human ization </s> Our Ġlex ical Ġsemantic Ġanalysis Ġinvolves Ġquant ifying Ġlinguistic Ġcorrelates Ġof Ġcomponent Ġpsychological Ġprocesses Ġthat Ġcontribute Ġto Ġdehuman ization . ĠOur Ġapproaches Ġare Ġinformed Ġby Ġsocial Ġpsychology Ġresearch Ġon Ġdehuman ization , Ġwhich Ġis Ġbriefly Ġsummarized Ġhere . ĠPrior Ġwork Ġhas Ġidentified Ġnumerous Ġrelated Ġprocesses Ġthat Ġcomprise Ġdehuman ization ĠB IB REF 4 . ĠOne Ġsuch Ġcomponent Ġis Ġlik ening Ġmembers Ġof Ġthe Ġtarget Ġgroup Ġto Ġnon - human Ġentities , Ġsuch Ġas Ġmachines Ġor Ġanimals ĠB IB REF 4 , ĠB IB REF 11 , ĠB IB REF 12 . ĠBy Ġperce iving Ġmembers Ġof Ġa Ġtarget Ġgroup Ġto Ġbe Ġnon - human , Ġthey Ġare ĠâĢ ľ outside Ġthe Ġboundary Ġin Ġwhich Ġmoral Ġvalues , Ġrules , Ġand Ġconsiderations Ġof Ġfairness Ġapply " ĠB IB REF 13 , Ġwhich Ġthus Ġleads Ġto Ġviolence Ġand Ġother Ġforms Ġof Ġabuse . ĠMet aph ors Ġand Ġimagery Ġrelating Ġtarget Ġgroups Ġto Ġver min Ġare Ġparticularly Ġinsidious Ġand Ġplayed Ġa Ġprominent Ġrole Ġin Ġthe Ġgenocide Ġof ĠJews Ġin ĠNazi ĠGermany Ġand ĠT uts is Ġin ĠRwanda ĠB IB REF 14 . ĠMore Ġrecently , Ġthe Ġver min Ġmetaphor Ġhas Ġbeen Ġinvoked Ġby Ġthe Ġmedia Ġto Ġdiscuss Ġterrorists Ġand Ġpolitical Ġleaders Ġof Ġmajority - Muslim Ġcountries Ġafter ĠSeptember Ġ11 ĠB IB REF 15 . ĠAccording Ġto ĠB IB REF 16 , Ġthe Ġver min Ġmetaphor Ġis Ġparticularly Ġpowerful Ġbecause Ġit Ġconceptual izes Ġthe Ġtarget Ġgroup Ġas ĠâĢ ľ eng aged Ġin Ġthreatening Ġbehavior , Ġbut Ġdevoid Ġof Ġthought Ġor Ġemotional Ġdesire ". </s> Dis g ust Ġunder lies Ġthe Ġdehuman izing Ġnature Ġof Ġthese Ġmetaphors Ġand Ġis Ġitself Ġanother Ġimportant Ġelement Ġof Ġdehuman ization . ĠDis g ust Ġcontributes Ġto Ġmembers Ġof Ġtarget Ġgroups Ġbeing Ġperceived Ġas Ġless - than - human Ġand Ġof Ġnegative Ġsocial Ġvalue ĠB IB REF 17 . ĠIt Ġis Ġoften Ġev oked Ġ( both Ġin Ġreal Ġlife Ġand Ġexperimental Ġsettings ) Ġthrough Ġlik ening Ġa Ġtarget Ġgroup Ġto Ġanimals . ĠB IB REF 18 Ġfind Ġthat Ġprim ing Ġparticipants Ġto Ġfeel Ġdisgust Ġfacilitates ĠâĢ ľ moral Ġexclusion Ġof Ġout - groups ". ĠExper iments Ġby ĠB IB REF 17 Ġand ĠB IB REF 19 Ġsimilarly Ġfind Ġthat Ġdisgust Ġis Ġa Ġpredictor Ġof Ġdehuman izing Ġperceptions Ġof Ġa Ġtarget Ġgroup . ĠBoth Ġmoral Ġdisgust Ġtowards Ġa Ġparticular Ġsocial Ġgroup Ġand Ġthe Ġinvocation Ġof Ġnon - human Ġmetaphors Ġare Ġfacilitated Ġby Ġessential ist Ġbeliefs Ġabout Ġgroups , Ġwhich ĠB IB REF 4 Ġpresents Ġas Ġa Ġnecessary Ġcomponent Ġof Ġdehuman ization . ĠIn Ġorder Ġto Ġdistinguish Ġbetween Ġhuman Ġand Ġnon - human , Ġdehuman ization Ġrequires Ġan Ġexaggerated Ġperception Ġof Ġinter group Ġdifferences . ĠEssential ist Ġthinking Ġthus Ġcontributes Ġto Ġdehuman ization Ġby Ġleading Ġto Ġthe Ġperception Ġof Ġsocial Ġgroups Ġas Ġcategor ically Ġdistinct , Ġwhich Ġin Ġturn Ġemphasizes Ġinter group Ġdifferences ĠB IB REF 4 </s> According Ġto ĠB IB REF 4 , Ġmuch Ġof Ġthe Ġprior Ġresearch Ġdescribes ĠâĢ ľ extremely Ġnegative Ġevaluations Ġof Ġothers " Ġas Ġa Ġmajor Ġcomponent Ġof Ġdehuman ization . ĠThis Ġis Ġespecially Ġpronounced Ġin ĠB IB REF 20 's Ġaccount Ġof Ġdeleg it im ization , Ġwhich Ġinvolves Ġusing Ġnegative Ġcharacteristics Ġto Ġcategor ize Ġgroups Ġthat Ġare ĠâĢ ľ ex cluded Ġfrom Ġthe Ġrealm Ġof Ġacceptable Ġnorms Ġand Ġvalues " ĠB IB REF 20 . ĠWhile ĠB IB REF 20 Ġdefines Ġdeleg it im ization Ġas Ġa Ġdistinct Ġprocess , Ġhe Ġconsiders Ġdehuman ization Ġto Ġbe Ġone Ġmeans Ġof Ġdeleg it im ization . ĠB IB REF 13 Ġalso Ġdiscusses Ġbroader Ġprocesses Ġof Ġmoral Ġexclusion , Ġone Ġof Ġwhich Ġis Ġdehuman ization . ĠA Ġclosely Ġrelated Ġprocess Ġis Ġpsychological Ġdist ancing , Ġin Ġwhich Ġone Ġperce ives Ġothers Ġto Ġbe Ġobjects Ġor Ġnonexistent ĠB IB REF 13 . ĠB IB REF 21 Ġidentifies Ġelements Ġthat Ġcontribute Ġto Ġthe Ġobject ification Ġ( and Ġthus Ġdehuman ization ) Ġof Ġwomen , Ġone Ġof Ġwhich Ġis Ġdenial Ġof Ġsubject ivity , Ġor Ġthe Ġhabitual Ġneglect Ġof Ġone 's Ġexperiences , Ġemotions , Ġand Ġfeelings . </s> Another Ġcomponent Ġof Ġdehuman ization Ġis Ġthe Ġdenial Ġof Ġagency Ġto Ġmembers Ġof Ġthe Ġtarget Ġgroup ĠB IB REF 4 . ĠAccording Ġto ĠB IB REF 16 , Ġthere Ġare Ġthree Ġtypes Ġof Ġagency : Ġ" the Ġability Ġto Ġ( 1 ) Ġexperience Ġemotion Ġand Ġfeel Ġpain Ġ( aff ective Ġmental Ġstates ), Ġ( 2 ) Ġact Ġand Ġproduce Ġan Ġeffect Ġon Ġtheir Ġenvironment Ġ( behavior al Ġpotential ), Ġand Ġ( 3 ) Ġthink Ġand Ġhold Ġbeliefs Ġ( c ognitive Ġmental Ġstates ) ĠB IB REF 16 . ĠDe human ization Ġtypically Ġinvolves Ġthe Ġdenial Ġof Ġone Ġor Ġmore Ġof Ġthese Ġtypes Ġof Ġagency ĠB IB REF 16 . </s> In ĠSection ĠSEC REF 4 , Ġwe Ġintroduce Ġcomputational Ġlinguistic Ġmethods Ġto Ġquantify Ġseveral Ġof Ġthese Ġcomponents Ġof Ġdehuman ization . </s> Background Ġ: :: ĠRelated ĠComput ational ĠWork </s> While Ġthis Ġis Ġthe Ġfirst Ġknown Ġcomputational Ġwork Ġthat Ġfocuses Ġon Ġdehuman ization , Ġwe Ġdraw Ġupon Ġa Ġgrowing Ġbody Ġof Ġliterature Ġat Ġthe Ġintersection Ġof Ġnatural Ġlanguage Ġprocessing Ġand Ġsocial Ġscience Ġresearch . ĠWe Ġare Ġparticularly Ġinspired Ġby Ġthe Ġarea Ġof Ġautomatically Ġdetecting Ġsubjective Ġlanguage , Ġwhich Ġwas Ġlargely Ġpioneered Ġby ĠJ any ce ĠW ie be Ġand Ġcolleagues , Ġwho Ġdeveloped Ġnovel Ġlex ical Ġresources Ġand Ġalgorithms Ġfor Ġthis Ġtask Ġbased Ġon Ġword Ġand Ġphrase Ġdistributions ĠB IB REF 5 . ĠThese Ġresources Ġhave Ġbeen Ġused Ġas Ġlingu istically - informed Ġfeatures Ġin Ġmachine Ġlearning Ġclassification Ġof Ġbiased Ġlanguage ĠB IB REF 7 . ĠOther Ġwork Ġhas Ġexpanded Ġthis Ġlex icon - based Ġapproach Ġto Ġaccount Ġfor Ġthe Ġrole Ġof Ġsynt actic Ġform Ġfor Ġidentifying Ġthe Ġwriter 's Ġperspective Ġtowards Ġdifferent Ġentities Ġmentioned Ġin Ġa Ġtext ĠB IB REF 6 , ĠB IB REF 22 . </s> These Ġmethods Ġhave Ġbeen Ġused Ġand Ġexpanded Ġto Ġanalyze Ġper n icious , Ġbut Ġoften Ġimplicit Ġsocial Ġbiases ĠB IB REF 23 . ĠFor Ġexample , ĠB IB REF 8 Ġanalyzed Ġracial Ġbias Ġin Ġpolice Ġtranscripts Ġby Ġtraining Ġmachine Ġlearning Ġclass ifiers Ġwith Ġlinguistic Ġfeatures Ġinformed Ġby Ġpolit eness Ġtheory ĠB IB REF 8 , Ġand ĠB IB REF 24 Ġinvestigated Ġhistorical Ġracial Ġbiases Ġin Ġmass Ġmedia Ġthrough Ġchanging Ġword Ġembed d ings ĠB IB REF 24 . ĠOther Ġwork Ġhas Ġlooked Ġat Ġwords Ġin Ġparticular Ġsynt actic Ġcontexts Ġto Ġanalyze Ġpower Ġand Ġagency , Ġand Ġhow Ġthese Ġconcepts Ġrelate Ġto Ġgender Ġbias Ġin Ġmovies ĠB IB REF 25 Ġand Ġin Ġmass Ġmedia Ġarticles Ġabout Ġthe Ġ# Me Too Ġmovement ĠB IB REF 26 . ĠThere Ġhas Ġalso Ġbeen Ġa Ġgrowing Ġfocus Ġon Ġthe Ġanalysis Ġof Ġsubtle Ġmanifestations Ġof Ġsocial Ġbiases , Ġsuch Ġas Ġcondesc ension ĠB IB REF 27 , Ġmicro ag ress ions ĠB IB REF 9 , Ġand ĠâĢ ľ other ing " Ġlanguage ĠB IB REF 28 , ĠB IB REF 29 . ĠIn Ġaddition , Ġour Ġfocus Ġon Ġdehuman ization Ġis Ġclosely Ġrelated Ġto Ġthe Ġdetection Ġand Ġanalysis Ġof Ġhate Ġspeech Ġand Ġabusive Ġlanguage ĠB IB REF 30 , ĠB IB REF 31 . </s> Gender Ġand Ġracial Ġbias Ġhave Ġalso Ġbeen Ġidentified Ġwithin Ġwidely - de ploy ed ĠN LP Ġsystems , Ġfor Ġtasks Ġincluding Ġtoxicity Ġdetection ĠB IB REF 32 , Ġsentiment Ġanalysis ĠB IB REF 33 , Ġcore ference Ġresolution ĠB IB REF 34 , Ġlanguage Ġidentification ĠB IB REF 35 , Ġand Ġin Ġmany Ġother Ġareas ĠB IB REF 36 . ĠGiven Ġthe Ġbiases Ġcaptured , Ġreproduced , Ġand Ġperpet uated Ġin ĠN LP Ġsystems , Ġthere Ġis Ġalso Ġa Ġgrowing Ġinterest Ġin Ġmitigating Ġsubjective Ġbiases ĠB IB REF 36 , Ġwith Ġapproaches Ġincluding Ġmodifying Ġembed ding Ġspaces ĠB IB REF 37 , ĠB IB REF 38 , Ġaugment ing Ġdatasets ĠB IB REF 39 , Ġand Ġadapting Ġnatural Ġlanguage Ġgeneration Ġmethods Ġto ĠâĢ ľ neutral ize " Ġtext ĠB IB REF 40 . </s> A Ġrelated Ġline Ġof Ġresearch Ġhas Ġdeveloped Ġcomputational Ġapproaches Ġto Ġinvestigate Ġlanguage Ġuse Ġand Ġvariation Ġin Ġdiscourse Ġabout Ġcomplex Ġsociop olitical Ġissues Ġin Ġthe Ġmedia . ĠFor Ġexample , Ġsome Ġwork Ġhas Ġdrawn Ġupon Ġpolitical Ġcommunication Ġtheory Ġto Ġautomatically Ġdetect Ġthe Ġframing Ġof Ġan Ġissue ĠB IB REF 41 , ĠB IB REF 42 , ĠB IB REF 43 Ġthrough Ġboth Ġsupervised Ġclassification ĠB IB REF 44 , ĠB IB REF 45 Ġand Ġun super vised Ġmethods , Ġsuch Ġas Ġtopic Ġmodeling Ġand Ġlex icon Ġinduction ĠB IB REF 46 , ĠB IB REF 47 , ĠB IB REF 48 . ĠScholars Ġhave Ġalso Ġdeveloped Ġcomputational Ġmethods Ġto Ġidentify Ġlex ical Ġcues Ġof Ġpartisan Ġpolitical Ġspeech , Ġpolitical Ġsl ant Ġin Ġmass Ġmedia , Ġand Ġpolarization Ġin Ġsocial Ġmedia ĠB IB REF 49 , ĠB IB REF 50 , ĠB IB REF 48 . </s> Background Ġ: :: ĠAtt itudes ĠTowards ĠLGBTQ ĠCommunities Ġin Ġthe ĠUnited ĠStates </s> Because Ġwe Ġapply Ġour Ġframework Ġto Ġstudy Ġdehuman izing Ġlanguage Ġin Ġdiscussions Ġabout ĠLGBTQ Ġcommunities Ġin Ġthe ĠNew ĠYork ĠTimes , Ġsome Ġbackground Ġis Ġnecessary . ĠOverall , Ġthe ĠAmerican Ġpublic Ġhas Ġbecome Ġmore Ġaccepting Ġof ĠLGBTQ Ġpeople Ġand Ġsupportive Ġof Ġtheir Ġrights . ĠIn Ġ1977 , Ġequal Ġpercentages Ġof Ġrespondents Ġ( 43 %) Ġagreed Ġand Ġdisagreed Ġwith Ġthe Ġstatement Ġthat Ġgay Ġor Ġlesbian Ġrelations Ġbetween Ġconsent ing Ġadults Ġshould Ġbe Ġlegal ĠB IB REF 2 . ĠAppro val Ġof Ġgay Ġand Ġlesbian Ġrelations Ġthen Ġdecreased Ġin Ġthe Ġ1980 s ; Ġin Ġ1986 , Ġonly Ġ32 % Ġof Ġrespondents Ġbelieved Ġthey Ġshould Ġbe Ġlegal . ĠAccording Ġto ĠGallup , Ġattitudes Ġhave Ġbecome Ġincreasingly Ġpositive Ġsince Ġthe Ġ1990 s , Ġand Ġin Ġ2019 , Ġ73 % Ġresponded Ġthat Ġgay Ġor Ġlesbian Ġrelations Ġshould Ġbe Ġlegal . ĠThe ĠPew ĠResearch Ġcenter Ġbegan Ġsurve ying ĠAmericans Ġabout Ġtheir Ġbeliefs Ġabout Ġsame - sex Ġmarriage Ġin Ġ2001 Ġand Ġfound Ġsimilar Ġtrends ĠB IB REF 51 . ĠBetween Ġ2001 Ġand Ġ2019 , Ġsupport Ġfor Ġsame - sex Ġmarriage Ġamong Ġrespondents Ġjumped Ġfrom Ġ35 % Ġto Ġ61 %. </s> In Ġaddition Ġto Ġthe Ġpublic 's Ġoverall Ġattitudes , Ġit Ġis Ġimportant Ġto Ġconsider Ġvariation Ġand Ġchange Ġin Ġthe Ġspecific Ġwords Ġused Ġto Ġrefer Ġto ĠLGBTQ Ġpeople . ĠBecause Ġthese Ġlabels Ġpotentially Ġconvey Ġmany Ġdifferent Ġsocial Ġmeanings Ġand Ġhave Ġdifferent Ġrelationships Ġwith Ġdehuman ization Ġin Ġthe Ġmedia , Ġa Ġprimary Ġfocus Ġof Ġthis Ġstudy Ġinvolves Ġcomparing Ġdifferent ĠLGBTQ Ġlabels , Ġspecifically Ġgay Ġand Ġhomosexual . ĠThe ĠGallup Ġsurvey Ġasked Ġfor Ġopinions Ġon Ġlegality Ġof ĠâĢ ľ hom osexual Ġrelations " Ġuntil Ġ2008 , Ġbut Ġthen Ġchanged Ġthe Ġwording Ġto ĠâĢ ľ gay Ġand Ġlesbian Ġrelations ". ĠThis Ġwas Ġlikely Ġbecause Ġmany Ġpeople Ġwho Ġidentify Ġas Ġgay Ġand Ġlesbian Ġfind Ġthe Ġword Ġhomosexual Ġto Ġbe Ġoutdated Ġand Ġderogatory . ĠAccording Ġto Ġthe ĠLGBTQ Ġmedia Ġmonitoring Ġorganization ĠGL A AD , Ġhomosexual 's Ġoff ens iveness Ġorigin ates Ġin Ġthe Ġword 's Ġdehuman izing Ġclinical Ġhistory , Ġwhich Ġhad Ġfalsely Ġsuggested Ġthat ĠâĢ ľ people Ġattracted Ġto Ġthe Ġsame Ġsex Ġare Ġsomehow Ġdise ased Ġor Ġpsychologically / em otion ally Ġdis ordered " Ġ. ĠBeyond Ġits Ġoutdated Ġclinical Ġassociations , Ġsome Ġargue Ġthat Ġthe Ġword Ġhomosexual Ġis Ġmore Ġclosely Ġassociated Ġwith Ġsex Ġand Ġall Ġof Ġits Ġnegative Ġcon notations Ġsimply Ġby Ġvirtue Ġof Ġcontaining Ġthe Ġword Ġsex , Ġwhile Ġterms Ġsuch Ġas Ġgay Ġand Ġlesbian Ġavoid Ġsuch Ġcon notations ĠB IB REF 52 . ĠMost Ġnewspapers , Ġincluding Ġthe ĠNew ĠYork ĠTimes , Ġalmost Ġexclusively Ġused Ġthe Ġword Ġhomosexual Ġin Ġarticles Ġabout Ġgay Ġand Ġlesbian Ġpeople Ġuntil Ġthe Ġlate Ġ1980 s ĠB IB REF 53 . ĠThe ĠNew ĠYork ĠTimes Ġbegan Ġusing Ġthe Ġword Ġgay Ġin Ġnon - qu oted Ġtext Ġin Ġ1987 . ĠMany Ġmajor Ġnewspapers Ġbegan Ġrestricting Ġthe Ġuse Ġof Ġthe Ġword Ġhomosexual Ġin Ġ2006 ĠB IB REF 52 . ĠAs Ġof Ġ2013 , Ġthe ĠNew ĠYork ĠTimes Ġhas Ġconfined Ġthe Ġuse Ġof Ġhomosexual Ġto Ġspecific Ġreferences Ġto Ġsexual Ġactivity Ġor Ġclinical Ġorientation , Ġin Ġaddition Ġto Ġdirect Ġquotes Ġand Ġparaph r ases Ġ. </s> Beyond Ġdifferences Ġin Ġhow ĠLGBTQ Ġpeople Ġperceive Ġthe Ġterms Ġgay Ġor Ġlesbian Ġrelative Ġto Ġhomosexual , Ġit Ġhas Ġbeen Ġshown Ġthat Ġthe Ġspecific Ġchoice Ġof Ġlabel Ġcan Ġalso Ġhave Ġan Ġeffect Ġon Ġattitudes Ġtowards ĠLGBTQ Ġpeople . ĠIn Ġ2012 , ĠB IB REF 54 Ġasked Ġsurvey Ġrespondents Ġabout Ġeither ĠâĢ ľ gay Ġand Ġlesbian Ġrights " Ġor ĠâĢ ľ hom osexual Ġrights ". ĠRespond ents Ġwho Ġread Ġthe Ġword ĠâĢ ľ hom osexual " Ġtended Ġto Ġshow Ġless Ġsupport Ġfor ĠLGBTQ Ġrights . ĠThis Ġeffect Ġwas Ġprimarily Ġdriven Ġby Ġhigh Ġauthor itar ians , Ġpeople Ġwho Ġshow Ġhigh Ġsensitivity Ġto Ġinter group Ġdistinctions . ĠThe Ġauthors Ġposit Ġthat Ġhomosexual Ġmakes Ġsocial Ġgroup Ġdistinctions Ġmore Ġblatant Ġthan Ġgay Ġor Ġlesbian , Ġwhich Ġthen Ġleads Ġto Ġa Ġgreater Ġdegree Ġof Ġpsychological Ġdist ancing , Ġthus Ġenabling Ġparticipants Ġto Ġfurther Ġremove ĠLGBTQ Ġpeople Ġfrom Ġtheir Ġrealm Ġof Ġmoral Ġconsideration ĠB IB REF 54 . ĠBased Ġon Ġprior Ġresearch Ġand Ġevolving Ġmedia Ġguidelines , Ġwe Ġexpect Ġour Ġcomputational Ġanalysis Ġto Ġshow Ġthat Ġhomosexual Ġoccurs Ġin Ġmore Ġdehuman izing Ġcontexts Ġthan Ġother ĠLGBTQ Ġlabels , Ġsuch Ġas Ġgay . </s> Oper ational izing ĠDe human ization </s> In ĠSection ĠSEC REF 1 , Ġwe Ġbriefly Ġdiscussed Ġmultiple Ġelements Ġof Ġdehuman ization Ġthat Ġhave Ġbeen Ġidentified Ġin Ġsocial Ġpsychology Ġliterature . ĠHere Ġwe Ġintroduce Ġand Ġquantify Ġlex ical Ġcorrelates Ġto Ġoperational ize Ġfour Ġof Ġthese Ġcomponents : Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġdenial Ġof Ġagency , Ġmoral Ġdisgust , Ġand Ġuse Ġof Ġnon - human Ġmetaphors Ġ( particularly Ġver min ). </s> Oper ational izing ĠDe human ization Ġ: :: ĠNegative ĠEvaluation Ġof Ġa ĠTarget ĠGroup </s> One Ġprominent Ġaspect Ġof Ġdehuman ization Ġis Ġextremely Ġnegative Ġevaluations Ġof Ġmembers Ġof Ġa Ġtarget Ġgroup ĠB IB REF 4 . ĠAttribution Ġof Ġnegative Ġcharacteristics Ġto Ġmembers Ġof Ġa Ġtarget Ġgroup Ġin Ġorder Ġto Ġexclude Ġthat Ġgroup Ġfrom ĠâĢ ľ the Ġrealm Ġof Ġacceptable Ġnorms Ġand Ġvalues " Ġis Ġspecifically Ġthe Ġkey Ġcomponent Ġof Ġdeleg it im ization , Ġa Ġprocess Ġof Ġmoral Ġexclusion Ġclosely Ġrelated Ġto Ġdehuman ization . ĠWe Ġhypothes ize Ġthat Ġthis Ġnegative Ġevaluation Ġof Ġa Ġtarget Ġgroup Ġcan Ġbe Ġrealized Ġby Ġwords Ġand Ġphrases Ġwhose Ġcon notations Ġhave Ġextremely Ġlow Ġval ence , Ġwhere Ġthe Ġval ence Ġof Ġa Ġword Ġrefers Ġto Ġthe Ġdimension Ġof Ġmeaning Ġcorresponding Ġto Ġpositive / negative Ġ( or Ġpleasure / dis ple asure ) ĠB IB REF 55 , ĠB IB REF 56 . ĠThus , Ġwe Ġpropose Ġseveral Ġval ence Ġlex icon - based Ġapproaches Ġto Ġmeasure Ġthis Ġcomponent : Ġparagraph - level Ġval ence Ġanalysis , ĠCon notation ĠFrames , Ġand Ġword Ġembed ding Ġneighbor Ġval ence . ĠEach Ġof Ġthese Ġtechniques Ġhas Ġdifferent Ġadvantages Ġand Ġdisadvantages Ġregarding Ġprecision Ġand Ġinterpret ability . </s> Oper ational izing ĠDe human ization Ġ: :: ĠNegative ĠEvaluation Ġof Ġa ĠTarget ĠGroup Ġ: :: ĠPar agraph - level ĠVal ence ĠAnalysis </s> As Ġwe Ġmentioned Ġabove , Ġone Ġimportant Ġdimension Ġof Ġaffect ive Ġmeaning Ġis Ġval ence , Ġwhich Ġcorresponds Ġto Ġan Ġindividual 's Ġevaluation Ġof Ġan Ġevent Ġor Ġconcept , Ġranging Ġfrom Ġnegative / un pleasant Ġto Ġpositive / pleasant ĠB IB REF 55 , ĠB IB REF 57 . ĠA Ġstraightforward Ġlex ical Ġapproach Ġto Ġquantify Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup Ġis Ġthus Ġto Ġmeasure Ġthe Ġaverage Ġval ence Ġover Ġall Ġwords Ġin Ġcorpor a Ġcontaining Ġdiscussions Ġof Ġthe Ġtarget Ġgroup . ĠWe Ġquantify Ġthis Ġby Ġusing Ġthe Ġval ence Ġdimension Ġfrom Ġthe ĠN RC ĠV AD Ġlex icon , Ġwhich Ġcontains Ġreal - valued Ġscores Ġranging Ġfrom Ġzero Ġto Ġone Ġfor Ġval ence , Ġarousal Ġand Ġdominance Ġfor Ġ20 , 000 ĠEnglish Ġwords , Ġwhere Ġa Ġscore Ġof Ġzero Ġis Ġthe Ġlowest Ġval ence Ġ( most Ġnegative Ġemotion ) Ġand Ġa Ġscore Ġof Ġone Ġis Ġthe Ġhighest Ġpossible Ġval ence Ġ( most Ġpositive Ġemotion ) ĠB IB REF 56 . ĠWords Ġwith Ġthe Ġhighest Ġval ence Ġin Ġthe ĠN RC ĠV AD ĠLex icon Ġinclude Ġlove , Ġhappy , Ġand Ġhappily , Ġwhile Ġwords Ġwith Ġthe Ġlowest Ġval ence Ġinclude Ġtoxic , Ġnightmare , Ġand Ġshit . </s> In Ġour Ġcase Ġstudy , Ġwe Ġqual itatively Ġfound Ġthat Ġparagraphs Ġwere Ġthe Ġoptimal Ġlevel Ġof Ġcontext Ġfor Ġanalysis ; Ġfull Ġarticles Ġoften Ġfocused Ġon Ġtopics Ġunrelated Ġto ĠLGBTQ Ġcommunities , Ġeven Ġif Ġan ĠLGBTQ Ġlabel Ġwas Ġmentioned Ġsomewhere Ġin Ġthe Ġarticle , Ġwhile Ġsingle Ġsentences Ġdid Ġnot Ġprovide Ġenough Ġdiscussion Ġto Ġget Ġa Ġsense Ġof Ġhow ĠLGBTQ Ġgroups Ġare Ġrepresented Ġby Ġthe Ġnewspaper . ĠThus , Ġwe Ġcalculate Ġparagraph - level Ġscores Ġby Ġtaking Ġthe Ġaverage Ġval ence Ġscore Ġover Ġall Ġwords Ġin Ġthe Ġparagraph Ġthat Ġappear Ġ( or Ġwhose Ġle mm as Ġappear ) Ġin Ġthe ĠN RC ĠV AD Ġlex icon . </s> Oper ational izing ĠDe human ization Ġ: :: ĠNegative ĠEvaluation Ġof Ġa ĠTarget ĠGroup Ġ: :: ĠCon notation ĠFrames </s> While Ġparagraph - level Ġval ence Ġanalysis Ġis Ġquick Ġand Ġsimple , Ġit Ġis Ġsometimes Ġtoo Ġcoarse Ġbecause Ġwe Ġaim Ġto Ġunderstand Ġthe Ġsentiment Ġdirected Ġtowards Ġthe Ġtarget Ġgroup , Ġnot Ġjust Ġnearby Ġin Ġthe Ġtext . ĠFor Ġexample , Ġsuppose Ġthe Ġtarget Ġgroup Ġis Ġnamed ĠâĢ ľ B ". ĠA Ġsentence Ġsuch Ġas ĠâĢ ľ A Ġviolently Ġattacked ĠB " Ġwould Ġlikely Ġhave Ġextremely Ġnegative Ġval ence , Ġbut Ġthe Ġwriter Ġmay Ġnot Ġfeel Ġnegatively Ġtowards Ġthe Ġvictim , ĠâĢ ľ B ". </s> We Ġaddress Ġthis Ġby Ġusing ĠB IB REF 22 's ĠCon notation ĠFrames ĠLex icon , Ġwhich Ġcontains Ġrich Ġannotations Ġfor Ġ900 ĠEnglish Ġverbs Ġ( BI B REF 22 ). ĠAmong Ġother Ġthings , Ġfor Ġeach Ġverb , Ġthe ĠCon notation ĠFrames ĠLex icon Ġprovides Ġscores Ġ( ranging Ġfrom Ġ- 0 . 87 Ġto Ġ0 . 8 ) Ġfor Ġthe Ġwriter 's Ġperspective Ġtowards Ġthe Ġverb 's Ġsubject Ġand Ġobject . ĠIn Ġthe Ġexample Ġabove Ġfor Ġthe Ġverb Ġattack , Ġthe Ġlex icon Ġlists Ġthe Ġwriter 's Ġperspective Ġtowards Ġthe Ġsubject ĠâĢ ľ A ", Ġthe Ġattacker , Ġas Ġ- 0 . 6 Ġ( strong ly Ġnegative ) Ġand Ġthe Ġobject ĠâĢ ľ B " Ġas Ġ0 . 23 Ġ( weak ly Ġpositive ). </s> We Ġextract Ġall Ġsubject - verb - object Ġtu ples Ġcontaining Ġat Ġleast Ġone Ġtarget Ġgroup Ġlabel Ġusing Ġthe ĠSp acy Ġdependency Ġparser Ġ. ĠFor Ġeach Ġsubject Ġand Ġobject , Ġwe Ġcapture Ġthe Ġnoun Ġand Ġthe Ġmodifying Ġadject ives , Ġas Ġgroup Ġlabels Ġ( such Ġas Ġgay ) Ġcan Ġoften Ġtake Ġeither Ġnominal Ġor Ġadject ival Ġforms . ĠFor Ġeach Ġtuple , Ġwe Ġuse Ġthe Ġcon notation Ġframe Ġlex icon Ġto Ġdetermine Ġthe Ġwriter 's Ġperspective Ġeither Ġtowards Ġthe Ġsubject Ġif Ġthe Ġgroup Ġlabel Ġappears Ġin Ġthe Ġsubject Ġnoun Ġphrase , Ġor Ġperspective Ġtowards Ġthe Ġobject Ġif Ġthe Ġlabel Ġappears Ġin Ġthe Ġobject Ġnoun Ġphrase . ĠWe Ġthen Ġaverage Ġperspective Ġscores Ġover Ġall Ġtu ples . </s> Oper ational izing ĠDe human ization Ġ: :: ĠNegative ĠEvaluation Ġof Ġa ĠTarget ĠGroup Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠVal ence </s> While Ġa ĠCon notation ĠFrames Ġapproach Ġcan Ġbe Ġmore Ġprecise Ġthan Ġword - count ing Ġval ence Ġanalysis , Ġit Ġlimits Ġus Ġto Ġanalyzing ĠSV O Ġtri ples , Ġwhich Ġexcludes Ġa Ġlarge Ġportion Ġof Ġthe Ġavailable Ġdata Ġabout Ġthe Ġtarget Ġgroups . ĠThis Ġreveals Ġa Ġcon undrum : Ġbroader Ġcontext Ġcan Ġprovide Ġvaluable Ġinsights Ġinto Ġthe Ġimplicit Ġevaluations Ġof Ġa Ġsocial Ġgroup , Ġbut Ġwe Ġalso Ġwant Ġto Ġdirectly Ġprobe Ġattitudes Ġtowards Ġthe Ġgroup Ġitself . </s> We Ġaddress Ġthis Ġtension Ġby Ġtraining Ġvector Ġspace Ġmodels Ġto Ġrepresent Ġthe Ġdata , Ġin Ġwhich Ġeach Ġunique Ġword Ġin Ġa Ġlarge Ġcorpus Ġis Ġrepresented Ġby Ġa Ġvector Ġ( embed ding ) Ġin Ġhigh - dimensional Ġspace . ĠThe Ġgeometry Ġof Ġthe Ġresulting Ġvector Ġspace Ġcaptures Ġmany Ġsemantic Ġrelations Ġbetween Ġwords . ĠFurthermore , Ġprior Ġwork Ġhas Ġshown Ġthat Ġvector Ġspace Ġmodels Ġtrained Ġon Ġcorpor a Ġfrom Ġdifferent Ġtime Ġperiods Ġcan Ġcapture Ġsemantic Ġchange ĠB IB REF 58 , ĠB IB REF 59 . ĠFor Ġexample , Ġdi ach ronic Ġword Ġembed d ings Ġreveal Ġthat Ġthe Ġword Ġgay Ġmeant ĠâĢ ľ che er ful " Ġor ĠâĢ ľ d apper " Ġin Ġthe Ġearly Ġ20 th Ġcentury , Ġbut Ġshifted Ġto Ġits Ġcurrent Ġmeaning Ġof Ġsexual Ġorientation Ġby Ġthe Ġ1970 s . ĠBecause Ġword Ġembed d ings Ġare Ġcreated Ġfrom Ġreal - world Ġdata , Ġthey Ġcontain Ġreal - world Ġbiases . ĠFor Ġexample , ĠB IB REF 37 Ġdemonstrated Ġthat Ġgender Ġstereotypes Ġare Ġdeeply Ġingrained Ġin Ġthese Ġsystems . ĠThough Ġproblematic Ġfor Ġthe Ġwidespread Ġuse Ġof Ġthese Ġmodels Ġin Ġcomputational Ġsystems , Ġthese Ġrevealed Ġbiases Ġindicate Ġthat Ġword Ġembed d ings Ġcan Ġactually Ġbe Ġused Ġto Ġidentify Ġstereotypes Ġabout Ġsocial Ġgroups Ġand Ġunderstand Ġhow Ġthey Ġchange Ġover Ġtime ĠB IB REF 24 . </s> This Ġtechnique Ġcan Ġsimilarly Ġbe Ġapplied Ġto Ġunderstand Ġhow Ġa Ġsocial Ġgroup Ġis Ġnegatively Ġevaluated Ġwithin Ġa Ġlarge Ġtext Ġcorpus . ĠIf Ġthe Ġvector Ġcorresponding Ġto Ġa Ġsocial Ġgroup Ġlabel Ġis Ġlocated Ġin Ġthe Ġsemantic Ġembed ding Ġspace Ġnear Ġnegative Ġwords Ġor Ġterms Ġwith Ġclearly Ġnegative Ġevaluations , Ġthat Ġgroup Ġis Ġlikely Ġnegatively Ġevaluated Ġ( and Ġpossibly Ġdehuman ized ) Ġin Ġthe Ġtext . ĠThese Ġvector Ġspace Ġmodels Ġcan Ġbe Ġused Ġin Ġa Ġdi ach ronic Ġstudy Ġto Ġcapture Ġsubtle Ġchanges Ġin Ġthe Ġcon notations Ġof Ġa Ġgroup Ġlabel Ġover Ġtime , Ġeven Ġwhen Ġthe Ġden ot ational Ġmeaning Ġof Ġthe Ġlabel Ġhas Ġremained Ġconstant . ĠFurthermore , Ġcomparing Ġdifferences Ġin Ġwhat Ġwords Ġare Ġmost Ġclosely Ġassociated Ġwith Ġrelated Ġgroup Ġlabels Ġcan Ġreveal Ġmeaning Ġvariation Ġbetween Ġthese Ġlabels , Ġeven Ġif Ġthey Ġrefer Ġto Ġsimilar Ġpopulations . </s> We Ġfirst Ġpre process Ġthe Ġdata Ġby Ġlower c asing , Ġremoving Ġnumbers , Ġand Ġremoving Ġpunct uation . ĠWe Ġthen Ġuse Ġthe Ġword 2 vec Ġskip - gram Ġmodel Ġto Ġcreate Ġword Ġembed d ings ĠB IB REF 60 . ĠThe Ġresulting Ġvector Ġspaces Ġhave Ġ100 Ġdimensions , Ġand Ġwe Ġspecify Ġthe Ġfollowing Ġadditional Ġparameters Ġto Ġthe Ġmodel : Ġcontext Ġwindow Ġof Ġsize Ġ10 , Ġminimum Ġfrequency Ġof Ġ5 Ġfor Ġa Ġword Ġto Ġbe Ġincluded , Ġ10 Ġnegative Ġsamples , Ġ10 Ġiterations , Ġand Ġa Ġsampling Ġrate Ġof Ġ$ 10 ^{ - 4 } $. ĠWe Ġthen Ġuse Ġcos ine Ġsimilarity Ġto Ġdetermine Ġa Ġgroup Ġlabel 's Ġnearest Ġneighbors Ġin Ġthe Ġvector Ġspace . ĠFor Ġour Ġdi ach ronic Ġanalysis , Ġwe Ġfirst Ġtrain Ġword 2 vec Ġon Ġthe Ġentire Ġcorpus , Ġand Ġthen Ġuse Ġthe Ġresulting Ġvectors Ġto Ġinitialize Ġword 2 vec Ġmodels Ġfor Ġeach Ġyear Ġof Ġdata Ġin Ġorder Ġto Ġencourage Ġco herence Ġand Ġstability Ġacross Ġyears . ĠAfter Ġtraining Ġword 2 vec , Ġwe Ġzero - center Ġand Ġnormal ize Ġall Ġembed d ings Ġto Ġalleviate Ġthe Ġhub ness Ġproblem ĠB IB REF 61 . </s> We Ġthen Ġidentify Ġvectors Ġfor Ġgroup Ġlabels Ġby Ġtaking Ġthe Ġcent roid Ġof Ġall Ġmorph ological Ġforms Ġof Ġthe Ġlabel , Ġweighted Ġby Ġfrequency . ĠFor Ġexample , Ġthe Ġvector Ġrepresentation Ġfor Ġthe Ġlabel Ġgay Ġis Ġactually Ġthe Ġweighted Ġcent roid Ġof Ġthe Ġwords Ġgay Ġand Ġgays . ĠThis Ġenables Ġus Ġto Ġsimultaneously Ġaccount Ġfor Ġadject ival , Ġsingular Ġnominal , Ġand Ġplural Ġnominal Ġforms Ġfor Ġeach Ġsocial Ġgroup Ġlabel Ġwith Ġa Ġsingle Ġvector . ĠFinally , Ġwe Ġestimate Ġthe Ġval ence Ġfor Ġeach Ġgroup Ġlabel Ġby Ġidentifying Ġits Ġ1000 Ġnearest Ġneighbors Ġvia Ġcos ine Ġsimilarity Ġ( 1000 Ġwas Ġchosen Ġarbitrarily ), Ġand Ġcalculating Ġthe Ġaverage Ġval ence Ġof Ġall Ġneighbors Ġthat Ġappear Ġin Ġthe ĠN RC ĠV AD Ġval ence Ġlex icon . </s> We Ġalso Ġtried Ġusing Ġa Ġgroup Ġlabel 's Ġvector Ġrepresentation Ġto Ġinduce Ġa Ġval ence Ġscore Ġdirectly , Ġinstead Ġof Ġusing Ġnearest Ġneighbors Ġas Ġa Ġproxy , Ġby Ġadapting Ġthe Ġregression - based Ġsentiment Ġprediction Ġfrom ĠB IB REF 62 Ġfor Ġword Ġembed d ings . ĠWe Ġfound Ġthat Ġthis Ġapproach Ġyielded Ġsimilar Ġresults Ġas Ġanalyzing Ġnearest Ġneighbor Ġval ence Ġbut Ġwas Ġdifficult Ġto Ġqual itatively Ġinterpret . ĠMore Ġdetails Ġfor Ġand Ġresults Ġfrom Ġthis Ġtechnique Ġcan Ġbe Ġfound Ġin Ġthe ĠAppendix . </s> Oper ational izing ĠDe human ization Ġ: :: ĠDen ial Ġof ĠAgency </s> Den ial Ġof Ġagency Ġrefers Ġto Ġthe Ġlack Ġof Ġattribut ing Ġa Ġtarget Ġgroup Ġmember Ġwith Ġthe Ġability Ġto Ġcontrol Ġtheir Ġown Ġactions Ġor Ġdecisions ĠB IB REF 16 . ĠAutom atically Ġdetecting Ġthe Ġextent Ġto Ġwhich Ġa Ġwriter Ġattributes Ġcognitive Ġabilities Ġto Ġa Ġtarget Ġgroup Ġmember Ġis Ġan Ġextraordinarily Ġchallenging Ġcomputational Ġtask . ĠFortunately , Ġthe Ġsame Ġlex icons Ġused Ġto Ġoperational ize Ġnegative Ġevaluations Ġprovide Ġresources Ġfor Ġmeasuring Ġlex ical Ġsignals Ġof Ġdenial Ġof Ġagency . </s> Oper ational izing ĠDe human ization Ġ: :: ĠDen ial Ġof ĠAgency Ġ: :: ĠCon notation ĠFrames </s> As Ġin ĠSection ĠSEC REF 6 , Ġwe Ġuse ĠCon notation ĠFrames Ġto Ġquantify Ġthe Ġamount Ġof Ġagency Ġattributed Ġto Ġa Ġtarget Ġgroup . ĠWe Ġuse ĠB IB REF 25 's Ġextension Ġof ĠCon notation ĠFrames Ġfor Ġagency ĠB IB REF 25 . ĠFollowing ĠB IB REF 25 's Ġinterpretation , Ġentities Ġwith Ġhigh Ġagency Ġexert Ġa Ġhigh Ġdegree Ġof Ġcontrol Ġover Ġtheir Ġown Ġdecisions Ġand Ġare Ġactive Ġdecision - makers , Ġwhile Ġentities Ġwith Ġlow Ġagency Ġare Ġmore Ġpassive ĠB IB REF 25 . ĠThis Ġcontrast Ġis Ġparticularly Ġapparent Ġin Ġexample Ġsentences Ġsuch Ġas ĠX Ġsearched Ġfor ĠY Ġand ĠX Ġwaited Ġfor ĠY , Ġwhere Ġthe Ġverb Ġsearched Ġgives ĠX Ġhigh Ġagency Ġand Ġwaited Ġgives ĠX Ġlow Ġagency ĠB IB REF 25 . ĠAdditionally , ĠB IB REF 25 's Ġreleased Ġlex icon Ġfor Ġagency Ġindicates Ġthat Ġthe Ġsubjects Ġof Ġverbs Ġsuch Ġas Ġattack Ġand Ġpraise Ġare Ġgiven Ġhigh Ġagency , Ġwhile Ġthe Ġsubjects Ġof Ġdoubts , Ġneeds , Ġand Ġowes Ġare Ġgiven Ġlow Ġagency ĠB IB REF 25 . </s> This Ġlex icon Ġconsiders Ġthe Ġagency Ġattributed Ġto Ġsubjects Ġof Ġnearly Ġ2000 Ġtrans itive Ġand Ġintr ans itive Ġverbs . ĠTo Ġuse Ġthis Ġlex icon Ġto Ġquantify Ġdenial Ġof Ġagency Ġin Ġour Ġcorpus , Ġwe Ġextract Ġall Ġsentences ' Ġhead Ġverbs Ġand Ġtheir Ġcorresponding Ġsubjects , Ġwhere Ġthe Ġsubject Ġnoun Ġphrase Ġcontains Ġa Ġtarget Ġgroup Ġlabel . ĠUnlike ĠB IB REF 22 's Ġreal - valued ĠCon notation ĠFrames Ġlex icon Ġfor Ġperspective , Ġthe Ġagency Ġlex icon Ġonly Ġprovides Ġbinary Ġlabels , Ġso Ġwe Ġcalculate Ġthe Ġfraction Ġof Ġsubject - verb Ġpairs Ġwhere Ġa Ġsubject Ġcontaining Ġa Ġgroup Ġlabel Ġwas Ġgiven Ġhigh Ġagency Ġby Ġits Ġhead Ġverb . </s> Oper ational izing ĠDe human ization Ġ: :: ĠDen ial Ġof ĠAgency Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠDomin ance </s> The ĠN RC ĠV AD Ġlex icon 's Ġdominance Ġannotations Ġprovide Ġanother Ġresource Ġfor Ġquant ifying Ġdehuman ization ĠB IB REF 56 . ĠAs Ġwith Ġval ence , Ġthe ĠN RC ĠV AD Ġlex icon 's Ġdominance Ġdimension Ġcontains Ġreal - valued Ġscores Ġbetween Ġzero Ġand Ġone Ġfor Ġ20 , 000 ĠEnglish Ġwords . ĠHowever , Ġit Ġis Ġimportant Ġto Ġnote Ġthat Ġthe Ġdominance Ġlex icon Ġprimarily Ġcaptures Ġpower , Ġwhich Ġis Ġdistinct Ġfrom Ġbut Ġclosely Ġrelated Ġto Ġagency . ĠWhile Ġpower Ġrefers Ġto Ġone 's Ġcontrol Ġover Ġothers , Ġagency Ġrefers Ġto Ġone 's Ġcontrol Ġover Ġtheir Ġown Ġself . ĠWhile Ġthis Ġlex icon Ġis Ġa Ġproxy , Ġit Ġqual itatively Ġappears Ġto Ġcapture Ġsignals Ġof Ġdenial Ġof Ġagency ; Ġthe Ġhighest Ġdominance Ġwords Ġare Ġpowerful , Ġleadership , Ġsuccess , Ġand Ġgovern , Ġwhile Ġthe Ġlowest Ġdominance Ġwords Ġare Ġweak , Ġfrail , Ġempty , Ġand Ġpenn iless . ĠWe Ġthus Ġtake Ġthe Ġsame Ġapproach Ġas Ġin ĠSection ĠSEC REF 10 Ġwith Ġthe Ġsame Ġvector Ġspace Ġmodels . ĠHowever , Ġwe Ġnow Ġcalculate Ġthe Ġaverage Ġdominance Ġscore Ġof Ġthe Ġ1000 Ġnearest Ġneighbors Ġto Ġeach Ġgroup Ġlabel Ġvector Ġrepresentation . </s> As Ġin ĠSection ĠSEC REF 10 , Ġwe Ġalso Ġinduced Ġa Ġdominance Ġscore Ġdirectly Ġfrom Ġa Ġgroup Ġlabel 's Ġvector Ġrepresentation Ġby Ġadapting Ġthe Ġregression - based Ġsentiment Ġprediction Ġfrom ĠB IB REF 62 Ġfor Ġword Ġembed d ings . ĠMore Ġdetails Ġand Ġresults Ġfor Ġthis Ġtechnique Ġcan Ġbe Ġfound Ġin Ġthe ĠAppendix . </s> Oper ational izing ĠDe human ization Ġ: :: ĠMoral ĠDis g ust </s> In Ġorder Ġto Ġoperational ize Ġthe Ġmoral Ġdisgust Ġcomponent Ġof Ġdehuman ization Ġwith Ġlex ical Ġtechniques , Ġwe Ġdraw Ġinspiration Ġfrom ĠMoral ĠFound ations Ġtheory , Ġwhich Ġpost ulates Ġthat Ġthere Ġare Ġfive Ġdimensions Ġof Ġmoral Ġintu itions : Ġcare , Ġfairness / pro portion ality , Ġloyalty / ing roup , Ġauthority / respect , Ġand Ġsanct ity / p urity ĠB IB REF 63 . ĠThe Ġnegative Ġend Ġof Ġthe Ġsanct ity / p urity Ġdimension Ġcorresponds Ġto Ġmoral Ġdisgust . ĠWhile Ġwe Ġdo Ġnot Ġdirectly Ġincorporate ĠMoral ĠFound ations ĠTheory Ġin Ġour Ġframework Ġfor Ġdehuman ization , Ġwe Ġutilize Ġlex icons Ġcreated Ġby ĠB IB REF 64 Ġcorresponding Ġto Ġeach Ġmoral Ġfoundation . ĠThe Ġdictionary Ġfor Ġmoral Ġdisgust Ġincludes Ġover Ġthirty Ġwords Ġand Ġstems , Ġincluding Ġdisgust *, Ġsin , Ġper vert , Ġand Ġobsc en * Ġ( the Ġaster isks Ġindicate Ġthat Ġthe Ġdictionary Ġincludes Ġall Ġwords Ġcontaining Ġthe Ġpreceding Ġprefix , Ġsuch Ġas Ġboth Ġobscene Ġand Ġobsc en ity ). </s> We Ġopt Ġfor Ġa Ġvector Ġapproach Ġinstead Ġof Ġcounting Ġraw Ġfrequencies Ġof Ġmoral Ġdisgust - related Ġwords Ġbecause Ġwords Ġthat Ġexplicitly Ġinvoke Ġmoral Ġdisgust Ġare Ġvery Ġsparse Ġin Ġour Ġnews Ġcorpus . ĠFurthermore , Ġvectors Ġare Ġcapable Ġof Ġcapturing Ġassociations Ġwith Ġthe Ġsocial Ġgroup Ġlabel Ġitself , Ġwhile Ġcounts Ġin Ġparagraphs Ġand Ġfull Ġnews Ġarticles Ġwould Ġnot Ġdirectly Ġcapture Ġsuch Ġassociations . ĠUsing Ġthe Ġword Ġembed d ings Ġfrom ĠSection ĠSEC REF 10 , Ġwe Ġconstruct Ġa Ġvector Ġto Ġrepresent Ġthe Ġconcept Ġof Ġmoral Ġdisgust , Ġwhich Ġis Ġthe Ġaverage Ġof Ġthe Ġcorresponding Ġvectors Ġfor Ġall Ġwords Ġin Ġthe ĠâĢ ľ M oral ĠDis g ust " Ġdictionary , Ġweighted Ġby Ġword Ġfrequency . ĠThis Ġmethod Ġof Ġcreating Ġa Ġvector Ġfrom Ġthe ĠMoral ĠFound ations Ġdictionary Ġresembles Ġthat Ġused Ġby ĠB IB REF 65 . ĠWe Ġcan Ġthen Ġanalyze Ġimplicit Ġassociations Ġbetween Ġa Ġsocial Ġgroup Ġand Ġmoral Ġdisgust Ġby Ġcalculating Ġthe Ġcos ine Ġsimilarity Ġbetween Ġthe Ġgroup Ġlabel 's Ġvector Ġand Ġthe ĠMoral ĠDis g ust Ġconcept Ġvector , Ġwhere Ġa Ġhigher Ġsimilarity Ġsuggests Ġcloser Ġassociations Ġbetween Ġthe Ġsocial Ġgroup Ġand Ġmoral Ġdisgust . ĠAs Ġpreviously , Ġeach Ġgroup Ġlabel 's Ġvector Ġis Ġthe Ġaverage Ġof Ġits Ġmorph ological Ġvariants ' Ġvectors , Ġweighted Ġby Ġfrequency . </s> Oper ational izing ĠDe human ization Ġ: :: ĠVer min Ġas Ġa ĠDe human izing ĠMet aph or </s> Met aph ors Ġcomparing Ġhumans Ġto Ġver min Ġhave Ġbeen Ġespecially Ġprominent Ġin Ġdehuman izing Ġgroups Ġthroughout Ġhistory ĠB IB REF 4 , ĠB IB REF 15 . ĠEven Ġif Ġa Ġmarginalized Ġsocial Ġgroup Ġis Ġnot Ġdirectly Ġequ ated Ġto Ġver min Ġin Ġthe Ġpress , Ġthis Ġmetaphor Ġmay Ġbe Ġinvoked Ġin Ġmore Ġsubtle Ġways , Ġsuch Ġas Ġthrough Ġthe Ġuse Ġof Ġverbs Ġthat Ġare Ġalso Ġassociated Ġwith Ġver min Ġ( like Ġsc urry Ġas Ġopposed Ġto Ġthe Ġmore Ġneutral Ġhurry ) ĠB IB REF 66 . ĠWhile Ġthere Ġis Ġsome Ġnatural Ġlanguage Ġprocessing Ġwork Ġon Ġthe Ġcomplex Ġtask Ġof Ġmetaphor Ġdetection Ġ( e . g . ĠB IB REF 67 , ĠB IB REF 67 ), Ġsuch Ġsystems Ġwould Ġnot Ġbe Ġable Ġto Ġeasily Ġquantify Ġsuch Ġindirect Ġassociations . </s> We Ġthus Ġquantify Ġthe Ġmetaph orical Ġrelationship Ġbetween Ġa Ġsocial Ġgroup Ġand Ġver min Ġby Ġcalculating Ġsimilarities Ġbetween Ġthese Ġconcepts Ġin Ġa Ġdistribution al Ġsemantic Ġvector Ġspace , Ġas Ġwe Ġdid Ġwith Ġmoral Ġdisgust Ġin ĠSection ĠSEC REF 14 . ĠWe Ġcreate Ġa ĠVer min Ġconcept Ġvector Ġby Ġcalculating Ġthe Ġaverage Ġof Ġthe Ġfollowing Ġver min Ġwords ' Ġvectors , Ġweighted Ġby Ġfrequency : Ġver min , Ġrodent ( s ), Ġrat ( s ) Ġmice , Ġcock ro aches , Ġterm ite ( s ), Ġbed bug ( s ), Ġfle as Ġ. ĠWe Ġdo Ġnot Ġinclude Ġthe Ġsingular Ġmouse Ġor Ġfle a Ġbecause Ġnon - ver min Ġsenses Ġof Ġthose Ġwords Ġwere Ġmore Ġfrequent Ġ( computer Ġmouse Ġand Ġfle a Ġmarket , Ġrespectively ), Ġand Ġword 2 vec Ġdoes Ġnot Ġaccount Ġfor Ġpoly se my . ĠWe Ġcalculate Ġcos ine Ġsimilarity Ġbetween Ġeach Ġsocial Ġgroup Ġlabel Ġand Ġthe ĠVer min Ġconcept Ġvector . ĠA Ġlarge Ġcos ine Ġsimilarity Ġbetween Ġa Ġlabel Ġand Ġthe ĠVer min Ġvector Ġsuggests Ġthat Ġthe Ġsocial Ġgroup Ġis Ġclosely Ġassociated Ġwith Ġver min . </s> Table ĠT AB REF 18 Ġprovides Ġan Ġoverview Ġof Ġthe Ġfour Ġelements Ġof Ġdehuman ization Ġthat Ġwe Ġstudy Ġand Ġthe Ġlex ical Ġtechniques Ġused Ġto Ġquantify Ġthem . </s> Data </s> The Ġdata Ġfor Ġour Ġcase Ġstudy Ġspans Ġover Ġthirty Ġyears Ġof Ġarticles Ġfrom Ġthe ĠNew ĠYork ĠTimes , Ġfrom ĠJanuary Ġ1986 Ġto ĠDecember Ġ2015 , Ġand Ġwas Ġoriginally Ġcollected Ġby ĠB IB REF 68 ĠB IB REF 68 . ĠThe Ġarticles Ġcome Ġfrom Ġall Ġsections Ġof Ġthe Ġnewspaper , Ġsuch Ġas ĠâĢ ľ World ", ĠâĢ ľ New ĠYork Ġ& ĠRegion ", ĠâĢ ľ O pin ion ", ĠâĢ ľ Style ", Ġand ĠâĢ ľ Sports ". ĠOur Ġdistribution al Ġsemantic Ġmethods Ġrely Ġon Ġall Ġof Ġthe Ġavailable Ġdata Ġin Ġorder Ġto Ġobtain Ġthe Ġmost Ġfine - gr ained Ġunderstanding Ġof Ġthe Ġrelationships Ġbetween Ġwords Ġpossible . ĠFor Ġthe Ġother Ġtechniques , Ġwe Ġextract Ġparagraphs Ġcontaining Ġany Ġword Ġfrom Ġa Ġpredetermined Ġlist Ġof ĠLG TB Q Ġterms Ġ( shown Ġin ĠTable ĠT AB REF 19 ). </s> Each Ġacronym Ġlabel Ġwas Ġmatched Ġinsensitive Ġto Ġcase Ġand Ġpunct uation . ĠSome Ġcurrently Ġprominent ĠLGBTQ Ġterms , Ġsuch Ġas Ġqueer Ġand Ġtrans Ġwere Ġnot Ġincluded Ġin Ġthis Ġstudy , Ġas Ġother Ġsenses Ġof Ġthese Ġwords Ġwere Ġconsiderably Ġmore Ġfrequent Ġin Ġearlier Ġyears . ĠWe Ġfurther Ġfiltered Ġout Ġparagraphs Ġfrom Ġsections Ġthat Ġtypically Ġdid Ġnot Ġper tain Ġto Ġnews , Ġsuch Ġas ĠâĢ ľ Ar ts ", ĠâĢ ľ The ater ", Ġand ĠâĢ ľ M ovies ". ĠWhile Ġthese Ġsections Ġcould Ġprovide Ġvaluable Ġinformation , Ġwe Ġdecided Ġto Ġfocus Ġon Ġrepresentation Ġof ĠLGBTQ Ġgroups Ġin Ġmore Ġnews - related Ġcontexts . ĠFurthermore , Ġdue Ġto Ġthe Ġoriginal Ġnewspaper Ġlayout Ġand Ġsubsequent Ġdigital Ġprocessing , ĠArts Ġsections Ġoften Ġcontained Ġextremely Ġlong Ġparagraphs Ġcontaining Ġnumerous Ġlistings Ġand Ġschedules . ĠRem oving Ġthese Ġsections Ġsubstantially Ġsimplified Ġdata Ġprocessing . </s> A Ġchallenging Ġquestion Ġwhen Ġanalyzing Ġmass Ġmedia Ġfor Ġsubjective Ġattitudes Ġis Ġdeciding Ġwhose Ġperspective Ġwe Ġwant Ġto Ġcapture : Ġan Ġindividual Ġreporter , Ġthe Ġinstitution , Ġor Ġsociety Ġat Ġlarge ? ĠIn Ġthis Ġcase Ġstudy , Ġwe Ġaim Ġto Ġidentify Ġthe Ġinstitution 's Ġdehuman izing Ġattitudes Ġtowards ĠLGBTQ Ġpeople . ĠWe Ġrepresent Ġthe ĠNew ĠYork ĠTimes Ġinstitution Ġas Ġa Ġcombination Ġof Ġthe Ġjournalists ' Ġwords Ġin Ġnews Ġarticles , Ġdirect Ġquotes , Ġparaph r ases Ġfrom Ġinterviews , Ġand Ġpublished Ġopinion Ġarticles . ĠTherefore , Ġdespite Ġour Ġnews Ġfocus , Ġwe Ġinclude Ġdata Ġfrom ĠâĢ ľ O pin ion " Ġsections ; Ġwhile Ġopinion Ġarticles Ġare Ġstyl istically Ġdifferent Ġfrom Ġtraditional Ġjournalistic Ġreporting Ġdue Ġto Ġmore Ġovert Ġbiases Ġand Ġarguments , Ġthese Ġarticles Ġare Ġimportant Ġin Ġconstructing Ġthe Ġinstitution 's Ġperspective . ĠIn Ġaddition , Ġwe Ġconsider Ġall Ġtext Ġin Ġeach Ġrelevant Ġparagraph , Ġincluding Ġquotes Ġand Ġparaph r ases , Ġbecause Ġthey Ġare Ġimportant Ġto Ġa Ġnewspaper 's Ġframing Ġof Ġan Ġissue , Ġas Ġparticular Ġquotes Ġrepresenting Ġspecific Ġstances Ġare Ġintentionally Ġincluded Ġor Ġexcluded Ġfrom Ġany Ġgiven Ġarticle ĠB IB REF 69 . ĠHowever , Ġthis Ġapproach Ġis Ġsometimes Ġproblematic Ġwhen Ġthe Ġnewspaper Ġquotes Ġclearly Ġextreme Ġpositions Ġthat Ġthey Ġare Ġunlikely Ġto Ġagree Ġwith , Ġand Ġwe Ġdiscuss Ġthis Ġissue Ġthroughout Ġour Ġqualitative Ġanalysis . </s> We Ġrefer Ġto Ġthe Ġremaining Ġsubset Ġof Ġthe ĠNew ĠYork ĠTimes Ġdata Ġafter Ġfiltering Ġas Ġthe ĠLGBTQ Ġcorpus . ĠThe ĠLGBTQ Ġcorpus Ġconsists Ġof Ġ93 , 9 77 Ġparagraphs Ġand Ġ7 . 36 Ġmillion Ġtokens . ĠHowever , Ġa Ġlarge Ġincrease Ġin Ġreporting Ġon ĠLGBTQ - related Ġissues Ġhas Ġled Ġto Ġa Ġhighly Ġskewed Ġdistribution Ġin Ġthe Ġamount Ġof Ġdata Ġover Ġyears , Ġwith Ġ1986 Ġcontaining Ġthe Ġleast Ġdata Ġ( 11 44 Ġparagraphs Ġand Ġ73 , 549 Ġtokens ) Ġand Ġ2012 Ġcontaining Ġthe Ġmost Ġ( 59 24 Ġparagraphs Ġand Ġ465 , 254 Ġtokens ). </s> For Ġall Ġexperiments , Ġwe Ġalso Ġinclude Ġresults Ġfor Ġthe Ġterms ĠAmerican Ġand ĠAmericans . ĠOur Ġprimary Ġmotivation Ġfor Ġincluding ĠAmerican ( s ) Ġis Ġto Ġcontrast Ġchanges Ġin ĠLGBTQ Ġlabels ' Ġrepresentation Ġin Ġthe ĠNew ĠYork ĠTimes Ġwith Ġanother Ġsocial Ġgroup Ġlabel Ġthat Ġappears Ġin Ġsufficiently Ġdifferent Ġcontexts . ĠThis Ġensures Ġthat Ġthe Ġchanges Ġwe Ġfind Ġin Ġdehuman izing Ġlanguage Ġtowards ĠLGBTQ Ġgroups Ġdo Ġnot Ġapply Ġuniformly Ġto Ġall Ġsocial Ġgroups , Ġand Ġare Ġthus Ġnot Ġmerely Ġan Ġartifact Ġof Ġthe Ġpublication 's Ġoverall Ġlanguage Ġchange . ĠWhile Ġa Ġnatural ĠâĢ ľ control " Ġvariable Ġwould Ġbe Ġlabels Ġsuch Ġas Ġstraight Ġor Ġheterosexual , Ġwe Ġfound Ġthat Ġthese Ġterms Ġonly Ġoccurred Ġwithin Ġdiscussions Ġof ĠLGBTQ Ġcommunities , Ġlikely Ġbecause Ġthey Ġname Ġsocially Ġunmarked Ġcategories . ĠWe Ġalso Ġconsidered Ġcomparing ĠLGBTQ Ġlabels Ġto Ġperson / people , Ġbut Ġwe Ġfound Ġthat Ġthe Ġword Ġembed ding - based Ġexperiments Ġwere Ġparticularly Ġsensitive Ġto Ġsynt actic Ġforms , Ġso Ġwe Ġselected Ġa Ġword Ġthat Ġbehaves Ġmore Ġsimilarly Ġsynt act ically Ġto Ġgay Ġand Ġhomosexual , Ġparticularly Ġby Ġhaving Ġboth Ġnominal Ġand Ġadject ival Ġuses . ĠNevertheless , Ġit Ġis Ġimportant Ġto Ġnote Ġthat ĠAmerican ( s ) Ġis Ġby Ġno Ġmeans Ġa Ġneutral Ġcontrol Ġvariable . ĠBecause Ġof Ġits Ġin - group Ġstatus Ġfor Ġthe ĠNew ĠYork ĠTimes Ġ( a ĠU . S . Ġinstitution ), Ġwe Ġexpect Ġour Ġmeasurements Ġto Ġshow Ġthat ĠAmerican ( s ) Ġappears Ġin Ġmore Ġhuman izing Ġcontexts Ġthan ĠLGBTQ Ġgroup Ġlabels ; Ġhowever , Ġwe Ġdo Ġnot Ġexpect Ġto Ġfind Ġsubstantial Ġchanges Ġin Ġthe Ġuse Ġof ĠAmerican ( s ) Ġover Ġtime . </s> Figure ĠFIG REF 20 Ġshows Ġthe Ġcounts Ġof Ġgroup Ġlabels Ġfor Ġeach Ġyear Ġin Ġthe ĠNew ĠYork ĠTimes Ġfrom Ġ1986 Ġto Ġ2015 . ĠFor Ġvisualization Ġpurposes , Ġonly Ġwords Ġwith Ġa Ġtotal Ġcount Ġgreater Ġthan Ġ1000 Ġare Ġshown . ĠNotably , Ġthe Ġrelative Ġfrequency Ġof Ġhomosexual Ġdecreased Ġsubstantially Ġover Ġtime , Ġwhile Ġgay , Ġlesbian , Ġand Ġbisexual Ġare Ġmore Ġfrequent Ġin Ġlater Ġyears . ĠWe Ġalso Ġsee Ġthe Ġemergence Ġof Ġthe Ġterms Ġl g bt Ġand Ġtransgender Ġafter Ġ2000 . ĠAgg reg ated Ġcounts Ġover Ġall Ġyears Ġfor Ġeach ĠLGBTQ Ġlabel Ġcan Ġbe Ġfound Ġin Ġthe ĠAppendix . </s> Results Ġ: :: ĠWord ĠEmb edd ings </s> Using Ġall Ġof Ġthe ĠNew ĠYork ĠTimes Ġdata Ġavailable , Ġwe Ġcreated Ġvector Ġspace Ġmodels Ġfor Ġeach Ġyear Ġusing Ġthe Ġmethods Ġdescribed Ġin ĠSection ĠSEC REF 10 . ĠTable ĠT AB REF 22 Ġshows Ġthe Ġten Ġnearest Ġneighboring Ġwords Ġ( by Ġcos ine Ġsimilarity ) Ġto Ġour Ġvector Ġrepresentation Ġof Ġall ĠLGBTQ Ġterms , Ġwhich Ġis Ġthe Ġweighted Ġaverage Ġof Ġthe Ġembed d ings Ġof Ġthe ĠLGBTQ Ġterms Ġfrom ĠTable ĠT AB REF 19 . ĠFor Ġvisual Ġconvenience , Ġwords Ġthat Ġoccurred Ġfewer Ġthen Ġten Ġtimes Ġand Ġproper Ġnames Ġare Ġnot Ġshown . ĠWe Ġalso Ġfiltered Ġout Ġother ĠLGBTQ Ġlabels Ġand Ġforms Ġof Ġthe Ġword Ġheterosexual Ġfrom Ġthese Ġlists , Ġas Ġthese Ġwords Ġwere Ġcomm om Ġneighbors Ġfor Ġall Ġterms Ġacross Ġall Ġyears . </s> Table ĠT AB REF 22 Ġshows Ġthat Ġin Ġ1986 , ĠLGBTQ Ġgroups Ġwere Ġmost Ġhighly Ġassociated Ġwith Ġwords Ġthat Ġoften Ġconvey Ġa Ġsense Ġof Ġsexual Ġdev ian cy , Ġincluding Ġprom isc uity , Ġprom isc uous , Ġpolygamy , Ġbest ial ity , Ġand Ġpornography . ĠThese Ġassociations Ġsuggest Ġthat ĠLGBTQ Ġpeople Ġwere Ġdehuman ized Ġto Ġsome Ġextent Ġat Ġthis Ġtime , Ġand Ġtheir Ġidentities Ġwere Ġnot Ġfully Ġrecognized Ġor Ġvalued . ĠThis Ġshifted Ġby Ġ2000 , Ġwhere Ġwe Ġno Ġlonger Ġsee Ġassociations Ġbetween ĠLGBTQ Ġgroups Ġand Ġideas Ġthat Ġevoke Ġmoral Ġdisgust . ĠInstead , Ġthe Ġ2000 Ġvector Ġspace Ġshows Ġthat ĠLGBTQ Ġpeople Ġhave Ġbecome Ġmore Ġassociated Ġwith Ġcivil Ġrights Ġissues Ġ( suggest ed Ġby Ġinter racial , Ġhomophobia , Ġand Ġdiscrimination ). ĠThe Ġwords Ġord ination Ġand Ġord aining Ġlikely Ġappear Ġdue Ġto Ġmajor Ġcontroversies Ġthat Ġarose Ġat Ġthis Ġtime Ġabout Ġwhether ĠLGBTQ Ġpeople Ġshould Ġbe Ġpermitted Ġto Ġbe Ġordained . ĠWe Ġalso Ġsee Ġsome Ġindications Ġof Ġself - ident ification Ġwith Ġthe Ġterm Ġopenly . ĠFinally , Ġwe Ġsee Ġa Ġslight Ġshift Ġtowards Ġassociations Ġwith Ġidentity Ġin Ġ2015 , Ġwith Ġnearby Ġwords Ġincluding Ġnont rans gender , Ġcloset ed , Ġequality , Ġand Ġsexuality . ĠCur iously , Ġthe Ġword Ġabortion Ġis Ġa Ġnearby Ġterm Ġfor Ġall Ġthree Ġyears . ĠPerhaps Ġthis Ġis Ġbecause Ġopinions Ġtowards Ġabortion Ġand ĠLGBTQ Ġrights Ġseem Ġto Ġbe Ġdivided Ġalong Ġsimilar Ġpartisan Ġlines . </s> Table ĠT AB REF 23 Ġshows Ġthe Ġten Ġnearest Ġneighboring Ġwords Ġto Ġour Ġrepresentations Ġof Ġgay Ġand Ġhomosexual Ġ( which Ġare Ġweighted Ġaverages Ġof Ġtheir Ġsingular / ad ject ival Ġand Ġplural Ġforms ' Ġvectors ). ĠProper Ġnames , Ġwords Ġappearing Ġless Ġthan Ġten Ġtimes Ġthat Ġyear , Ġother ĠLGBTQ Ġterms , Ġand Ġforms Ġof Ġheterosexual Ġare Ġagain Ġfiltered Ġout Ġfor Ġconvenience Ġof Ġvisualization Ġand Ġinterpretation . ĠTable ĠT AB REF 23 Ġreveals Ġvariation Ġin Ġsocial Ġmeaning Ġbetween Ġgay Ġand Ġhomosexual Ġfrom Ġas Ġearly Ġas Ġ1986 Ġdespite Ġden ot ational Ġsimilarity , Ġand Ġthese Ġdifferences Ġintensify Ġover Ġtime . ĠIn Ġ1986 , Ġgay Ġis Ġassociated Ġwith Ġterms Ġof Ġdiscrimination , Ġcivil Ġrights Ġand Ġactivism , Ġsuch Ġas Ġhomophobia , Ġfeminist , Ġsuff rage , Ġsexism , Ġand Ġa . c . l . u Ġ( American ĠCivil ĠLiberties ĠUnion , Ġa Ġnonprofit Ġorganization ). ĠOn Ġthe Ġother Ġhand , Ġhomosexual Ġis Ġprimarily Ġassociated Ġwith Ġwords Ġrelated Ġto Ġsexual Ġactivity Ġ( e . g . Ġprom isc uity , Ġanal , Ġintercourse , Ġconsent ing ). </s> In Ġ1986 , Ġthis Ġpattern Ġmay Ġbe Ġlikely Ġbecause Ġof Ġdiscussions Ġof Ġsexual Ġactivity Ġin Ġthe Ġcontext Ġof Ġthe ĠAIDS Ġepidemic , Ġbut Ġwe Ġcontinue Ġto Ġobserve Ġthe Ġpe j oration Ġof Ġthe Ġword Ġhomosexual Ġover Ġtime Ġand Ġits Ġincreased Ġuse Ġin Ġdehuman izing Ġcontexts . ĠIn Ġ2000 , Ġnearest Ġneighbors Ġof Ġthe Ġword Ġhomosexual Ġinclude Ġdev iant , Ġimmoral , Ġand Ġcriminal izing . ĠWhile Ġgay Ġbecomes Ġmore Ġassociated Ġwith Ġissues Ġrelated Ġto Ġmarriage Ġequality Ġand Ġidentity Ġin Ġ2015 , Ġhomosexual Ġbecomes Ġextremely Ġassociated Ġwith Ġmoral Ġdisgust Ġand Ġillicit Ġactivity , Ġwith Ġnearest Ġneighbors Ġincluding Ġbest ial ity , Ġpedoph ilia , Ġadultery , Ġinf antic ide , Ġand Ġabhor rent . </s> This Ġqualitative Ġanalysis Ġof Ġword Ġembed ding Ġneighbors Ġreveals Ġsignificant Ġvariation Ġand Ġchange Ġin Ġthe Ġsocial Ġmeanings Ġassociated Ġwith ĠLGBTQ Ġgroup Ġlabels , Ġwith Ġclear Ġrelationships Ġto Ġdehuman izing Ġlanguage . ĠWe Ġwill Ġnow Ġpresent Ġour Ġquantitative Ġresults Ġfor Ġmeasuring Ġeach Ġcomponent Ġof Ġdehuman ization . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠPar agraph - level ĠVal ence ĠAnalysis Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 27 Ġshows Ġthe Ġaverage Ġval ence Ġfor Ġparagraphs Ġcontaining ĠLGBTQ Ġlabels Ġ( and ĠAmerican ( s ) Ġfor Ġcomparison ), Ġwhere Ġa Ġparagraph 's Ġval ence Ġis Ġsimply Ġthe Ġaverage Ġval ence Ġover Ġits Ġwords Ġ( or Ġle mm as ) Ġthat Ġappear Ġin Ġthe ĠN RC ĠV AD ĠVal ence ĠLex icon . ĠThe ĠN RC ĠV AD Ġlex icons Ġactually Ġcontain Ġseveral ĠLGBTQ Ġterms , Ġwhich Ġall Ġhave Ġlower Ġthan Ġthe Ġaverage Ġval ence Ġscore Ġof Ġ0 . 5 : Ġtranssexual Ġ( 0 . 264 ), Ġhomosexual Ġ( 0 . 333 ), Ġlesbian Ġ( 0 . 385 ), Ġgay Ġ( 0 . 388 ), Ġand Ġbisexual Ġ( 0 . 438 ). ĠThese Ġvalues Ġcontrast Ġstark ly Ġwith Ġmore Ġpositively - val enced Ġentries Ġin Ġthe Ġlex icon , Ġsuch Ġas Ġheterosexual Ġ( 0 . 561 ), Ġperson Ġ( 0 . 646 ), Ġhuman Ġ( 0 . 767 ), Ġman Ġ( 0 . 688 ), Ġand Ġwoman Ġ( 0 . 8 65 ). ĠThese Ġdisparities Ġlikely Ġreveal Ġbiases Ġamong Ġthe Ġhuman Ġannot ators Ġwhose Ġjudgments Ġwere Ġused Ġto Ġconstruct Ġthe ĠN RC ĠV AD Ġlex icon ĠB IB REF 56 . ĠWhile Ġthe Ġlex icon Ġmay Ġitself Ġbe Ġan Ġinteresting Ġartifact Ġof Ġdehuman izing Ġattitudes Ġtowards ĠLGBTQ Ġpeople , Ġwe Ġremoved Ġthese Ġterms Ġfrom Ġthe Ġlex icon Ġbefore Ġcalculating Ġaverage Ġval ence Ġover Ġeach Ġparagraph Ġin Ġthe ĠLGBTQ Ġcorpus Ġin Ġorder Ġto Ġisolate Ġlinguistic Ġsignals Ġin Ġthe ĠNew ĠYork ĠTimes Ġdata Ġfrom Ġannotation Ġbiases . ĠWithout Ġthis Ġpre processing Ġstep , Ġthe Ġtemporal Ġtrends Ġand Ġrelative Ġdifferences Ġbetween Ġall ĠLGBTQ Ġterms , Ġgay , Ġand Ġhomosexual Ġremain Ġroughly Ġthe Ġsame , Ġbut Ġeach Ġof Ġthe ĠLGBTQ Ġlabels Ġwould Ġoccur Ġin Ġsignificantly Ġmore Ġnegative Ġparagraphs Ġthan ĠAmerican . </s> Figure ĠFIG REF 27 Ġshows Ġthe Ġparagraph Ġaverages Ġover Ġfive - year Ġranges Ġfor Ġvisualization Ġpurposes Ġprimarily Ġdue Ġto Ġdata Ġsp arsity Ġin Ġlater Ġyears Ġfor Ġhomosexual Ġ( there Ġwere Ġjust Ġ208 Ġparagraphs Ġcontaining Ġhomosexual Ġin Ġ2014 , Ġrelative Ġto Ġ3 669 Ġcontaining Ġgay Ġin Ġthe Ġsame Ġyear ). ĠAnalysis Ġof Ġoverlapping Ġconfidence Ġintervals Ġand ĠWil co xon Ġsigned - rank Ġtests Ġover Ġthe Ġmeans Ġfor Ġeach Ġof Ġthe Ġthirty Ġyears Ġindicates Ġthat Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġoccur Ġin Ġsignificantly Ġmore Ġpositive Ġparagraphs Ġthan Ġhomosexual Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠHowever , ĠAmerican Ġactually Ġoccurs Ġin Ġmore Ġnegative Ġparagraphs Ġthan Ġall ĠLGBTQ Ġterms Ġor Ġgay Ġ($ p Ġ< Ġ0 . 05 $ ). ĠA Ġlinear Ġregression Ġanalysis Ġover Ġall Ġyears Ġreveals Ġthat Ġall ĠLGBTQ Ġterms , Ġgay , Ġand Ġhomosexual Ġall Ġsignificantly Ġincrease Ġin Ġparagraph - level Ġval ence Ġover Ġtime Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠHowever , Ġwhen Ġconsidering Ġjust Ġthe Ġlast Ġ15 Ġyears , Ġgay Ġstill Ġsignificantly Ġincreases Ġin Ġparagraph - level Ġval ence , Ġwhile Ġhomosexual Ġmay Ġbe Ġtrending Ġdownward , Ġalthough Ġthis Ġtrend Ġdoes Ġnot Ġreach Ġsignificance Ġin Ġour Ġdata Ġ($ p Ġ= Ġ0 . 07 8 $ ). </s> The Ġparagraph - level Ġval ence Ġanalysis Ġshown Ġin ĠFigure ĠFIG REF 27 Ġsuggests Ġthat ĠLGBTQ Ġgroups Ġhave Ġbecome Ġincreasingly Ġpositively Ġevaluated Ġover Ġtime , Ġand Ġhave Ġthus Ġlikely Ġbeen Ġless Ġdehuman ized Ġin Ġthe ĠNew ĠYork ĠTimes . ĠHowever , Ġthe Ġlack Ġof Ġa Ġstatistically Ġsignificant Ġincrease Ġ( rather , Ġa Ġslight Ġdownward Ġtrend ) Ġin Ġaverage Ġval ence Ġfor Ġparagraphs Ġcontaining Ġhomosexual Ġbetween Ġ2001 Ġand Ġ2015 Ġsuggests Ġthat Ġattitudes Ġtowards Ġand Ġevaluations Ġof Ġpeople Ġdescribed Ġas Ġhomosexual Ġhave Ġnot Ġimproved Ġin Ġthe Ġsame Ġway Ġas Ġthose Ġdescribed Ġby Ġother ĠLGBTQ Ġlabels . </s> Finally , Ġthis Ġmeasurement Ġdoes Ġnot Ġsupport Ġour Ġinitial Ġhypothesis Ġthat ĠLGBTQ Ġgroups Ġhave Ġbeen Ġmore Ġnegatively Ġevaluated Ġthan Ġthe Ġother Ġsocial Ġgroup ĠAmerican ( s ), Ġbut Ġdoes Ġreveal Ġthat Ġthe Ġobserved Ġtrend Ġfor ĠLGBTQ Ġlabels Ġis Ġnot Ġmerely Ġan Ġartifact Ġof Ġchanging Ġreporting Ġstyles , Ġsince Ġparagraphs Ġcontaining ĠAmerican ( s ) Ġshow Ġa Ġvery Ġdifferent Ġpattern . ĠOverall , Ġthis Ġresult Ġdemonstrates Ġsubstantial Ġlanguage Ġchange Ġin Ġthe ĠNew ĠYork ĠTimes 's Ġdiscussion Ġof ĠLGBTQ Ġpeople Ġas Ġwell Ġas Ġvariation Ġin Ġthe Ġcontexts Ġwhere Ġdifferent Ġgroup Ġlabels Ġappear , Ġparticularly Ġhomosexual . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠPar agraph - level ĠVal ence ĠAnalysis Ġ: :: ĠQual itative ĠAnalysis </s> How Ġwell Ġdoes Ġparagraph - level Ġval ence Ġanalysis Ġcapture Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġand Ġthus Ġa Ġmajor Ġcomponent Ġof Ġdehuman ization ? ĠTo Ġfacilitate Ġa Ġqualitative Ġevaluation Ġof Ġthis Ġtechnique , Ġwe Ġidentify Ġseveral Ġhundred Ġparagraphs Ġwith Ġthe Ġhighest Ġand Ġlowest Ġaverage Ġval ence . ĠIt Ġappears Ġthat Ġmost Ġparagraphs Ġwith Ġhigh Ġval ence Ġscores Ġdo Ġexpress Ġpositive Ġevaluations Ġof ĠLGBTQ Ġindividuals , Ġand Ġthose Ġwith Ġlow Ġscores Ġexpress Ġnegative Ġevaluations . </s> Table ĠT AB REF 29 Ġcontain Ġexamples Ġwith Ġextremely Ġhigh Ġand Ġlow Ġval ence . ĠWe Ġidentify Ġseveral Ġmajor Ġthemes Ġfrom Ġthese Ġresults , Ġwhich Ġare Ġincluded Ġin Ġthe ĠInterpret ation Ġcolumn Ġof ĠTable ĠT AB REF 29 . ĠMost Ġparagraphs Ġwith Ġhigh Ġval ence Ġscores Ġemphasize Ġequal Ġrights , Ġwhile Ġsome Ġfocus Ġon Ġthe Ġactivities Ġof Ġadvocacy Ġorganizations . ĠOn Ġthe Ġother Ġend , Ġnumerous Ġparagraphs Ġwith Ġextremely Ġlow Ġval ence Ġtend Ġto Ġfocus Ġon Ġviolence Ġagainst ĠLGBTQ Ġpeople , Ġdiscussions Ġof Ġdisease Ġ( especially ĠAIDS ), Ġand ĠLGBTQ Ġissues Ġinternationally . ĠOther Ġthemes Ġthat Ġemerged Ġin Ġlow - val ence Ġparagraphs Ġinclude Ġreports Ġon Ġ( and Ġdirect Ġquotes Ġfrom ) Ġpublic Ġfigures Ġwho Ġdehuman ized Ġpeople Ġas Ġwell Ġas Ġportray als Ġof ĠLGBTQ Ġpeople Ġas Ġreckless , Ġirresponsible , Ġand Ġangry . </s> While Ġthis Ġtechnique Ġaccurately Ġcaptures Ġthe Ġval ence Ġfor Ġmany Ġparagraphs , Ġwe Ġalso Ġidentify Ġseveral Ġshortcomings . ĠSome Ġof Ġthe Ġextreme Ġoutl iers Ġare Ġextremely Ġshort Ġparagraphs , Ġincluding Ġsubtitles Ġwithin Ġarticles Ġwhich Ġare Ġincluded Ġas Ġparagraphs Ġin Ġthe Ġdata . ĠTable ĠT AB REF 30 Ġshows Ġseveral Ġexamples Ġthat Ġwere Ġmis character ized Ġby Ġour Ġparagraph - level Ġval ence Ġanalysis Ġtechnique . ĠIn Ġaddition Ġto Ġthe Ġshort Ġsubtitles , Ġthere Ġare Ġseveral Ġparagraphs Ġwith Ġhighly Ġpositive Ġaverage Ġval ence Ġthat Ġseem Ġto Ġexpress Ġmore Ġnegative Ġevaluations Ġof ĠLGBTQ Ġpeople . ĠIn Ġthe Ġthird Ġparagraph Ġin ĠTable ĠT AB REF 30 , Ġthe Ġval ence Ġis Ġskewed Ġby Ġthe Ġpositive Ġwords Ġsupported Ġ( 0 . 8 53 ) Ġand Ġmarriage Ġ( 0 . 8 44 ) Ġeven Ġthough Ġthe Ġparagraph Ġis Ġactually Ġdiscussing Ġlow Ġsupport Ġfor Ġgay Ġmarriage . ĠWhile Ġthe Ġfourth Ġparagraph Ġargues Ġthat Ġgay Ġcouples Ġwould Ġbe Ġsub par Ġparents Ġrelative Ġto Ġstraight Ġcouples , Ġit Ġuses Ġpositive Ġterms Ġsuch Ġas Ġlove Ġand Ġideal . ĠFurthermore , Ġkins hip Ġterms Ġtend Ġto Ġbe Ġassigned Ġhighly Ġpositive Ġvalues Ġin Ġthe ĠN RC ĠV AD ĠVal ence ĠLex icon , Ġincluding Ġchild Ġ( 0 . 9 12 ), Ġchildren Ġ( 0 . 8 57 ), Ġand Ġfamily Ġ( 0 . 968 ). ĠSimilarly , Ġeven Ġthough Ġthe Ġfinal Ġexample Ġdescribes Ġdiscrimination Ġbased Ġon Ġsexual Ġorientation , Ġthe Ġparagraph 's Ġaverage Ġval ence Ġis Ġimpacted Ġby Ġpositive Ġkins hip Ġterms Ġsuch Ġas Ġfather Ġ( 0 . 8 12 ) Ġand Ġmother Ġ( 0 . 9 31 ) Ġ. </s> Overall , Ġour Ġqualitative Ġanalysis Ġof Ġparagraph - level Ġval ence Ġscores Ġshows Ġthat Ġhighly Ġpositive Ġval ence Ġoften Ġaccompanies Ġexpressions Ġof Ġpositive Ġevaluation Ġtowards ĠLGBTQ Ġgroups , Ġand Ġlow Ġval ence Ġoften Ġaccompanies Ġexpressions Ġof Ġnegative Ġevaluation . ĠHowever , Ġparagraph - level Ġval ence Ġscores Ġare Ġalso Ġimpacted Ġby Ġspecific Ġwords Ġc ued Ġby Ġvarious Ġtopics ; Ġparagraphs Ġabout Ġsame - sex Ġmarriage Ġtend Ġto Ġbe Ġmore Ġpositive Ġbecause Ġwords Ġlike Ġmarriage , Ġmarry , Ġand Ġcouple Ġhave Ġhigh Ġval ence Ġscores Ġ( great er Ġthan Ġ0 . 8 ), Ġwhile Ġparagraphs Ġreporting Ġon Ġhate Ġcrimes Ġtend Ġto Ġbe Ġmore Ġnegative Ġbecause Ġthey Ġcontain Ġmore Ġextremely Ġlow - val ence Ġwords Ġrelated Ġto Ġcrime , Ġviolence , Ġinjury , Ġand Ġdeath . ĠFurthermore , Ġwe Ġhave Ġfound Ġthat Ġthis Ġsimple Ġlex icon - based Ġmethod Ġhas Ġdifficulties Ġin Ġdis ent angling Ġperspectives Ġwithin Ġthe Ġtext . ĠFor Ġexample , Ġeven Ġthough Ġthere Ġare Ġlinguistic Ġsignals Ġof Ġdehuman ization Ġexpressed Ġin Ġarticles Ġreporting Ġon Ġanti - LGBT Q Ġviolence Ġand Ġhomophobic Ġspeech , Ġthese Ġdehuman izing Ġattitudes Ġare Ġnot Ġnecessarily Ġfrom Ġthe Ġviewpoint Ġof Ġthe Ġjournalist Ġor Ġthe Ġinstitution . ĠNevertheless , Ġthere Ġcould Ġbe Ġan Ġoverall Ġdehuman izing Ġeffect Ġif Ġthe Ġmedia 's Ġdiscussions Ġof Ġa Ġmarginalized Ġsocial Ġgroup Ġlargely Ġemphasizes Ġevents Ġthat Ġharm Ġpeople , Ġsuch Ġas Ġviolent Ġcrimes Ġand Ġdisease . ĠIt Ġis Ġpossible Ġthat Ġrepeated Ġassociations Ġbetween ĠLGBTQ Ġidentity Ġlabels Ġand Ġsuch Ġnegative Ġcontexts Ġcontribute Ġto Ġnegative Ġevaluations Ġof ĠLGBTQ Ġgroups . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠCon notation ĠFrames Ġof ĠPerspective Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 34 Ġshows Ġthe Ġwriter 's Ġaverage Ġperspective Ġ( val ence ) Ġtowards Ġnoun Ġphrases Ġcontaining Ġeither Ġany ĠLGBTQ Ġlabels , Ġgay ( s ), Ġhomosexual ( s ), Ġor Ġthe Ġcomparison Ġgroup ĠAmerican ( s ) Ġusing Ġthe ĠCon notation ĠFrames Ġlex icon ĠB IB REF 22 . ĠNote Ġthat Ġthe Ġwide Ġvariation , Ġparticularly Ġfor Ġhomosexual , Ġis Ġlikely Ġdue Ġto Ġsp arsity , Ġas Ġlimiting Ġthe Ġcon notation Ġframes Ġanalysis Ġto Ġverbs ' Ġimmediate Ġsubject Ġand Ġdirect Ġobject Ġnoun Ġphrase Ġdepend ents Ġ( cons isting Ġof Ġonly Ġdetermin ers , Ġadject ives , Ġand Ġnoun s ) Ġgreatly Ġreduced Ġthe Ġamount Ġof Ġdata Ġfor Ġeach Ġyear . ĠFor Ġexample , Ġthere Ġwere Ġonly Ġ39 Ġtri ples Ġfor Ġhomosexual Ġin Ġ2015 . ĠWe Ġthus Ġshow Ġresults Ġaggreg ated Ġover Ġfive - year Ġintervals , Ġas Ġin ĠFigure ĠFIG REF 27 . </s> As Ġwith Ġparagraph - level Ġval ence , Ġthe Ġwriter 's Ġperspective Ġtowards Ġthe Ġlabel Ġhomosexual Ġis Ġsignificantly Ġmore Ġnegative Ġthan Ġtowards Ġgay Ġ($ Ġp Ġ< Ġ0 . 001 $ ). ĠLinear Ġregression Ġindicates Ġthat Ġperspectives Ġtowards Ġnoun Ġphrases Ġnamed Ġby Ġany ĠLGBTQ Ġterm , Ġgay , Ġand ĠAmerican Ġhave Ġall Ġsignificantly Ġincreased Ġover Ġtime Ġ($ p Ġ< Ġ0 . 01 $ ). ĠHowever , Ġthe Ġtrends Ġare Ġstill Ġquite Ġdifferent , Ġas Ġthe Ġslopes Ġfor Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġare Ġan Ġorder Ġof Ġmagnitude Ġgreater Ġthan ĠAmerican Ġ($ m Ġ= ( 1 . 1 \ pm Ġ0 . 39 )\ times Ġ10 ^{ - 4 } $ Ġfor ĠAmerican , Ġ$ m =( 1 . 4 \ pm Ġ0 . 18 )\ times Ġ10 ^{ - 3 } $ Ġfor Ġall ĠLGBTQ Ġterms , Ġand Ġ$ m =( 1 . 1 \ pm Ġ0 . 22 )\ times Ġ10 ^{ - 3 } $ Ġfor Ġgay ). ĠFurthermore , Ġthe Ġwriter 's Ġperspective Ġtowards Ġnoun Ġphrases Ġcontaining Ġhomosexual Ġhave Ġsignificantly Ġdecreased Ġover Ġtime Ġ($ p Ġ< Ġ0 . 0001 $ ). </s> Overall , Ġcon notation Ġframes ' Ġperspective Ġscores Ġreveal Ġa Ġsimilar Ġpattern Ġas Ġthe Ġparagraph - level Ġval ence Ġanalysis , Ġwhere ĠLGBTQ Ġgroups Ġoverall Ġappear Ġto Ġbe Ġmore Ġpositively Ġevaluated Ġin Ġthe ĠNew ĠYork ĠTimes Ġover Ġtime . ĠUnlike Ġgay Ġand Ġthe Ġaggreg ated Ġall ĠLGBTQ Ġterms , Ġthe Ġlabel Ġhomosexual Ġundergo es Ġpe j oration , Ġas Ġhomosexual Ġbecomes Ġincreasingly Ġused Ġwhen Ġ( impl icit ly ) Ġexpressing Ġnegative Ġattitudes Ġtowards ĠLGBTQ Ġpeople . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠCon notation ĠFrames Ġof ĠPerspective Ġ: :: ĠQual itative ĠAnalysis </s> To Ġqual itatively Ġanalyze Ġhow Ġwell Ġthe Ġcon notation Ġframes ' Ġlex icon Ġcapture Ġthe Ġnegative Ġevaluation Ġof Ġa Ġtarget Ġgroup Ġcomponent Ġof Ġdehuman ization , Ġwe Ġidentify Ġsubject - verb - object Ġtu ples Ġwhere Ġthe Ġverb Ġindicates Ġthat Ġthe Ġwriter Ġhas Ġextremely Ġpositive Ġor Ġnegative Ġperspective Ġtowards Ġeither Ġthe Ġsubject Ġor Ġobject . ĠThe Ġfirst Ġtwo Ġparagraphs Ġin ĠTable ĠT AB REF 36 Ġwere Ġidentified Ġamong Ġthose Ġwith Ġthe Ġmost Ġnegative Ġwriters ' Ġperspectives Ġtowards Ġphrases Ġcontaining ĠLGBTQ Ġlabels . ĠThe Ġfirst Ġparagraph Ġ( within Ġa Ġquote ) Ġuses Ġthe Ġphrase Ġany Ġhomosexual Ġact Ġas Ġthe Ġdirect Ġobject Ġto Ġthe Ġverb Ġcommitted , Ġwhich Ġhas Ġthe Ġeffect Ġof Ġframing Ġhomosexuality Ġas Ġa Ġcrime Ġor Ġother Ġillicit Ġactivity . ĠBy Ġdeem ing Ġgay Ġcandidates Ġunworthy Ġof Ġthe Ġpriesthood , Ġthe Ġspeaker Ġclearly Ġnegatively Ġevaluates ĠLGBTQ Ġpeople . ĠOn Ġthe Ġopposite Ġend , Ġmany Ġof Ġthe Ġparagraphs Ġlabeled Ġby Ġour Ġmethod Ġas Ġcontaining Ġextremely Ġpositive Ġperspectives Ġtowards Ġphrases Ġcontaining ĠLGBTQ Ġlabels Ġdo Ġappear Ġto Ġhave Ġpositive Ġevaluations Ġof Ġthese Ġgroups . ĠThe Ġsecond Ġand Ġthird Ġparagraphs Ġof ĠTable ĠT AB REF 36 Ġillustrate Ġthis , Ġwhere Ġthe Ġgays Ġare Ġviewed Ġpositively Ġfor Ġhaving Ġsaved Ġa Ġtown , Ġand Ġgay Ġrights Ġadvocates Ġare Ġpraised Ġfor Ġtheir Ġwork . </s> However , Ġwe Ġfound Ġseveral Ġinstances Ġwhere Ġparagraphs Ġseem Ġto Ġhave Ġbeen Ġmis label ed , Ġwhich Ġare Ġshown Ġin ĠTable ĠT AB REF 37 . ĠIn Ġthe Ġfirst Ġparagraph Ġof ĠTable ĠT AB REF 37 , Ġour Ġtechnique Ġidentifies Ġgay Ġmarriage Ġas Ġthe Ġsubject Ġof Ġdependent Ġof Ġthe Ġnegative - pers pect ive Ġverb Ġharmed , Ġbut Ġdoes Ġnot Ġaccount Ġfor Ġthe Ġpreceding Ġtext , Ġwhich Ġactually Ġreveals Ġthat Ġthe Ġparagraph Ġcontradicts Ġthe Ġpremise Ġthat Ġgay Ġmarriage Ġcauses Ġharm , Ġand Ġthus Ġdoes Ġnot Ġcontain Ġovertly Ġnegative Ġevaluations Ġof ĠLGBTQ Ġgroups Ġ( although Ġthis Ġparticular Ġexample Ġreveals Ġthe Ġdifficulty Ġof Ġoperational izing Ġthis Ġcomponent Ġbecause ĠProtect Mar riage Ġgroups Ġstrongly Ġoppose Ġsame - sex Ġmarriage Ġand Ġmay Ġitself Ġhave Ġnegative Ġevaluations Ġof ĠLGBTQ Ġpeople ). ĠThe Ġsecond Ġexample Ġsimilarly Ġshows Ġthat Ġthis Ġsimple Ġmethod Ġdoes Ġnot Ġadequately Ġaccount Ġfor Ġvarious Ġforms Ġof Ġneg ation , Ġas Ġthe Ġpositive - pers pect ive Ġverb Ġprotect Ġis Ġactually Ġneg ated . ĠThe Ġlast Ġexample Ġin ĠTable ĠT AB REF 37 Ġpresents Ġa Ġcomplex Ġcase , Ġand Ġit Ġis Ġeven Ġqual itatively Ġchallenging Ġto Ġdetermine Ġthe Ġwriter 's Ġperspective Ġtowards ĠLGBTQ Ġpeople . ĠOur Ġmethod Ġidentifies Ġgays Ġas Ġthe Ġsubject Ġof Ġthe Ġverb Ġstrengthen , Ġeven Ġthough Ġthe Ġsubject Ġshould Ġbe Ġthe Ġger und Ġallowing Ġgays Ġ( into Ġthe Ġmilitary ), Ġand Ġthe Ġlex icon 's Ġentry Ġfor Ġthe Ġwriter 's Ġperspective Ġtowards Ġthe Ġsubject Ġof Ġof Ġstrengthen Ġis Ġa Ġhighly Ġpositive Ġ0 . 7 . ĠHowever , Ġthe Ġobject Ġof Ġthis Ġverb Ġis Ġthe Ġterrorist Ġorganization ĠAl ĠQaeda ; Ġour Ġbackground Ġknowledge Ġsuggests Ġthat Ġthe Ġcapacity Ġto Ġstrengthen ĠAl ĠQaeda Ġwould Ġreflect Ġnegative Ġperspectives . ĠHowever , Ġthis Ġadditional Ġcontext Ġprovided Ġby Ġthe Ġrest Ġof Ġthe Ġparagraph Ġindicates Ġthat Ġthe Ġwriter Ġis Ġbeing Ġsarcastic Ġand Ġconsiders Ġthe Ġproposition Ġthat Ġgays Ġhave Ġany Ġimpact Ġon Ġstrengthening ĠAl ĠQaeda Ġto Ġbe Ġridiculous . ĠFinally , Ġthe Ġwriter Ġemphasizes Ġtheir Ġown Ġstance Ġin Ġopposition Ġto Ġthe ĠMissouri Ġcongressional Ġcandidate Ġby Ġcalling Ġupon Ġcommon Ġstereotypes Ġof Ġgay Ġpeople Ġbeing Ġgood Ġat Ġdancing Ġand Ġaccess or izing . </s> Me asuring Ġthe Ġcon notation Ġframes ' Ġlex icon Ġperspective Ġscores Ġover Ġverbs ' Ġsubjects Ġand Ġdirect Ġobjects Ġcannot Ġleverage Ġas Ġmuch Ġcontext Ġor Ġdata Ġas Ġmeasuring Ġval ence Ġover Ġparagraphs Ġusing Ġthe ĠN RC ĠV AD Ġlex icon Ġlabeled Ġfor Ġ20 , 000 Ġwords . ĠHowever , Ġthis Ġtechnique Ġcan Ġmake Ġmore Ġfine - gr ained Ġdistinctions Ġregarding Ġthe Ġwriter 's Ġ( and Ġinstitution 's ) Ġattitudes Ġdirected Ġtowards ĠLGBTQ Ġpeople Ġand Ġis Ġnot Ġas Ġdramatically Ġimpacted Ġby Ġthe Ġemotional Ġval ence Ġof Ġthe Ġtopic Ġdiscussed . ĠHowever , Ġwith Ġboth Ġtechniques Ġpresented , Ġwe Ġhave Ġdifficulties Ġdis ent angling Ġthe Ġjournalist 's Ġperspective Ġfrom Ġthose Ġexpressed Ġby Ġothers Ġand Ġsimply Ġreported Ġby Ġthe Ġjournalist . ĠWhile Ġremoving Ġdirect Ġquotations Ġmay Ġpartially Ġaddress Ġthis Ġissue , Ġwe Ġdeliberately Ġdid Ġnot Ġremove Ġtext Ġfrom Ġdirect Ġquotes Ġor Ġparaph r ases . ĠThe Ġjournalists Ġand Ġnewspaper Ġmake Ġintentional Ġdecisions Ġabout Ġwhat Ġtext Ġto Ġinclude Ġand Ġexclude Ġfrom Ġquotations , Ġwhich Ġcould Ġstill Ġmeaning fully Ġrepresent Ġtheir Ġperspectives Ġand Ġvalues ĠB IB REF 69 . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠVal ence Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 40 Ġshows Ġthe Ġaverage Ġval ence Ġscores Ġof Ġthe Ġ1000 Ġnearest Ġneighbors Ġto Ġthe Ġvector Ġrepresentations Ġof Ġgay , Ġhomosexual , Ġall ĠLGBTQ Ġterms , Ġand ĠAmerican Ġfor Ġeach Ġyear . ĠCons istent Ġwith Ġother Ġresults Ġfor Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġthe Ġvector Ġrepresentations Ġfor Ġall ĠLGBTQ Ġterms Ġand Ġgay Ġshow Ġsimilar Ġpatterns , Ġalthough Ġthe ĠLGBTQ Ġvector Ġmore Ġclosely Ġresembles Ġthe Ġhomosexual Ġvector Ġin Ġearlier Ġyears Ġsince Ġhomosexual Ġwas Ġmore Ġfrequently Ġused Ġin Ġall Ġdiscussions Ġof ĠLGBTQ Ġcommunities Ġat Ġthat Ġtime . ĠIn Ġcontrast Ġto Ġour Ġother Ġtechniques Ġto Ġquantify Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġthis Ġmeasurement Ġnotably Ġshows Ġthat Ġthe Ġval ence Ġof ĠAmerican 's Ġneighboring Ġwords Ġis Ġsignificantly Ġgreater Ġthan Ġany Ġof Ġthe ĠLGBTQ Ġgroup Ġrepresentations ' Ġneighbors Ġover Ġall Ġyears Ġ( Wil co xon 's Ġsigned - rank Ġtest , Ġ$ p Ġ< Ġ0 . 0001 $ ), Ġindicating Ġthat ĠAmerican Ġis Ġused Ġin Ġmore Ġpositive Ġcontexts Ġthan ĠLGBTQ Ġterms . ĠFurthermore , Ġall ĠLGBTQ Ġvectors ' Ġneighbors Ġhave Ġan Ġaverage Ġval ence Ġbelow Ġthe Ġneutral Ġ0 . 5 . ĠHowever , Ġthe Ġaverage Ġval ence Ġof Ġthe Ġaggreg ated Ġall ĠLGBTQ Ġterms Ġrepresentation 's Ġneighbors Ġsignificantly Ġincreases Ġover Ġtime Ġ($ p Ġ< Ġ0 . 0001 $ ), Ġsuggesting Ġsome Ġincreasing Ġhuman ization Ġin Ġthe Ġlanguage Ġused Ġin Ġdiscussions Ġof ĠLGBTQ Ġpeople . </s> Figure ĠFIG REF 40 Ġalso Ġreveals Ġdramatic Ġcon not ational Ġdifferences Ġbetween Ġgay Ġand Ġhomosexual . ĠAs Ġshown Ġby Ġnon - over l apping Ġconfidence Ġintervals Ġand Ġa ĠWil co xon Ġsigned - rank Ġtest , Ġthe Ġaverage Ġval ence Ġfor Ġhomosexual 's Ġneighbors Ġis Ġsignificantly Ġlower Ġthan Ġgay 's Ġneighbors Ġover Ġall Ġyears Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠGay 's Ġaverage Ġneighbor Ġval ence Ġslightly Ġincreases Ġover Ġtime , Ġbut Ġdoes Ġnot Ġreach Ġsignificance Ġ($ p Ġ= Ġ0 . 060 $ ), Ġbut Ġhomosexual 's Ġneighboring Ġwords Ġbecome Ġsignificantly Ġmore Ġnegative Ġover Ġtime Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠAnaly zing Ġthe Ġval ence Ġof Ġthe Ġnearest Ġneighbors Ġindicates Ġthat Ġhomosexual Ġhas Ġlong Ġbeen Ġused Ġin Ġmore Ġnegative Ġ( and Ġpotentially Ġdehuman izing ) Ġcontexts Ġthan Ġgay , Ġand Ġthat Ġthese Ġwords ' Ġmeanings Ġhave Ġfurther Ġdiver ged Ġas Ġthe Ġlabel Ġhomosexual Ġhas Ġbeen Ġused Ġin Ġincreasingly Ġnegative Ġcontexts Ġover Ġtime . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠVal ence Ġ: :: ĠQual itative ĠAnalysis </s> Compared Ġto Ġthe Ġprevious Ġmethods , Ġone Ġlimitation Ġof Ġusing Ġword Ġembed d ings Ġto Ġquantify Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup Ġis Ġthat Ġthe Ġembed d ings Ġare Ġnot Ġeasily Ġinterpret able Ġby Ġanalyzing Ġa Ġsmall Ġsample Ġof Ġdata . ĠInstead , Ġwe Ġassess Ġthis Ġtechnique Ġby Ġidentifying ĠLGBTQ Ġterms ' Ġnearest Ġneighbors Ġin Ġseveral Ġout lier Ġyears . ĠTo Ġfacilitate Ġthis Ġqualitative Ġanalysis , Ġwe Ġidentify Ġa Ġset Ġof Ġunique Ġnearest Ġneighbors Ġfor Ġeach ĠLGBTQ Ġlabel Ġin Ġeach Ġout lier Ġyear , Ġwhere Ġa Ġword Ġis Ġa Ġunique Ġnearest Ġneighbor Ġfor Ġa Ġgiven ĠLGBTQ Ġterm Ġand Ġyear Ġif Ġit Ġis Ġnot Ġin Ġthat Ġterm 's Ġtop Ġ1000 Ġnearest Ġneighbors Ġin Ġany Ġother Ġyear . </s> Table ĠT AB REF 42 Ġcontains Ġseveral Ġexample Ġparagraphs Ġthat Ġillustrate Ġoverarching Ġthemes Ġfor Ġthe Ġout lier Ġyears Ġ1993 , Ġ1999 , Ġand Ġ2014 . ĠIn Ġthe Ġyear Ġ1999 , Ġgay , Ġhomosexual Ġand Ġthe Ġaggreg ated Ġrepresentation Ġall ĠLGBTQ Ġterms Ġwere Ġall Ġmore Ġmore Ġclosely Ġassociated Ġwith Ġlow - val ence Ġwords Ġthan Ġin Ġalmost Ġany Ġother Ġyear . ĠWe Ġconnected Ġthis Ġfinding Ġto Ġa Ġperiod Ġof Ġintense Ġreporting Ġin Ġthe Ġmonths Ġfollowing Ġthe ĠOctober Ġ1998 Ġmurder Ġof Ġa Ġgay ĠWyoming Ġcollege Ġstudent , ĠMatthew ĠShepard , Ġwhich Ġdrew Ġnational Ġattention Ġto Ġand Ġcondemnation Ġof Ġanti - LGBT Q Ġviolence . ĠBecause Ġmany Ġoccurrences Ġof ĠLGBTQ Ġgroup Ġlabels Ġappeared Ġin Ġsimilar Ġcontexts Ġas Ġcontent Ġabout Ġthis Ġincident , Ġterms Ġdirectly Ġrelated Ġto ĠMatthew ĠShepard 's Ġcase Ġhave Ġcloser Ġvector Ġrepresentations Ġto ĠLGBTQ Ġterms Ġin Ġthis Ġyear . ĠFor Ġexample , Ġgay Ġand Ġall ĠLGBTQ Ġterms 's Ġ1000 Ġnearest Ġneighbors Ġinclude Ġw y oming Ġin Ġ1998 Ġand Ġ1999 Ġand Ġshe pard Ġfrom Ġthe Ġyears Ġ1998 - 2001 . ĠUnique Ġnearest Ġneighbors Ġfor Ġgay Ġin Ġ1999 Ġinclude Ġother Ġterms Ġthat Ġcould Ġbe Ġconnected Ġto Ġthis Ġincident , Ġincluding Ġimprisoned , Ġslander ous , Ġdisgrace ful , Ġunethical , Ġand Ġunjust ifiable . ĠNot Ġonly Ġwas ĠMatthew ĠShepard 's Ġtragic Ġmurder Ġdeeply Ġrooted Ġin Ġthe Ġdehuman ization Ġof ĠLGBTQ Ġpeople , Ġbut ĠShepard Ġwas Ġfurther Ġdehuman ized Ġin Ġthe Ġmedia 's Ġemphasis Ġon Ġthe Ġgruesome Ġdetails Ġof Ġhis Ġdeath ĠB IB REF 71 . ĠB IB REF 71 Ġargue Ġthat Ġthe Ġmedia 's Ġframing Ġof Ġthis Ġcase Ġactually Ġfurther Ġsocially Ġstigmat ized ĠLGBTQ Ġpeople . </s> Sur prisingly , Ġmeasuring Ġa Ġgroup Ġlabel 's Ġval ence Ġby Ġthe Ġval ence Ġof Ġits Ġnearest Ġword Ġembed ding Ġneighbors Ġreveals Ġthat Ġthe Ġmost Ġnegative Ġyear Ġfor Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġsince Ġ1999 Ġwas Ġ2014 , Ġthe Ġsecond Ġmost - recent Ġyear Ġof Ġdata . ĠUnique Ġnearest Ġneighbors Ġfor Ġgay Ġin Ġ2014 Ġinclude Ġharassing , Ġintimidation , Ġand Ġstigma . ĠWe Ġidentified Ġseveral Ġmajor Ġthemes Ġin Ġ2014 Ġthat Ġco - occ urred Ġwith ĠLGBTQ Ġgroup Ġlabels Ġand Ġpossibly Ġled Ġto Ġthis Ġdistribution al Ġsemantic Ġpattern , Ġprimarily Ġreporting Ġon Ġanti - LGBT Q Ġlaws Ġand Ġattitudes Ġin ĠUganda Ġand ĠRussia Ġ( particularly Ġin Ġlight Ġof Ġthe Ġ2014 ĠWinter ĠOlympics Ġin ĠSochi ). ĠWe Ġalso Ġfound Ġthat Ġthe Ġterms Ġathletes Ġand Ġwinter oly mp ics Ġappeared Ġin Ġboth Ġgay Ġand Ġall ĠLGBTQ Ġterms 's Ġnearest Ġ1000 Ġneighbors Ġin Ġ2014 Ġ( ath letes Ġonly Ġappeared Ġas Ġa Ġnearby Ġneighbor Ġfor Ġtwo Ġother Ġyears : Ġ1994 Ġand Ġ2013 ). ĠIn Ġaddition , Ġthe Ġterms ĠUganda Ġand ĠUg andan Ġare Ġamong Ġall ĠLGBTQ Ġterms Ġand Ġgay 's Ġnearest Ġ1000 Ġneighbors Ġin Ġ2014 , Ġbut Ġno Ġother Ġyear . </s> Unlike Ġin Ġ1999 Ġand Ġ2014 , ĠLGBTQ Ġterms Ġin Ġthe Ġyear Ġ1993 Ġseem Ġto Ġbe Ġassociated Ġwith Ġhigher - val ence Ġwords , Ġespecially Ġhomosexual , Ġwhose Ġnearby Ġwords Ġhad Ġthe Ġhighest Ġaverage Ġval ence Ġin Ġ1993 Ġout Ġof Ġall Ġyears . ĠHom osexual 's Ġunique Ġnearest Ġneighbors Ġin Ġ1993 Ġinclude Ġthe Ġhigh - val ence Ġwords Ġmorale , Ġpledge , Ġcivilian , Ġcompromise , Ġforce , Ġand Ġreadiness . ĠThis Ġwords Ġappear Ġto Ġbe Ġconnected Ġwith Ġnumerous Ġstories Ġin Ġ1993 Ġcovering Ġthe Ġcontroversy Ġover Ġwhether Ġor Ġnot ĠLGBTQ Ġpeople Ġshould Ġbe Ġallowed Ġto Ġserve Ġin Ġthe Ġmilitary . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠDen ial Ġof ĠAgency Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 45 Ġshows Ġthe Ġagency Ġof Ġeach Ġgroup Ġlabel Ġbased Ġon Ġits Ġhead Ġverb 's Ġentry Ġin Ġthe ĠCon notation ĠFrames Ġlex icon Ġfor Ġagency Ġand Ġthe Ġgroup Ġlabel 's Ġposition Ġrelative Ġto Ġthat Ġverb ĠB IB REF 25 . ĠAs Ġin ĠFigure ĠFIG REF 34 Ġfor Ġperspective , Ġthere Ġis Ġlarge Ġvariance Ġfor Ġeach Ġyear Ġdue Ġto Ġdata Ġsp arsity Ġwhen Ġusing Ġthe ĠCon notation ĠFrames Ġlex icon , Ġparticularly Ġfor Ġhomosexual , Ġwhich Ġis Ġconsiderably Ġless Ġfrequent Ġthan Ġgay Ġor Ġother ĠLGBTQ Ġterms Ġin Ġlater Ġyears . ĠIn Ġextracting Ġsubject - verb Ġpairs Ġfor Ġthis Ġanalysis , Ġwe Ġdecided Ġto Ġmaximize Ġprecision Ġby Ġextracting Ġonly Ġthe Ġnoun s Ġand Ġtheir Ġimmediate Ġadject ival Ġmodifiers , Ġwhich Ġlimited Ġthe Ġamount Ġof Ġdata . ĠFor Ġvisualization Ġpurposes , Ġwe Ġthus Ġshow Ġaverage Ġagency Ġover Ġfive - year Ġintervals . </s> Overall , ĠWil co xon Ġsigned - rank Ġtests Ġon Ġthe Ġmeans Ġfor Ġall Ġgroup Ġlabels Ġfor Ġeach Ġof Ġthe Ġ30 Ġyears Ġindicates Ġthat Ġgay Ġoccurs Ġin Ġcontexts Ġwith Ġsignificantly Ġhigher Ġagency Ġthan Ġhomosexual Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠAll Ġfour Ġgroup Ġlabels Ġactually Ġsignificantly Ġdecrease Ġin Ġagency Ġover Ġtime Ġaccording Ġto Ġlinear Ġregress ions Ġover Ġall Ġ30 Ġyears Ġ($ p Ġ< Ġ0 . 001 $ ), Ġbut Ġthe Ġslope Ġfor Ġhomosexual Ġis Ġmuch Ġgreater Ġ( Ġ$ m =( - 7 . 9 \ pm Ġ1 . 3 )\ times Ġ10 ^{ - 3 } $ Ġfor Ġhomosexual , Ġcompared Ġto Ġ$ m =( - 3 . 9 \ pm Ġ. 55 )\ times Ġ10 ^{ - 3 } $ Ġfor Ġgay , Ġand Ġ$ m =( - 1 . 5 \ pm Ġ. 46 )\ times Ġ10 ^{ - 3 } $ Ġfor Ġall ĠLGBTQ Ġterms ). ĠFurthermore Ġin Ġthe Ġmost Ġrecent Ġ15 Ġyears , Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġshow Ġno Ġsignificant Ġchange Ġ($ p Ġ= Ġ0 . 09 7 $ Ġfor Ġgay Ġand Ġ$ p Ġ= Ġ0 . 14 $ Ġfor Ġall ĠLGBTQ Ġterms ), Ġbut Ġhomosexual Ġstill Ġdecreases Ġsignificantly Ġin Ġagency Ġ($ p Ġ< Ġ0 . 05 $ ). </s> Figure ĠFIG REF 45 Ġsuggests Ġthat ĠLGBTQ Ġgroups Ġexperience Ġgreater Ġdenial Ġof Ġagency Ġin Ġthe ĠNew ĠYork ĠTimes Ġthan Ġthe Ġinstitution 's Ġin - group Ġidentifier ĠAmerican . ĠFurthermore , Ġpeople Ġdescribed Ġas Ġhomosexual Ġexperience Ġeven Ġmore Ġdenial Ġof Ġagency Ġthan Ġpeople Ġwho Ġare Ġdescribed Ġas Ġgay , Ġillustrating Ġhow Ġthese Ġtwo Ġterms Ġdiffer Ġwith Ġrespect Ġto Ġdehuman ization . ĠUnlike Ġthe Ġimproving Ġattitudes Ġindicated Ġby Ġour Ġanalysis Ġof Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup , Ġit Ġappears Ġthat Ġdenial Ġof Ġagency Ġincreased Ġover Ġtime Ġfor Ġall ĠLGBTQ Ġgroups . ĠHowever , Ġthe Ġrelatively Ġrapid Ġchange Ġin Ġagency Ġfor Ġhomosexual Ġis Ġconsistent Ġwith Ġour Ġother Ġresults Ġof Ġthis Ġlabel 's Ġpe j oration . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠDen ial Ġof ĠAgency Ġ: :: ĠQual itative ĠAnalysis </s> We Ġagain Ġqual itatively Ġinvestigate Ġthe Ġlabels Ġassigned Ġby Ġthis Ġtechnique Ġfor Ġa Ġsample Ġof Ġparagraphs . ĠIn Ġgeneral , Ġthe Ġbinary Ġlabels Ġfor Ġpositive Ġand Ġnegative Ġagency Ġappear Ġto Ġbe Ġreasonably Ġaccurate , Ġas Ġshown Ġby Ġthe Ġfirst Ġfour Ġexample Ġin ĠTable ĠT AB REF 47 . ĠVer bs Ġthat Ġattribute Ġhigh Ġagency Ġto Ġthe Ġsubject Ġinclude Ġdevelop Ġand Ġendorse , Ġsuggesting Ġthat Ġthe ĠLGBTQ - aligned Ġsubjects Ġare Ġin Ġcontrol Ġand Ġactively Ġmaking Ġtheir Ġown Ġdecisions . ĠOn Ġthe Ġother Ġend , ĠLGBTQ Ġpeople Ġare Ġgiven Ġlow Ġagency Ġwhen Ġpresented Ġas Ġthe Ġsubjects Ġof Ġpassive Ġverbs Ġsuch Ġas Ġface Ġand Ġacknowledge . </s> The ĠCon notation ĠFrames ĠLex icon Ġlabels Ġfor Ġagency Ġseem Ġto Ġappropriately Ġdescribe ĠLGBTQ Ġpeople 's Ġagency Ġespecially Ġaccurately Ġfor Ġlow Ġagency ; Ġwe Ġcould Ġnot Ġreadily Ġidentify Ġcount ere x amples Ġin Ġour Ġsample Ġwhere ĠLGBTQ Ġpeople Ġactually Ġwere Ġportrayed Ġwith Ġhigh Ġagency Ġbut Ġwere Ġlabeled Ġwith Ġlow Ġagency . ĠHowever , Ġwe Ġfound Ġseveral Ġmis character izations Ġin Ġwhich ĠLGBTQ Ġpeople Ġwere Ġlabeled Ġas Ġhaving Ġa Ġhigh Ġdegree Ġof Ġagency Ġbut Ġdo Ġnot Ġqual itatively Ġseem Ġto Ġbe Ġportrayed Ġas Ġparticularly Ġagent ive Ġor Ġin Ġcontrol Ġof Ġtheir Ġown Ġactions . ĠThis ĠCon notation ĠFrames Ġtechnique Ġconsidered Ġthe Ġexample Ġbelow Ġto Ġattribute Ġhigh Ġagency Ġto ĠLGBTQ Ġpeople Ġbecause Ġthe Ġword Ġhomosexual Ġappeared Ġin Ġthe Ġsubject Ġof Ġthe Ġhigh - agency Ġverb Ġviolate ; Ġhowever , Ġhomosexual Ġactually Ġmod ifies Ġrelationships , Ġnot Ġpeople Ġthemselves . ĠFurthermore , Ġthis Ġdebate Ġwithin Ġreligion Ġappears Ġto Ġbe Ġdevoid Ġof Ġinput Ġfrom ĠLGBTQ Ġpeople Ġand Ġthus Ġdoes Ġnot Ġportray Ġthem Ġas Ġparticularly Ġagent ive . </s> At Ġthe Ġsame Ġtime , Ġit Ġundersc ored Ġa Ġstark Ġdivision Ġin ĠJudaism Ġover Ġthe Ġplace Ġof Ġhomosexuals Ġin Ġsociety . ĠOrthodox Ġrabb inical Ġgroups Ġbelieve Ġthat Ġhomosexual Ġrelationships Ġviolate ĠJewish Ġlaw ... Ġ( 1996 ) </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠDomin ance Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 51 Ġshows Ġthe Ġaverage Ġdominance Ġof Ġeach Ġgroup Ġlabel 's Ġword 2 vec Ġrepresentation 's Ġ1000 Ġnearest Ġneighbors Ġaccording Ġto Ġthe ĠN RC ĠV AD ĠDomin ance ĠLex icon ĠB IB REF 56 , Ġwhich Ġwe Ġuse Ġas Ġan Ġestimate Ġof Ġthe Ġdominance Ġassociated Ġwith Ġeach Ġgroup Ġlabel . ĠAmerican Ġis Ġclearly Ġsignificantly Ġassociated Ġwith Ġgreater Ġdominance Ġthan Ġgay , Ġhomosexual , Ġand Ġall ĠLGBTQ Ġterms Ġ( Wil co xon Ġsigned - rank Ġtest ; Ġ$ p Ġ< Ġ0 . 0001 $ ), Ġand Ġgay Ġhas Ġsignificantly Ġhigher Ġdominance Ġthan Ġhomosexual Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠWhile Ġthe Ġdominance Ġassociated Ġwith Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġsignificantly Ġincreased Ġover Ġthe Ġthree Ġdecades Ġ($ p Ġ< Ġ0 . 01 $ ), Ġdominance Ġsignificantly Ġdecreased Ġfor Ġhomosexual Ġ($ p Ġ< Ġ0 . 0001 $ ). ĠFurthermore , Ġwhile Ġthe Ġincrease Ġin Ġaverage Ġnearest Ġneighbor Ġdominance Ġdoes Ġnot Ġreach Ġsignificance Ġfor Ġgay Ġand Ġall ĠLGBTQ Ġterms Ġin Ġthe Ġmost Ġrecent Ġ15 Ġyears Ġ($ p Ġ= Ġ0 . 47 $ Ġfor Ġall ĠLGBTQ Ġterms Ġand Ġ$ p Ġ= Ġ0 . 0 66 $ Ġfor Ġgay ), Ġhomosexual Ġsignificantly Ġdecreases Ġin Ġthat Ġtime Ġspan Ġ($ p Ġ< Ġ0 . 0001 $ ). </s> Even Ġthough Ġdominance Ġmay Ġmore Ġdirectly Ġencode Ġpower Ġrather Ġthan Ġagency , Ġthe ĠN RC ĠV AD ĠDomin ance ĠLex icon Ġis Ġa Ġuseful Ġresource Ġfor Ġoperational izing Ġdenial Ġof Ġagency Ġbecause Ġof Ġthe Ġclose Ġrelationship Ġbetween Ġthese Ġconcepts . ĠAs Ġwith Ġthe ĠCon notation ĠFrames ' Ġagency Ġmeasurements Ġshown Ġin ĠFigure ĠFIG REF 45 , Ġour Ġanalysis Ġof Ġthe Ġdominance Ġof Ġeach Ġlabel 's Ġnearest Ġword 2 vec Ġneighbors Ġsuggest Ġthat ĠLGBTQ Ġgroups Ġexperience Ġgreater Ġdenial Ġof Ġagency Ġthan Ġthe ĠNew ĠYork ĠTimes 's Ġin - group ĠAmerican . ĠBoth Ġtechniques Ġshow Ġdifferences Ġbetween Ġthe Ġlabels Ġgay Ġand Ġhomosexual , Ġwhere Ġhomosexual Ġis Ġconsistently Ġassociated Ġwith Ġlower Ġagency Ġthan Ġgay Ġand Ġfurther Ġdecreases Ġover Ġtime . ĠHowever , Ġthese Ġtwo Ġmeasurements Ġsuggest Ġslightly Ġdifferent Ġtemporal Ġdynamics Ġfor Ġthe Ġdenial Ġof Ġagency Ġof ĠLGBTQ Ġpeople Ġin Ġthe Ġearliest Ġhalf Ġof Ġthe Ġdata ; Ġwe Ġobserve Ġa Ġdecrease Ġin Ġagency Ġfor ĠLGBTQ Ġgroups Ġwith ĠCon notation ĠFrames , Ġbut Ġa Ġslight Ġincrease Ġwith Ġnearest Ġneighbor Ġdominance . </s> Results Ġ: :: ĠNegative ĠEvaluation ĠTowards ĠTarget ĠGroup Ġ: :: ĠWord ĠEmb edd ing ĠNeighbor ĠDomin ance Ġ: :: ĠQual itative ĠAnalysis </s> Using Ġthe ĠV AD ĠDomin ance ĠLex icon Ġto Ġcalculate Ġaverage Ġdominance Ġof Ġeach Ġsocial Ġgroup Ġlabel Ġappears Ġto Ġcorrespond Ġwell Ġto Ġour Ġnotion Ġof Ġdenial Ġof Ġagency . ĠBecause Ġgay 's Ġnearest Ġneighbors Ġhave Ġa Ġmuch Ġhigher Ġaverage Ġdominance Ġthan Ġhomosexual 's Ġfor Ġmost Ġyears , Ġwe Ġqual itatively Ġevaluate Ġthis Ġtechnique Ġby Ġcomparing Ġwords Ġthat Ġappear Ġas Ġa Ġnearby Ġneighbor Ġfor Ġgay Ġand Ġnot Ġhomosexual Ġfor Ġa Ġgiven Ġyear , Ġand Ġvice Ġversa . ĠWords Ġthat Ġappear Ġwithin Ġthe Ġ1000 Ġwords Ġnearest Ġto Ġgay Ġand Ġnot Ġhomosexual Ġin Ġthe Ġ2015 Ġword 2 vec Ġspace Ġinclude Ġhigh - agency Ġwords Ġsuch Ġas Ġbrave , Ġfreedom , Ġand Ġadvocate , Ġwhich Ġhave Ġdominance Ġscores Ġof Ġ0 . 9 17 , Ġ0 . 905 , Ġand Ġ0 . 8 18 Ġrespectively Ġaccording Ġto Ġthe ĠV AD Ġlex icon . ĠOn Ġthe Ġother Ġhand , Ġwords Ġnearby Ġto Ġhomosexual Ġand Ġnot Ġgay Ġin Ġ2015 Ġinclude Ġlow - agency Ġwords Ġsuch Ġas Ġsub missive Ġ( 0 . 173 ), Ġunprotected Ġ( 0 . 197 ), Ġand Ġignorant Ġ( 0 . 223 ). ĠWe Ġfind Ġsimilar Ġresults Ġcomparing Ġgay Ġand Ġhomosexual Ġin Ġthe Ġword 2 vec Ġmodel Ġtrained Ġon Ġ1986 Ġdata ; Ġhigh - agency Ġwords Ġsuch Ġas Ġliberation Ġ( 0 . 8 57 ) Ġand Ġmobilization Ġ( 0 . 787 ) Ġare Ġnearby Ġneighbors Ġfor Ġgay Ġand Ġnot Ġhomosexual , Ġwhile Ġlow - agency Ġwords Ġsuch Ġas Ġdepressive Ġ( 0 . 202 ) Ġand Ġinvoluntary Ġ( 0 . 231 ) Ġare Ġnearby Ġneighbors Ġfor Ġhomosexual Ġand Ġnot Ġgay . </s> We Ġadditionally Ġqual itatively Ġinvestigate Ġthe Ġvector Ġspace Ġmodels Ġcorresponding Ġto Ġseveral Ġout lier Ġyears . ĠIn Ġ1993 , Ġhomosexual 's Ġneighbors Ġhave Ġby Ġfar Ġthe Ġhighest Ġaverage Ġdominance Ġcompared Ġto Ġany Ġother Ġyear Ġfor Ġthis Ġlabel . ĠSimilar Ġto Ġwhat Ġwe Ġsaw Ġwith Ġneighbor Ġval ence , Ġthis Ġis Ġlikely Ġdue Ġto Ġa Ġlarge Ġvolume Ġof Ġdiscourse Ġabout Ġthe Ġmilitary Ġin Ġdebates Ġsurrounding Ġthe ĠâĢ ľ Don 't ĠAsk , ĠDon 't ĠTell " Ġlegislation . ĠSome Ġhigh - dom inance Ġwords Ġthat Ġare Ġunique Ġto Ġhomosexual 's Ġnearest Ġneighbors Ġin Ġ1993 Ġinclude Ġdirective Ġ( 0 . 9 18 ), Ġforce Ġ( 0 . 905 ), Ġenforce Ġ( 0 . 8 36 ) Ġand Ġtroops Ġ( 0 . 804 ). ĠAlso Ġsimilar Ġto Ġthe Ġneighbor Ġval ence Ġresults , Ġgay 's Ġneighbors ' Ġin Ġ1999 Ġhave Ġthe Ġlowest Ġaverage Ġdominance Ġthan Ġany Ġother Ġyear . ĠThis Ġcould Ġalso Ġlikely Ġbe Ġconnected Ġto ĠMatthew ĠShepard 's Ġdeath Ġand Ġthe Ġsubsequent Ġoutrage ; Ġunique Ġneighbors Ġfor Ġgay Ġin Ġ1999 Ġinclude Ġdisgrace ful Ġ( 0 . 240 ), Ġirresponsible Ġ( 0 . 241 ), Ġunethical Ġ( 0 . 269 ), Ġimprisoned Ġ( 0 . 302 ), Ġand Ġslander ous Ġ( 0 . 337 ). </s> Results Ġ: :: ĠMoral ĠDis g ust Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 55 Ġshows Ġthe Ġchanging Ġrelationships Ġbetween Ġall ĠLGBTQ Ġterms , Ġgay , Ġhomosexual Ġand Ġthe Ġdehuman izing Ġconcept Ġof ĠMoral ĠDis g ust . ĠBecause Ġthe Ġcos ine Ġdistance Ġbetween ĠAmerican Ġand ĠMoral ĠDis g ust Ġis Ġsignificantly Ġgreater Ġover Ġall Ġyears Ġthan Ġany ĠLGBTQ Ġrepresentation Ġ( Wil co xon Ġsigned - rank Ġtest ; Ġ$ p Ġ< Ġ0 . 0001 $ ), ĠAmerican Ġis Ġthe Ġgroup Ġlabel Ġleast Ġassociated Ġwith ĠMoral ĠDis g ust . ĠFurthermore , Ġthe Ġcos ine Ġdistance Ġbetween Ġgay Ġand ĠMoral ĠDis g ust Ġis Ġsignificantly Ġgreater Ġthan Ġthe Ġdistance Ġbetween Ġhomosexual Ġand ĠMoral ĠDis g ust Ġfor Ġevery Ġyear Ġ($ p Ġ< Ġ0 . 0001 $ ), Ġindicating Ġthat Ġhomosexual Ġis Ġmore Ġclosely Ġassociated Ġwith ĠMoral ĠDis g ust Ġthan Ġgay Ġis . ĠLinear Ġregression Ġanalyses Ġshow Ġthat ĠAmerican Ġsignificantly Ġdecreases Ġin Ġcos ine Ġdistance Ġover Ġthe Ġthirty Ġyears Ġ($ p Ġ< Ġ0 . 0001 $ ), Ġwhile Ġall ĠLGBTQ Ġterms Ġand Ġgay Ġsignificantly Ġincrease Ġin Ġcos ine Ġdistance Ġ($ p Ġ< Ġ0 . 0001 $ ), Ġindicated Ġweakening Ġassociations Ġbetween ĠLGBTQ Ġpeople Ġand Ġmoral Ġdisgust Ġover Ġtime . ĠUnlike Ġthe Ġaggreg ated Ġrepresentation Ġfor Ġall ĠLGBTQ Ġlabels Ġand Ġgay , Ġthe Ġincreasing Ġcos ine Ġdistance Ġbetween Ġhomosexual Ġand ĠMoral ĠDis g ust Ġdoes Ġnot Ġchange Ġsignificantly Ġover Ġtime Ġ($ p Ġ= Ġ0 . 54 $ ), Ġbut Ġdoes Ġsignificantly Ġdecrease Ġin Ġthe Ġlast Ġ15 Ġyears Ġ($ p Ġ< Ġ0 . 05 $ ). </s> Overall , Ġthese Ġmeasurements Ġof Ġassociations Ġbetween ĠLGBTQ Ġpeople Ġand ĠMoral ĠDis g ust Ġare Ġlargely Ġconsistent Ġwith Ġthe Ġother Ġoperational izations Ġof Ġdehuman ization . ĠAll ĠLGBTQ Ġgroup Ġlabels Ġare Ġmore Ġclosely Ġassociated Ġwith ĠMoral ĠDis g ust Ġthan Ġthe Ġnewspaper 's Ġin - group Ġterm ĠAmerican , Ġbut Ġthese Ġassociations Ġweaken Ġover Ġtime Ġwhich Ġsuggests Ġincreased Ġhuman ization . ĠNotably , Ġthe Ġterm Ġhomosexual Ġhas Ġalways Ġbeen Ġmore Ġassociated Ġwith ĠMoral ĠDis g ust Ġthan Ġthe Ġden otation ally - similar Ġterm Ġgay , Ġand Ġhomosexual Ġactually Ġbecomes Ġmore Ġclosely Ġassociated Ġwith Ġthis Ġdehuman izing Ġconcept Ġin Ġrecent Ġyears . </s> Results Ġ: :: ĠMoral ĠDis g ust Ġ: :: ĠQual itative ĠAnalysis </s> From Ġinvestigating Ġword Ġembed ding Ġneighbors Ġin ĠTable ĠT AB REF 23 , Ġwe Ġsaw Ġthat Ġhomosexual Ġhas Ġbecome Ġincreasingly Ġassociated Ġwith Ġimmoral Ġconcepts , Ġsuggesting Ġthat Ġmoral Ġdisgust Ġis Ġa Ġmechanism Ġby Ġwhich ĠLGBTQ Ġpeople Ġare Ġdehuman ized . ĠAlthough Ġrarely Ġimplicitly Ġinvoked , Ġthe Ġconnection Ġbetween ĠLGBTQ Ġpeople Ġand Ġdisgust Ġis Ġsupported Ġby Ġseveral Ġexamples Ġfrom Ġthe Ġdata , Ġas Ġshown Ġin ĠTable ĠT AB REF 57 . ĠThe Ġmost Ġdramatic Ġchange Ġover Ġtime Ġoccurs Ġin Ġthe Ġlate Ġ1980 s Ġand Ġearly Ġ1990 s , Ġwhen Ġgay , Ġhomosexual , Ġand Ġall ĠLGBTQ Ġterms Ġbecome Ġrapidly Ġless Ġassociated Ġwith ĠMoral ĠDis g ust Ġbased Ġon Ġtheir Ġincreasing Ġcos ine Ġdistance . ĠBecause Ġa Ġlot Ġof Ġdisease - related Ġwords Ġare Ġincluded Ġin Ġthe ĠMoral ĠDis g ust Ġcomponent Ġof Ġthe ĠMoral ĠFound ations ĠDictionary ĠB IB REF 64 , Ġthis Ġis Ġlikely Ġdue Ġto Ġdecreasing Ġattention Ġto Ġthe ĠAIDS Ġepidemic . </s> Results Ġ: :: ĠVer min Ġas Ġa ĠDe human izing ĠMet aph or Ġ: :: ĠQuant itative ĠResults </s> Figure ĠFIG REF 60 Ġshows Ġthe Ġrelationships Ġbetween ĠLGBTQ Ġlabels Ġ( and ĠAmerican ) Ġand Ġthe Ġdehuman izing Ġver min Ġmetaphor , Ġquant ified Ġas Ġthe Ġcos ine Ġdistance Ġbetween Ġthe Ġlabels ' Ġword 2 vec Ġvectors Ġand Ġa ĠVer min Ġconcept Ġvector Ġrepresentation , Ġcalculated Ġas Ġthe Ġcent roid Ġof Ġmultiple Ġver min - related Ġwords . ĠAs Ġwith ĠMoral ĠDis g ust Ġin ĠFigure ĠFIG REF 55 , Ġthe Ġin - group Ġterm ĠAmerican Ġis Ġsignificantly Ġfurther Ġaway Ġfrom ĠVer min Ġover Ġall Ġyears Ġthan Ġany Ġof Ġthe ĠLGBTQ Ġrepresentations Ġ( Wil co xon Ġsigned - rank Ġtest ; Ġ$ p Ġ< Ġ0 . 0001 $ ). ĠThe Ġcos ine Ġdistance Ġbetween Ġgay Ġand ĠVer min Ġis Ġalso Ġsignificantly Ġgreater Ġthan Ġthe Ġdistance Ġbetween Ġhomosexual Ġand ĠVer min Ġ($ p Ġ< Ġ0 . 0001 $ ), Ġindicating Ġthat Ġhomosexual Ġis Ġmore Ġclosely Ġassociated Ġwith Ġthe Ġdehuman izing Ġver min Ġmetaphor Ġthan Ġgay Ġis . ĠFurthermore , Ġwhile Ġall ĠLGBTQ Ġterms Ġand Ġgay Ġsignificantly Ġincrease Ġin Ġcos ine Ġdistance Ġ( bec ome Ġless Ġassociated Ġwith ĠVer min ) Ġover Ġtime Ġ( linear Ġregression ; Ġ$ p Ġ< Ġ0 . 0001 $ ), Ġhomosexual Ġslightly Ġdecreases Ġin Ġcos ine Ġdistance Ġ( great er Ġassociation Ġwith ĠVer min ), Ġbut Ġthis Ġdecrease Ġdoes Ġnot Ġreach Ġsignificance Ġ($ p Ġ= Ġ0 . 13 $ ). </s> Our Ġmeasurement Ġof Ġthe Ġimplicit Ġver min Ġmetaphor Ġshows Ġa Ġsimilar Ġpattern Ġas Ġour Ġmeasurements Ġfor Ġmoral Ġdisgust , Ġdenial Ġof Ġagency , Ġand Ġnegative Ġevaluations Ġof Ġthe Ġtarget Ġgroup . ĠOverall , ĠLGBTQ Ġgroups Ġare Ġmore Ġimplicitly Ġassociated Ġwith Ġver min Ġthan Ġthe Ġcomparison Ġsocial Ġgroup ĠAmerican , Ġbut Ġthis Ġassociation Ġis Ġweakening Ġover Ġtime , Ġsuggesting Ġincreased Ġhuman ization . ĠUnlike Ġthe Ġlabels Ġgay Ġand Ġthe Ġaggreg ated Ġrepresentation Ġof Ġall ĠLGBTQ Ġterms , Ġthe Ġassociation Ġbetween Ġver min Ġand Ġthe Ġlabel Ġhomosexual Ġdoes Ġnot Ġsignificantly Ġchange Ġover Ġtime . </s> Results Ġ: :: ĠVer min Ġas Ġa ĠDe human izing ĠMet aph or Ġ: :: ĠQual itative ĠAnalysis </s> Met aph ors Ġcomparing Ġhumans Ġto Ġver min Ġhave Ġbeen Ġespecially Ġprominent Ġin Ġdehuman izing Ġgroups Ġthroughout Ġhistory ĠB IB REF 4 , ĠB IB REF 15 . ĠAlthough Ġno ĠNew ĠYork ĠTimes Ġwriters Ġdirectly Ġcompare ĠLGBTQ Ġpeople Ġto Ġver min , Ġthis Ġmetaphor Ġmay Ġbe Ġinvoked Ġin Ġmore Ġsubtle Ġways . ĠFurthermore , Ġseveral Ġparagraphs Ġin Ġthe Ġdata Ġdemonstrate Ġthat Ġpeople Ġexplicitly Ġmention Ġthe Ġmetaphor Ġin Ġorder Ġto Ġargue Ġagainst Ġit . ĠThese Ġexamples , Ġshown Ġin ĠTable ĠT AB REF 62 , Ġfurther Ġpoint Ġto Ġthe Ġexistence Ġof ĠLGBTQ Ġpeople - as - ver min Ġmetaphors , Ġeven Ġif Ġthey Ġare Ġnot Ġfrequently Ġdirectly Ġinvoked . </s> Discussion </s> Our Ġframework Ġfor Ġthe Ġcomputational Ġlinguistic Ġanalysis Ġof Ġdehuman ization Ġinvolves Ġidentifying Ġmajor Ġdimensions Ġof Ġdehuman ization Ġfrom Ġsocial Ġpsychology Ġliterature , Ġproposing Ġlinguistic Ġcorrelates Ġfor Ġeach Ġdimension , Ġand Ġdeveloping Ġrobust Ġand Ġinterpret able Ġcomputational Ġmethods Ġto Ġquantify Ġthese Ġcorrelates . ĠWe Ġapplied Ġthis Ġframework Ġto Ġstudy Ġthe Ġdehuman ization Ġof ĠLGBTQ Ġpeople Ġin Ġthe ĠNew ĠYork ĠTimes Ġover Ġthe Ġcourse Ġof Ġ30 Ġyears , Ġfrom Ġ1986 Ġto Ġ2015 . ĠWe Ġmeasured Ġfour Ġdimensions Ġof Ġdehuman ization : Ġnegative Ġevaluations Ġof Ġthe Ġtarget Ġgroup , Ġdenial Ġof Ġagency , Ġmoral Ġdisgust , Ġand Ġ( impl icit ) Ġinv ocations Ġof Ġver min Ġmetaphors . ĠOverall , Ġwe Ġfound Ġincreasingly Ġhuman izing Ġdescriptions Ġof ĠLGBTQ Ġpeople Ġover Ġtime . ĠLGBTQ Ġpeople Ġhave Ġbecome Ġmore Ġassociated Ġwith Ġpositive Ġemotional Ġlanguage , Ġsuggesting Ġthat Ġnegative Ġevaluations Ġof Ġthe Ġtarget Ġgroup Ġhave Ġdiminished . ĠLGBTQ Ġpeople Ġhave Ġbecome Ġmore Ġassociated Ġwith Ġhigher - dom inance Ġwords , Ġsuggesting Ġdecreasing Ġdenial Ġof Ġagency , Ġalthough Ġthis Ġfinding Ġwas Ġnot Ġreplicated Ġwith Ġthe Ġverb - centric ĠâĢ ľ Con notation ĠFrames " Ġmeasurement . ĠFurthermore , Ġlabels Ġfor ĠLGBTQ Ġpeople Ġhave Ġmoved Ġfurther Ġaway Ġfrom Ġthe Ġconcepts Ġof Ġmoral Ġdisgust Ġand Ġver min Ġwithin Ġdistribution al Ġsemantic Ġrepresentations , Ġsuggesting Ġthat Ġharmful Ġassociations Ġbetween ĠLGBTQ Ġpeople Ġand Ġthese Ġdehuman izing Ġconcepts Ġhave Ġweakened Ġover Ġtime . </s> Despite Ġthese Ġbroad Ġtrends , Ġwe Ġfound Ġthat Ġthe Ġlabels Ġgay Ġand Ġhomosexual Ġexhibit Ġstrikingly Ġdifferent Ġpatterns Ġamong Ġthese Ġfour Ġcomponents Ġof Ġdehuman ization . ĠHom osexual Ġis Ġassociated Ġwith Ġmore Ġnegative Ġ( low Ġval ence ) Ġlanguage Ġthan Ġgay , Ġsuggesting Ġthat Ġthere Ġare Ġmore Ġnegative Ġevaluations Ġof Ġpeople Ġdescribed Ġas Ġhomosexual Ġthan Ġgay . ĠThe Ġlabel Ġhomosexual Ġis Ġalso Ġassociated Ġwith Ġgreater Ġdenial Ġof Ġagency , Ġand Ġhas Ġstronger Ġconnections Ġto Ġmoral Ġdisgust Ġand Ġver min Ġthan Ġgay Ġdoes . ĠFurthermore , Ġunlike Ġfor ĠLGBTQ Ġlabels Ġas Ġa Ġwhole , Ġdiscussions Ġof Ġhomosexual Ġpeople Ġdo Ġnot Ġseem Ġto Ġhave Ġbecome Ġmore Ġhuman izing Ġover Ġtime . ĠOn Ġthe Ġcontrary , Ġseveral Ġtechniques Ġsuggested Ġthat Ġlanguage Ġused Ġalongside Ġthe Ġlabel Ġhomosexual Ġhas Ġactually Ġbecome Ġmore Ġdehuman izing Ġafter Ġabout Ġ2000 . ĠThrough Ġits Ġrepeated Ġuse Ġin Ġthese Ġcontexts , Ġthe Ġuse Ġof Ġthe Ġword Ġhomosexual Ġappears Ġto Ġhave Ġemerged Ġas Ġan Ġindex Ġof Ġmore Ġdehuman izing Ġattitudes Ġtowards ĠLGBTQ Ġpeople Ġthan Ġother Ġlabels . ĠDespite Ġthe Ġden ot ational Ġsimilarity Ġbetween Ġhomosexual Ġand Ġgay , Ġthese Ġcomputational Ġtechniques Ġcan Ġtrack Ġthe Ġstark Ġdifferences Ġand Ġdiver g ences Ġin Ġsocial Ġmeanings . </s> In Ġthis Ġwork , Ġwe Ġrestricted Ġour Ġanalysis Ġto Ġthe Ġlex ical Ġlevel Ġfor Ġease Ġof Ġinterpret ability , Ġand Ġlever aged Ġa Ġdiverse Ġarray Ġof Ġexisting Ġresources , Ġincluding Ġthe ĠN RC ĠV AD Ġlex icon ĠB IB REF 56 , ĠCon notation ĠFrames Ġlex icons ĠB IB REF 22 , ĠB IB REF 25 , Ġand Ġthe ĠMoral ĠFound ations ĠDictionary ĠB IB REF 64 . ĠFor Ġnegative Ġevaluations Ġof Ġa Ġtarget Ġgroup Ġand Ġdenial Ġof Ġagency , Ġwe Ġpropose Ġmultiple Ġdifferent Ġtechniques Ġthat Ġvary Ġin Ġaccuracy Ġand Ġinterpret ability . ĠWhile Ġword - count ing Ġmethods Ġare Ġoften Ġinaccurate Ġdue Ġto Ġtheir Ġsimplicity , Ġtheir Ġresults Ġcan Ġbe Ġeasily Ġunderstood , Ġwhile Ġembed ding - based Ġmethods Ġtend Ġto Ġsuffer Ġthe Ġopposite Ġproblem . ĠWe Ġdo Ġnot Ġclaim Ġto Ġhave Ġfound Ġthe Ġoptimal Ġbalance Ġof Ġmodel Ġquality Ġand Ġinterpret ability Ġin Ġthis Ġstudy , Ġbut Ġcarefully Ġconsidering Ġthis Ġtrade off Ġis Ġespecially Ġimportant Ġin Ġwork Ġthat Ġseeks Ġto Ġcharacterize Ġcomplex Ġand Ġsensitive Ġsocial Ġphenomena Ġsuch Ġas Ġdehuman ization . </s> Discussion Ġ: :: ĠLim itations Ġand ĠFuture ĠWork </s> As Ġthe Ġfirst Ġattempt Ġto Ġcomput ationally Ġanalyze Ġdehuman ization , Ġthis Ġwork Ġhas Ġmany Ġlimitations . ĠWhile Ġour Ġqualitative Ġanalysis Ġdemonstrates Ġthat Ġthe Ġproposed Ġtechniques Ġcapture Ġsome Ġlingu istics Ġsignals Ġof Ġdehuman ization , Ġother Ġaspects Ġof Ġthe Ġdata Ġerrone ously Ġimpact Ġour Ġmeasurements Ġas Ġwell . ĠFor Ġexample , Ġwe Ġstill Ġcannot Ġentirely Ġdis ent angle Ġa Ġjournalist 's Ġperspective Ġfrom Ġthe Ġtopics Ġand Ġevents Ġin Ġthe Ġstories Ġthey Ġdiscuss . ĠThese Ġtechniques Ġwould Ġpredict Ġthat Ġarticles Ġemphasizing Ġanti - LGBT Q Ġviolence Ġare Ġdehuman izing ; Ġin Ġreality , Ġthese Ġstories Ġmay Ġexpress Ġdehuman izing Ġattitudes Ġbut Ġthose Ġviews Ġare Ġnot Ġnecessarily Ġheld Ġby Ġthe Ġjournalist . ĠAn Ġexciting Ġarea Ġof Ġfuture Ġwork Ġcould Ġinvolve Ġdeveloping Ġmore Ġsophisticated Ġcomputational Ġmethods Ġthat Ġcan Ġbetter Ġdis amb ig uate Ġthe Ġwriter 's Ġattitudes , Ġattitudes Ġof Ġpeople Ġmentioned Ġin Ġthe Ġtext , Ġand Ġtopics Ġand Ġevents , Ġwhile Ġrecognizing Ġthat Ġeach Ġof Ġthese Ġcould Ġcontribute Ġto Ġthe Ġoverall Ġrepresentation Ġof Ġmarginalized Ġgroups Ġin Ġthe Ġmedia . ĠFurthermore , Ġthe Ġpresent Ġwork Ġuses Ġword 2 vec Ġsince Ġall Ġknown Ġaffect ive Ġlex icons Ġare Ġtype - level , Ġbut Ġcontextual ized Ġembed ding - based Ġmethods Ġhave Ġgreat Ġpotential Ġfor Ġmore Ġnuanced Ġanalyses Ġof Ġdehuman izing Ġlanguage Ġby Ġleveraging Ġtoken - level Ġrepresentations ĠB IB REF 72 , ĠB IB REF 73 . </s> Another Ġlimitation Ġof Ġthis Ġstudy Ġis Ġthe Ġlack Ġof Ġground Ġtruth Ġlabels Ġfor Ġwhat Ġcontent Ġis Ġdehuman izing , Ġwhich Ġhas Ġprevented Ġus Ġfrom Ġdeveloping Ġsupervised Ġmethods Ġand Ġquantitative Ġevaluations . ĠWhile Ġit Ġis Ġchallenging Ġto Ġdirectly Ġsolicit Ġopinions Ġfrom Ġhuman Ġannot ators Ġabout Ġsuch Ġa Ġcomplex Ġand Ġsubjective Ġconcept Ġsuch Ġas Ġdehuman ization , Ġannotation Ġstrategies Ġsuch Ġas Ġbest - worst Ġscaling Ġcan Ġbe Ġpromising Ġapproaches ĠB IB REF 74 , Ġparticularly Ġif Ġthey Ġprimarily Ġconsider Ġopinions Ġfrom Ġmembers Ġof Ġthe Ġsocial Ġgroups Ġof Ġinterest . ĠThese Ġground - truth Ġlabels Ġcould Ġenable Ġfuture Ġwork Ġto Ġdevelop Ġmodels Ġto Ġun ify Ġall Ġof Ġthese Ġdistinct Ġcomponents Ġof Ġdehuman ization Ġinto Ġa Ġsingle Ġmeasurement Ġfor Ġa Ġgiven Ġtext . </s> Our Ġframework Ġcould Ġalso Ġbe Ġgreatly Ġexpanded Ġto Ġinclude Ġmore Ġinsights Ġfrom Ġdehuman ization Ġtheory . ĠBeyond Ġthe Ġfour Ġelements Ġof Ġdehuman ization Ġthat Ġwe Ġdiscuss Ġin Ġthis Ġarticle , Ġsocial Ġpsychology Ġresearch Ġhas Ġidentified Ġmany Ġother Ġcognitive Ġprocesses Ġthat Ġcontribute Ġto Ġdehuman ization , Ġsuch Ġas Ġpsychological Ġdist ancing , Ġessential ism Ġ( the Ġperception Ġthat Ġthe Ġtarget Ġgroup Ġhas Ġsome Ġessence Ġthat Ġmakes Ġthem Ġcategor ically Ġand Ġfundamentally Ġdifferent ), Ġand Ġdenial Ġof Ġsubject ivity Ġ( neg lect Ġof Ġa Ġtarget Ġgroup Ġmember 's Ġpersonal Ġfeelings Ġand Ġexperiences ). ĠB IB REF 75 , ĠB IB REF 21 , ĠB IB REF 76 , ĠB IB REF 3 . ĠScholars Ġalso Ġoften Ġdifferentiate Ġbetween Ġtwo Ġforms Ġof Ġdehuman ization , Ġanimal istic Ġ( lik ening Ġhumans Ġto Ġanimals ) Ġand Ġmechan istic Ġ( lik ening Ġhumans Ġto Ġin animate Ġobjects Ġor Ġmachines ), Ġand Ġthe Ġlinguistic Ġsignals Ġunderpin ning Ġeach Ġof Ġthese Ġforms Ġcould Ġsubstantially Ġdiffer Ġfrom Ġone Ġanother ĠB IB REF 4 . </s> For Ġsimplicity Ġand Ġease Ġof Ġinterpretation , Ġwe Ġfocus Ġon Ġquant ifying Ġlex ical Ġcues Ġof Ġdehuman ization . ĠHowever , Ġour Ġunderstanding Ġof Ġthe Ġlanguage Ġof Ġdehuman ization Ġwould Ġbe Ġenriched Ġby Ġconsidering Ġlinguistic Ġfeatures Ġbeyond Ġthe Ġlex icon . ĠFor Ġexample , ĠB IB REF 77 Ġhas Ġshown Ġthat Ġdefinite Ġplur als Ġin ĠEnglish Ġ( e . g . Ġthe Ġgays ) Ġhave Ġa Ġunique Ġsocial Ġand Ġpragmatic Ġeffect Ġcompared Ġto Ġbare Ġplur als Ġ( e . g . Ġgays ) Ġby Ġpackaging Ġindividual Ġentities Ġinto Ġone Ġmon olith Ġand Ġsetting Ġthis Ġgroup Ġapart Ġfrom Ġthe Ġspeaker . ĠIndex ing Ġa Ġspeaker 's Ġnon - members hip Ġin Ġthe Ġgroup Ġbeing Ġdiscussed Ġcreates Ġsocial Ġdistance Ġbetween Ġthe Ġspeaker Ġand Ġgroup ĠB IB REF 77 , Ġwhich Ġmakes Ġit Ġlikely Ġthat Ġusing Ġdefinite Ġplur als Ġto Ġlabel Ġmarginalized Ġsocial Ġgroups Ġplays Ġan Ġimportant Ġrole Ġin Ġdehuman ization . ĠSimilarly , Ġexamining Ġnon - lex ical Ġsignals Ġcould Ġhelp Ġus Ġcapture Ġelements Ġof Ġdehuman ization Ġthat Ġwe Ġcould Ġnot Ġeasily Ġidentify Ġonly Ġthrough Ġlex ical Ġresources . ĠFor Ġexample , Ġthe Ġpart Ġof Ġspeech Ġof Ġa Ġgroup Ġlabel Ġ( such Ġas Ġgay Ġas Ġa Ġnoun Ġor Ġadjective ) Ġcould Ġhave Ġimplications Ġfor Ġessential ism , Ġas Ġadject ives Ġsimply Ġname Ġattributes Ġto Ġsome Ġentity , Ġwhile Ġnoun s Ġexplicitly Ġstate Ġthe Ġentity 's Ġcategory Ġmembership Ġand Ġencaps ulates Ġother Ġstereotypes Ġassociated Ġwith Ġthat Ġcategory ĠB IB REF 78 , ĠB IB REF 79 , ĠB IB REF 76 , ĠB IB REF 80 . ĠWe Ġfurthermore Ġbelieve Ġthat Ġincorporating Ġdiscourse - level Ġanalysis , Ġsuch Ġas Ġexamining Ġthe Ġrole Ġof Ġdirect Ġquotes Ġin Ġan Ġarticle Ġand Ġwho Ġis Ġbeing Ġquoted , Ġcould Ġprovide Ġinformative Ġinsights Ġthat Ġcould Ġaddress Ġsome Ġlimitations Ġdiscussed Ġearlier . </s> We Ġsupported Ġour Ġproposed Ġframework Ġwith Ġa Ġcase Ġstudy Ġof ĠLGBTQ Ġrepresentation Ġin Ġthe ĠNew ĠYork ĠTimes . ĠThis Ġcase Ġstudy Ġhad Ġa Ġnumber Ġof Ġlimitations Ġas Ġan Ġanalysis Ġof Ġthe Ġdehuman ization Ġof ĠLGBTQ Ġpeople Ġin Ġthe Ġmedia . ĠWe Ġonly Ġlooked Ġat Ġone Ġdata Ġsource , Ġwhich Ġmay Ġhave Ġits Ġown Ġbiases Ġand Ġdoes Ġnot Ġcapture Ġthe Ġentirety Ġof Ġmedia Ġdiscourse Ġabout ĠLGBTQ Ġpeople . ĠFurthermore , Ġwe Ġonly Ġstudied Ġnewspaper Ġarticles Ġwritten Ġin Ġ( Standard ) ĠAmerican ĠEnglish . ĠFuture Ġwork Ġcould Ġassess Ġhow Ġwell Ġthese Ġlinguistic Ġcues Ġwe Ġmeasure Ġgeneral ize Ġto Ġother Ġlanguages Ġand Ġfocus Ġon Ġcross - ling u istic Ġcomparisons Ġof Ġdehuman izing Ġlanguage . ĠFinally , Ġmost Ġof Ġour Ġcase Ġstudy Ġinvolved Ġanalysis Ġof Ġan Ġaggreg ated ĠLGBTQ Ġrepresentation , with Ġa Ġspecific Ġfocus Ġon Ġthe Ġlabels Ġgay Ġand Ġhomosexual , Ġprimarily Ġdue Ġto Ġthe Ġamount Ġof Ġdata Ġavailable . ĠHowever , Ġas Ġa Ġresult , Ġwe Ġstill Ġhave Ġlittle Ġunderstanding Ġabout Ġthe Ġnuanced Ġdifferences Ġand Ġchanges Ġin Ġthe Ġrepresentation Ġof ĠLGBTQ Ġpeople Ġwho Ġdon 't Ġidentify Ġwith Ġ( or Ġaren 't Ġidentified Ġas ) Ġthese Ġlabels . </s> Discussion Ġ: :: ĠEth ical ĠImpl ications </s> Our Ġhope Ġin Ġpursuing Ġthis Ġwork Ġis Ġto Ġdraw Ġattention Ġto Ġissues Ġof Ġmedia Ġrepresentation Ġof Ġmarginalized Ġgroups Ġand Ġto Ġeventually Ġhelp Ġmake Ġthe Ġonline Ġworld Ġa Ġsafer Ġand Ġkind er Ġplace . ĠAn Ġimportant Ġpart Ġof Ġthis Ġmission Ġis Ġto Ġacknowledge Ġthe Ġethical Ġimplications Ġand Ġpotential Ġissues Ġof Ġour Ġown Ġwork . </s> The Ġmethods Ġthat Ġwe Ġuse Ġto Ġquantify Ġdehuman ization Ġare Ġthemselves Ġbiased Ġand Ġpotentially Ġharmful . ĠFor Ġexample , Ġwe Ġshowed Ġin ĠSection ĠSEC REF 25 Ġthat Ġthe Ġlex icon Ġthat Ġused Ġto Ġmeasure Ġval ence Ġcontains Ġits Ġown Ġanti - LGBT Q Ġbiases Ġby Ġconsidering ĠLGBTQ Ġgroup Ġlabels Ġto Ġbe Ġprimarily Ġnegative / un pleasant . ĠWe Ġalso Ġtrained Ġword 2 vec Ġmodels Ġon ĠNew ĠYork ĠTimes Ġdata , Ġwhich Ġpresents Ġbiases . ĠWhile Ġit Ġis Ġtypically Ġconcerning Ġwhen Ġsuch Ġmodels Ġare Ġtrained Ġon Ġbiased Ġdata Ġdue Ġto Ġharmful Ġdownstream Ġeffects ĠB IB REF 37 , Ġwe Ġleverage Ġthis Ġdata Ġas Ġa Ġuseful Ġresource Ġfor Ġuncover ing Ġhuman Ġbiases Ġand Ġunderstanding Ġhow Ġbiases Ġemerge Ġin Ġmedia Ġdiscourse Ġabout ĠLGBTQ Ġpeople . </s> Another Ġconcern Ġof Ġthis Ġwork Ġis Ġthat Ġwe Ġused Ġcomputational Ġmethods Ġto Ġrepresent Ġhuman Ġbeings Ġin Ġorder Ġto Ġanalyze Ġdehuman ization Ġon Ġa Ġlarge Ġscale . ĠHowever , Ġrepresenting Ġpeople Ġas Ġsequences Ġof Ġnumbers Ġ( especially Ġin Ġour Ġvector - based Ġexperiments ) Ġis Ġinherently Ġdehuman izing Ġthem . ĠWhile Ġwe Ġhope Ġthat Ġthis Ġwork Ġwill Ġhuman ize Ġand Ġempower Ġmarginalized Ġgroups , Ġwe Ġacknowledge Ġthat Ġit Ġalso Ġhas Ġthis Ġopposite Ġeffect . </s> Other Ġethical Ġimplications Ġof Ġthis Ġproject Ġappear Ġwithin Ġour Ġcase Ġstudy . ĠDue Ġto Ġthe Ġtime Ġscale Ġof Ġour Ġstudy Ġ( begin ning Ġin Ġ1986 ), Ġwe Ġdid Ġnot Ġinclude ĠLGBTQ Ġlabels Ġsuch Ġas Ġqueer Ġor Ġtrans , Ġwhich Ġoften Ġhad Ġdifferent Ġmeanings Ġand Ġwere Ġoften Ġfound Ġin Ġcontexts Ġunrelated Ġto ĠLGBTQ Ġidentities Ġin Ġearlier Ġyears . ĠFurthermore , Ġmuch Ġof Ġour Ġanalysis Ġfocused Ġon Ġan Ġaggreg ated Ġrepresentation Ġfor ĠLGBTQ Ġpeople , Ġwhich Ġunintentionally Ġminimized Ġthe Ġvast Ġdiversity Ġof Ġsocial Ġidentities Ġencomp assed Ġwithin Ġthis Ġumbrella . ĠWe Ġhighlighted Ġstriking Ġtemporal Ġchanges Ġand Ġdifferences Ġbetween Ġthe Ġlabels Ġgay Ġand Ġhomosexual , Ġwhich Ġwere Ġprimarily Ġchosen Ġbecause Ġthese Ġlabels Ġwere Ġwell - represented Ġthroughout Ġall Ġyears Ġof Ġdata . ĠHowever , Ġemphasizing Ġthese Ġlabels Ġat Ġthe Ġexpense Ġof Ġothers Ġmay Ġcontribute Ġto Ġthe Ġcontinued Ġer asure Ġof Ġpeople Ġwho Ġare Ġmarginalized Ġeven Ġwithin ĠLGBTQ Ġcommunities . </s> Conclusion </s> This Ġwork Ġis Ġthe Ġfirst Ġknown Ġcomputational Ġlinguistic Ġstudy Ġof Ġdehuman ization , Ġand Ġprovides Ġcontributions Ġto Ġmultiple Ġfields . ĠThe Ġproposed Ġframework Ġand Ġtechniques Ġto Ġquantify Ġsalient Ġcomponents Ġof Ġdehuman ization Ġcan Ġshed Ġlight Ġon Ġlinguistic Ġvariation Ġand Ġchange Ġin Ġdisc ourses Ġsurrounding Ġmarginalized Ġgroups . ĠFurthermore , Ġthese Ġtools Ġfor Ġlarge - scale Ġanalysis Ġhave Ġpotential Ġto Ġcomplement Ġsmaller - scale Ġpsychological Ġstudies Ġof Ġdehuman ization . ĠFinally , Ġthis Ġwork Ġhas Ġimplications Ġfor Ġautomatically Ġdetecting Ġand Ġunderstanding Ġmedia Ġbias Ġand Ġabusive Ġlanguage Ġonline . </s> Acknowled gements </s> We Ġwould Ġlike Ġto Ġthank ĠRob ĠVo ig t , ĠChris ĠPot ts , ĠPenny ĠEck ert , ĠDavid ĠJ urg ens , ĠC eren ĠBud ak , ĠAnna - Marie ĠSp re ng er , Ġand ĠIs ma el ĠMend o za Ġfor Ġtheir Ġinsightful Ġcontributions Ġand Ġfeedback . ĠWe Ġwould Ġalso Ġlike Ġto Ġthank Ġour Ġaudience Ġat ĠNW AV - 46 , Ġwho Ġprovided Ġhelpful Ġfeedback Ġon Ġan Ġearlier Ġversion Ġof Ġthis Ġproject . </s> App endix Ġ: :: ĠVal ence Ġprediction Ġresults </s> In Ġaddition Ġto Ġquant ifying Ġthe Ġnegative Ġevaluation Ġof Ġa Ġtarget Ġgroup Ġby Ġcalculating Ġthe Ġaverage Ġval ence Ġscore Ġof Ġa Ġgroup Ġlabel 's Ġvector Ġrepresentation 's Ġnearest Ġneighbors Ġaccording Ġto Ġthe ĠN RC ĠV AD Ġlex icon , Ġwe Ġalso Ġdirectly Ġinduced Ġa