accuracy and F1-score of 89.6% and 89.2%, respectively
EXTRACTIVE
accuracy and F1-score of 89.6% and 89.2%, respectively
EXTRACTIVE
Real-world classifier performance. To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively. These results show that in addition to superior accuracy during training and validation, our chosen classifier is also robust against over-fitting.
Real-world classifier performance. To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively. These results show that in addition to superior accuracy during training and validation, our chosen classifier is also robust against over-fitting.
1706.01875
Introduction </s> The Ġapparent Ġrise Ġin Ġpolitical Ġinc iv ility Ġhas Ġattracted Ġsubstantial Ġattention Ġfrom Ġscholars Ġin Ġrecent Ġyears . ĠThese Ġstudies Ġhave Ġlargely Ġfocused Ġon Ġthe Ġextent Ġto Ġwhich Ġpoliticians Ġand Ġelected Ġofficials Ġare Ġincreasingly Ġemploying Ġrhetoric Ġthat Ġappears Ġto Ġviolate Ġnorms Ġof Ġciv ility ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ. ĠFor Ġthe Ġpurposes Ġof Ġour Ġwork , Ġwe Ġuse Ġthe Ġincidence Ġof Ġoffensive Ġrhetoric Ġas Ġa Ġstand Ġin Ġfor Ġinc iv ility . ĠThe Ġ2016 ĠUS Ġpresidential Ġelection Ġwas Ġan Ġespecially Ġnoteworthy Ġcase Ġin Ġthis Ġregard , Ġparticularly Ġin Ġterms Ġof ĠDonald ĠTrump 's Ġcampaign Ġwhich Ġfrequently Ġviolated Ġnorms Ġof Ġciv ility Ġboth Ġin Ġhow Ġhe Ġspoke Ġabout Ġbroad Ġgroups Ġin Ġthe Ġpublic Ġ( such Ġas ĠMuslims , ĠMexicans , Ġand ĠAfrican ĠAmericans ) Ġand Ġthe Ġattacks Ġhe Ġleveled Ġat Ġhis Ġopponents ĠB IB REF 2 Ġ. ĠThe Ġconsequences Ġof Ġinc iv ility Ġare Ġthought Ġto Ġbe Ġcrucial Ġto Ġthe Ġfunctioning Ġof Ġdemocracy Ġsince ĠâĢ ľ public Ġciv ility Ġand Ġinterpersonal Ġpolit eness Ġsustain Ġsocial Ġharmony Ġand Ġallow Ġpeople Ġwho Ġdisagree Ġwith Ġone Ġanother Ġto Ġmaintain Ġongoing Ġrelationships " ĠB IB REF 3 Ġ. </s> While Ġpolitical Ġinc iv ility Ġappears Ġto Ġbe Ġon Ġthe Ġrise Ġamong Ġelites , Ġit Ġis Ġless Ġclear Ġwhether Ġthis Ġis Ġtrue Ġamong Ġthe Ġmass Ġpublic Ġas Ġwell . ĠIs Ġpolitical Ġdiscourse Ġparticularly Ġlacking Ġin Ġciv ility Ġcompared Ġto Ġdiscourse Ġmore Ġgenerally ? ĠDoes Ġthe Ġinc iv ility Ġof Ġmass Ġpolitical Ġdiscourse Ġrespond Ġto Ġthe Ġdynamics Ġof Ġpolitical Ġcampaigns ? ĠAdd ressing Ġthese Ġquestions Ġhas Ġbeen Ġdifficult Ġfor Ġpolitical Ġscientists Ġbecause Ġtraditional Ġtools Ġfor Ġstudying Ġmass Ġbehavior , Ġsuch Ġas Ġpublic Ġopinion Ġsurveys , Ġare Ġill - equipped Ġto Ġmeasure Ġhow Ġcitizens Ġdiscuss Ġpolitics Ġwith Ġone Ġanother . ĠSurvey Ġdata Ġdoes Ġreveal Ġthat Ġthe Ġpublic Ġtends Ġto Ġperceive Ġpolitics Ġas Ġbecoming Ġincreasingly Ġless Ġcivil Ġduring Ġthe Ġcourse Ġof Ġa Ġpolitical Ġcampaign ĠB IB REF 4 Ġ. ĠYet , Ġit Ġis Ġnot Ġclear Ġwhether Ġthese Ġperceptions Ġmatch Ġthe Ġreality , Ġparticularly Ġin Ġterms Ġof Ġthe Ġtypes Ġof Ġdiscussions Ġthat Ġcitizens Ġhave Ġwith Ġeach Ġother . </s> An Ġadditional Ġquestion Ġis Ġhow Ġinc iv ility Ġis Ġreceived Ġby Ġothers . ĠOn Ġone Ġhand , Ġviolations Ġof Ġnorms Ġregarding Ġoffensive Ġdiscourse Ġmay Ġbe Ġpolic ed Ġby Ġmembers Ġof Ġa Ġcommunity , Ġrendering Ġsuch Ġspeech Ġin effect ual . ĠOn Ġthe Ġother Ġhand , Ġoffensive Ġspeech Ġmay Ġbe Ġeffective Ġas Ġa Ġmeans Ġfor Ġdrawing Ġattention Ġto Ġa Ġparticular Ġargument . ĠIndeed , Ġthere Ġis Ġevidence Ġthat Ġincreasing Ġinc iv ility Ġin Ġpolitical Ġspeech Ġresults Ġin Ġhigher Ġlevels Ġof Ġattention Ġfrom Ġthe Ġpublic ĠB IB REF 1 Ġ. ĠDuring Ġthe Ġ2016 Ġcampaign , Ġthe Ġuse Ġof Ġswearing Ġin Ġcomments Ġposted Ġon ĠDonald ĠTrump 's ĠYouTube Ġchannel Ġtended Ġto Ġresult Ġin Ġadditional Ġresponses Ġthat Ġmim icked Ġsuch Ġswearing ĠB IB REF 5 Ġ. ĠThus , Ġoffensive Ġspeech Ġin Ġonline Ġfor a Ġmay Ġattract Ġmore Ġattention Ġfrom Ġthe Ġcommunity Ġand Ġlead Ġto Ġthe Ġspread Ġof Ġeven Ġmore Ġoffensive Ġspeech Ġin Ġsubsequent Ġposts . </s> To Ġaddress Ġthese Ġquestions Ġregarding Ġpolitical Ġinc iv ility , Ġwe Ġexamine Ġthe Ġuse Ġof Ġoffensive Ġspeech Ġin Ġpolitical Ġdiscussions Ġhoused Ġon ĠReddit . ĠScholars Ġtend Ġto Ġdefine Ġunc ivil Ġdiscourse Ġas ĠâĢ ľ communication Ġthat Ġviolates Ġthe Ġnorms Ġof Ġpolit eness " ĠB IB REF 1 Ġa Ġdefinition Ġthat Ġclearly Ġincludes Ġoffensive Ġremarks . ĠReddit Ġfor a Ġrepresent Ġa ĠâĢ ľ most Ġlikely " Ġcase Ġfor Ġthe Ġstudy Ġof Ġoffensive Ġpolitical Ġspeech Ġdue Ġits Ġstrong Ġfree Ġspeech Ġculture ĠB IB REF 6 Ġand Ġthe Ġability Ġof Ġparticipants Ġto Ġuse Ġpseud onymous Ġidentities . ĠThat Ġis , Ġif Ġpolitical Ġinc iv ility Ġin Ġthe Ġpublic Ġdid Ġincrease Ġduring Ġthe Ġ2016 Ġcampaign , Ġthis Ġshould Ġbe Ġespecially Ġevident Ġon Ġfor a Ġsuch Ġas ĠReddit . ĠTracking ĠReddit Ġdiscussions Ġthroughout Ġall Ġof Ġ2015 Ġand Ġ2016 , Ġwe Ġfind Ġthat Ġonline Ġpolitical Ġdiscussions Ġbecame Ġincreasingly Ġmore Ġoffensive Ġas Ġthe Ġgeneral Ġelection Ġcampaign Ġintensified . ĠBy Ġcomparison , Ġdiscussions Ġon Ġnon - political Ġsubreddits Ġdid Ġnot Ġbecome Ġincreasingly Ġoffensive Ġduring Ġthis Ġperiod . ĠAdditionally , Ġwe Ġfind Ġthat Ġthe Ġpresence Ġof Ġoffensive Ġcomments Ġdid Ġnot Ġsub side Ġeven Ġthree Ġmonths Ġafter Ġthe Ġelection . </s> Dat as ets </s> Our Ġstudy Ġmakes Ġuse Ġof Ġmultiple Ġdatasets Ġin Ġorder Ġto Ġidentify Ġand Ġcharacterize Ġtrends Ġin Ġoffensive Ġspeech . </s> The ĠCrowd Fl ower Ġhate Ġspeech Ġdataset . ĠThe ĠCrowd Fl ower Ġhate Ġspeech Ġdataset ĠB IB REF 7 Ġcontains Ġ14 . 5 K Ġtweets , Ġeach Ġreceiving Ġlabels Ġfrom Ġat Ġleast Ġthree Ġcontributors . ĠContribut ors Ġwere Ġallowed Ġto Ġclassify Ġeach Ġtweet Ġinto Ġone Ġof Ġthree Ġclasses : ĠNot ĠOffensive Ġ( NO ), ĠOffensive Ġbut Ġnot Ġhateful Ġ( O ), Ġand ĠOffensive Ġand Ġhateful Ġ( OH ). ĠOf Ġthe Ġ14 . 5 K Ġtweets , Ġonly Ġ37 . 6 % Ġhad Ġa Ġdecisive Ġclass ĠâĢĵ Ġi . e ., Ġthe Ġsame Ġclass Ġwas Ġassigned Ġby Ġall Ġcontributors . ĠFor Ġindec isive Ġcases , Ġthe Ġmajority Ġclass Ġwas Ġselected Ġand Ġa Ġclass Ġconfidence Ġscore Ġ( f raction Ġof Ġcontributors Ġthat Ġselected Ġthe Ġmajority Ġclass ) Ġwas Ġmade Ġavailable . ĠUsing Ġthis Ġapproach , Ġ50 . 4 %, Ġ33 . 1 %, Ġand Ġ16 . 5 % Ġof Ġthe Ġtweets Ġwere Ġcategorized Ġas ĠNO , ĠO , Ġand ĠOH , Ġrespectively . ĠSince Ġour Ġgoal Ġis Ġto Ġidentify Ġany Ġoffensive Ġspeech Ġ( not Ġjust Ġhate Ġspeech ), Ġwe Ġconsolidate Ġassigned Ġclasses Ġinto ĠOffensive Ġand ĠNot ĠOffensive Ġby Ġrel abel ing ĠOH Ġtweets Ġas ĠOffensive . ĠWe Ġuse Ġthis Ġmodified Ġdataset Ġto Ġtrain , Ġvalidate , Ġand Ġtest Ġour Ġoffensive Ġspeech Ġclass ifier . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , Ġthis Ġis Ġthe Ġonly Ġdataset Ġthat Ġprovides Ġoffensive Ġand Ġnot Ġoffensive Ġannotations Ġto Ġa Ġlarge Ġdataset . </s> Off ensive Ġword Ġlists . ĠWe Ġalso Ġuse Ġtwo Ġoffensive Ġword Ġlists Ġas Ġauxiliary Ġinput Ġto Ġour Ġclass ifier : Ġ( 1 ) ĠThe ĠHate base Ġhate Ġspeech Ġvocabulary ĠB IB REF 8 Ġconsisting Ġof Ġ112 2 Ġhateful Ġwords Ġand Ġ( 2 ) Ġ422 Ġoffensive Ġwords Ġbanned Ġfrom ĠGoogle 's ĠWhat ĠDo ĠYou ĠLove Ġproject ĠB IB REF 9 Ġ. </s> Reddit Ġcomments Ġdataset . ĠFinally , Ġafter Ġbuilding Ġour Ġoffensive Ġspeech Ġclass ifier Ġusing Ġthe Ġabove Ġdatasets , Ġwe Ġuse Ġit Ġto Ġclassify Ġcomments Ġmade Ġon ĠReddit . ĠWhile Ġthe Ġcomplete ĠReddit Ġdataset Ġcontains Ġ2 B Ġcomments Ġmade Ġbetween Ġthe Ġperiod Ġof ĠJanuary Ġ2015 Ġand ĠJanuary Ġ2017 , Ġwe Ġonly Ġanalyze Ġonly Ġ168 M . ĠWe Ġselect Ġcomments Ġto Ġbe Ġanalyzed Ġusing Ġthe Ġfollowing Ġprocess : Ġ( 1 ) Ġwe Ġexclude Ġcomments Ġshorter Ġthan Ġ10 Ġcharacters Ġin Ġlength , Ġ( 2 ) Ġwe Ġexclude Ġcomments Ġmade Ġby Ġ[ de leted ] Ġauthors , Ġand Ġ( 3 ) Ġwe Ġrandomly Ġsample Ġand Ġinclude Ġ10 % Ġof Ġall Ġremaining Ġcomments . ĠWe Ġcategor ize Ġcomments Ġmade Ġin Ġany Ġof Ġ21 Ġpopular Ġpolitical Ġsubreddits Ġas Ġpolitical Ġand Ġthe Ġremainder Ġas Ġap olitical . ĠOur Ġfinal Ġdataset Ġcontains Ġ129 M Ġap olitical Ġand Ġ39 M Ġpolitical Ġcomments . Ġfig : comment - tim eline Ġshows Ġthe Ġnumber Ġof Ġcomments Ġin Ġour Ġdataset Ġthat Ġwere Ġmade Ġduring Ġeach Ġweek Ġincluded Ġin Ġour Ġstudy . ĠWe Ġsee Ġan Ġincreasing Ġnumber Ġof Ġpolitical Ġcomments Ġper Ġweek Ġstarting Ġin ĠFebruary Ġ2016 ĠâĢĵ Ġthe Ġstart Ġof Ġthe Ġ2016 ĠUS Ġpresidential Ġprimaries . </s> Off ensive ĠSpeech ĠClassification </s> In Ġorder Ġto Ġidentify Ġoffensive Ġspeech , Ġwe Ġpropose Ġa Ġfully Ġautomated Ġtechnique Ġthat Ġclass ifies Ġcomments Ġinto Ġtwo Ġclasses : ĠOffensive Ġand ĠNot ĠOffensive . </s> Class ification Ġapproach </s> At Ġa Ġhigh - level , Ġour Ġapproach Ġworks Ġas Ġfollows : </s> Build Ġa Ġword Ġembed ding . ĠWe Ġconstruct Ġa Ġ100 - dimensional Ġword Ġembed ding Ġusing Ġall Ġcomments Ġfrom Ġour Ġcomplete ĠReddit Ġdataset Ġ( 2 B Ġcomments ). </s> Construct Ġa Ġhate Ġvector . ĠWe Ġconstruct Ġa Ġlist Ġof Ġoffensive Ġand Ġhateful Ġwords Ġidentified Ġfrom Ġexternal Ġdata Ġand Ġmap Ġthem Ġinto Ġa Ġsingle Ġvector Ġwithin Ġthe Ġhigh - dimensional Ġword Ġembed ding . </s> Text Ġtransformation Ġand Ġclassification . ĠFinally , Ġwe Ġtransform Ġtext Ġto Ġbe Ġclassified Ġinto Ġscal ars Ġrepresenting Ġtheir Ġdistance Ġfrom Ġthe Ġconstructed Ġhate Ġvector Ġand Ġuse Ġthese Ġas Ġinput Ġto Ġa ĠRandom ĠForest Ġclass ifier . </s> Building Ġa Ġword Ġembed ding . ĠAt Ġa Ġhigh - level , Ġa Ġword Ġembed ding Ġmaps Ġwords Ġinto Ġa Ġhigh - dimensional Ġcontinuous Ġvector Ġspace Ġin Ġsuch Ġa Ġway Ġthat Ġsemantic Ġsimilarities Ġbetween Ġwords Ġare Ġmaintained . ĠThis Ġmapping Ġis Ġachieved Ġby Ġexploiting Ġthe Ġdistribution al Ġproperties Ġof Ġwords Ġand Ġtheir Ġoccurrences Ġin Ġthe Ġinput Ġcorpus . </s> Rather Ġthan Ġusing Ġan Ġoff - the - she lf Ġword Ġembed ding Ġ( e . g ., Ġthe ĠGl o Ve Ġembed d ings ĠB IB REF 10 Ġtrained Ġusing Ġpublic Ġdomain Ġdata Ġsources Ġsuch Ġas ĠWikipedia Ġand Ġnews Ġarticles ), Ġwe Ġconstruct Ġa Ġ100 - dimensional Ġembed ding Ġusing Ġthe Ġcomplete ĠReddit Ġdataset Ġ( 2 B Ġcomments ) Ġas Ġthe Ġinput Ġcorpus . ĠThe Ġconstructed Ġembed ding Ġconsists Ġof Ġover Ġ400 M Ġunique Ġwords Ġ( words Ġoccurring Ġless Ġthan Ġ25 Ġtimes Ġin Ġthe Ġentire Ġcorpus Ġare Ġexcluded ) Ġusing Ġthe ĠWord 2 V ec ĠB IB REF 11 Ġimplementation Ġprovided Ġby Ġthe ĠG ens im Ġlibrary ĠB IB REF 12 Ġ. ĠPrior Ġto Ġconstructing Ġthe Ġembed ding , Ġwe Ġperform Ġstop - word Ġremoval Ġand Ġle mm at ize Ġeach Ġword Ġin Ġthe Ġinput Ġcorpus Ġusing Ġthe ĠSpa Cy ĠN LP Ġframework ĠB IB REF 13 Ġ. ĠThe Ġmain Ġreason Ġfor Ġbuilding Ġa Ġcustom Ġembed ding Ġis Ġto Ġensure Ġthat Ġour Ġembed d ings Ġcapture Ġsemantics Ġspecific Ġto Ġthe Ġdata Ġbeing Ġmeasured Ġ( Reddit ) ĠâĢĵ Ġe . g ., Ġwhile Ġthe Ġword ĠâĢ ľ k arma âĢ Ŀ Ġin Ġthe Ġnon - Reddit Ġcontext Ġmay Ġbe Ġassociated Ġwith Ġspirituality , Ġit Ġis Ġassociated Ġwith Ġpoints Ġ( comment Ġand Ġsubmission Ġscores ) Ġon ĠReddit . </s> Construct ing Ġa Ġhate Ġvector . ĠWe Ġuse Ġtwo Ġlists Ġof Ġwords Ġassociated Ġwith Ġhate ĠB IB REF 8 Ġand Ġoffensive ĠB IB REF 9 Ġspeech Ġto Ġconstruct Ġa Ġhate Ġvector Ġin Ġour Ġword Ġembed ding . ĠThis Ġis Ġdone Ġby Ġmapping Ġeach Ġword Ġin Ġthe Ġlist Ġinto Ġthe Ġ100 - dimensional Ġembed ding Ġand Ġcomputing Ġthe Ġmean Ġvector . ĠThis Ġvector Ġrepresents Ġthe Ġaverage Ġof Ġall Ġknown Ġoffensive Ġwords . ĠThe Ġmain Ġidea Ġbehind Ġcreating Ġa Ġhate Ġvector Ġis Ġto Ġcapture Ġthe Ġpoint Ġ( in Ġour Ġembed ding ) Ġto Ġwhich Ġthe Ġmost Ġoffensive Ġobserved Ġcomments Ġare Ġlikely Ġto Ġbe Ġnear . ĠAlthough Ġclust ering Ġour Ġoffensive Ġword Ġlists Ġinto Ġsimilar Ġgroups Ġand Ġconstructing Ġmultiple Ġhate Ġvectors ĠâĢĵ Ġone Ġfor Ġeach Ġcluster ĠâĢĵ Ġresults Ġin Ġmarginally Ġbetter Ġaccuracy Ġfor Ġour Ġclass ifier , Ġwe Ġuse Ġthis Ġapproach Ġdue Ġto Ġthe Ġfact Ġthat Ġour Ġclassification Ġcost Ġgrows Ġlin early Ġwith Ġthe Ġnumber Ġof Ġhate Ġvectors ĠâĢĵ Ġi . e ., Ġwe Ġneed Ġto Ġperform ĠIN LINE FORM 0 Ġdistance Ġcomput ations Ġper Ġhate Ġvector Ġto Ġclassify Ġstring ĠIN LINE FORM 1 Ġ. </s> Trans forming Ġand Ġclass ifying Ġtext . ĠWe Ġfirst Ġremove Ġstop - words Ġand Ġperform Ġle mm at ization Ġof Ġeach Ġword Ġin Ġthe Ġtext Ġto Ġbe Ġclassified . ĠWe Ġthen Ġobtain Ġthe Ġvector Ġrepresenting Ġeach Ġword Ġin Ġthe Ġtext Ġand Ġcompute Ġits Ġsimilarity Ġto Ġthe Ġconstructed Ġhate Ġvector Ġusing Ġthe Ġcos ine Ġsimilarity Ġmetric . ĠA Ġ0 - vector Ġis Ġused Ġto Ġrepresent Ġwords Ġin Ġthe Ġtext Ġthat Ġare Ġnot Ġpresent Ġin Ġthe Ġembed ding . ĠFinally , Ġthe Ġmaximum Ġcos ine Ġsimilarity Ġscore Ġis Ġused Ġto Ġrepresent Ġthe Ġcomment . ĠEqu ation ĠEQ REF 7 Ġshows Ġthe Ġtransformation Ġfunction Ġon Ġa Ġstring ĠIN LINE FORM 0 Ġ= ĠIN LINE FORM 1 Ġwhere ĠIN LINE FORM 2 Ġis Ġthe Ġvector Ġrepresenting Ġthe ĠIN LINE FORM 3 Ġle mm at ized Ġnon - stop - word , ĠIN LINE FORM 4 Ġis Ġthe Ġcos ine - similar ity Ġfunction , Ġand ĠIN LINE FORM 5 Ġis Ġthe Ġhate Ġvector . ĠDIS PLAY FORM 0 </s> In Ġwords , Ġthe Ġnumerical Ġvalue Ġassigned Ġto Ġa Ġtext Ġis Ġthe Ġcos ine Ġsimilarity Ġbetween Ġthe Ġhate Ġvector Ġand Ġthe Ġvector Ġrepresenting Ġthe Ġword Ġ( in Ġthe Ġtext ) Ġclosest Ġto Ġthe Ġhate Ġvector . ĠThis Ġapproach Ġallows Ġus Ġto Ġtransform Ġa Ġstring Ġof Ġtext Ġinto Ġa Ġsingle Ġnumerical Ġvalue Ġthat Ġcaptures Ġits Ġsemantic Ġsimilarity Ġto Ġthe Ġmost Ġoffensive Ġcomment . ĠWe Ġuse Ġthese Ġscal ars Ġas Ġinput Ġto Ġa Ġrandom Ġforest Ġclass ifier Ġto Ġperform Ġclassification Ġinto ĠOffensive Ġand ĠNot ĠOffensive Ġclasses . Ġfig : red uced - dimension - classes Ġshows Ġthe Ġproximity Ġof ĠOffensive Ġand ĠNon ĠOffensive Ġcomments Ġto Ġour Ġconstructed Ġhate Ġvector Ġafter Ġusing Ġt - dist ributed ĠSt och astic ĠNeighbor ĠEmb edd ing Ġ( t - S NE ) ĠB IB REF 14 Ġto Ġreduce Ġour Ġ100 - dimension Ġvector Ġspace Ġinto Ġ2 Ġdimensions . </s> Class ifier Ġevaluation </s> We Ġnow Ġpresent Ġresults Ġto Ġ( 1 ) Ġvalidate Ġour Ġchoice Ġof Ġclass ifier Ġand Ġ( 2 ) Ġdemonstrate Ġthe Ġimpact Ġof Ġtraining / valid ation Ġsample Ġcount Ġon Ġour Ġclass ifiers Ġperformance . </s> Class ifier Ġselection Ġmethodology . ĠTo Ġidentify Ġthe Ġmost Ġsuitable Ġclass ifier Ġfor Ġclass ifying Ġthe Ġscal ars Ġassociated Ġwith Ġeach Ġtext , Ġwe Ġperform Ġevaluations Ġusing Ġthe Ġsto ch astic Ġgradient Ġdescent , Ġnaive Ġbay es , Ġdecision Ġtree , Ġand Ġrandom Ġforest Ġclass ifiers . ĠFor Ġeach Ġclass ifier , Ġwe Ġsplit Ġthe ĠCrowd Fl ower Ġhate Ġspeech Ġdataset Ġinto Ġa Ġtraining / valid ation Ġset Ġ( 75 %), Ġand Ġa Ġhold out Ġset Ġ( 25 %). ĠWe Ġperform Ġ10 - fold Ġcross - valid ation Ġon Ġthe Ġtraining / valid ation Ġset Ġto Ġidentify Ġthe Ġbest Ġclass ifier Ġmodel Ġand Ġparameters Ġ( using Ġa Ġgrid Ġsearch ). ĠBased Ġon Ġthe Ġresults Ġof Ġthis Ġevaluation , Ġwe Ġselect Ġa Ġ100 - est im ator Ġentropy - based Ġsplitting Ġrandom Ġforest Ġmodel Ġas Ġour Ġclass ifier . Ġtab : class ifiers Ġshows Ġthe Ġmean Ġaccuracy Ġand ĠF 1 - score Ġfor Ġeach Ġevaluated Ġclass ifier Ġduring Ġthe Ġ10 - fold Ġcross - valid ation . </s> Real - world Ġclass ifier Ġperformance . ĠTo Ġevaluate Ġreal - world Ġperformance Ġof Ġour Ġselected Ġclass ifier Ġ( i . e ., Ġperformance Ġin Ġthe Ġabsence Ġof Ġmodel Ġand Ġparameter Ġbias ), Ġwe Ġperform Ġclassification Ġof Ġthe Ġhold out Ġset . ĠOn Ġthis Ġset , Ġour Ġclass ifier Ġhad Ġan Ġaccuracy Ġand ĠF 1 - score Ġof Ġ89 . 6 % Ġand Ġ89 . 2 %, Ġrespectively . ĠThese Ġresults Ġshow Ġthat Ġin Ġaddition Ġto Ġsuperior Ġaccuracy Ġduring Ġtraining Ġand Ġvalidation , Ġour Ġchosen Ġclass ifier Ġis Ġalso Ġrobust Ġagainst Ġover - fitting . </s> Imp act Ġof Ġdataset Ġquality Ġand Ġsize . ĠTo Ġunderstand Ġhow Ġthe Ġperformance Ġof Ġour Ġchosen Ġclass ifier Ġmodel Ġand Ġparameters Ġare Ġimpacted Ġby : Ġ( 1 ) Ġthe Ġquality Ġand Ġconsistency Ġof Ġmanually Ġassigned Ġclasses Ġin Ġthe ĠCrowd Fl ower Ġdataset Ġand Ġ( 2 ) Ġthe Ġsize Ġof Ġthe Ġdataset , Ġwe Ġre - evaluate Ġthe Ġclass ifier Ġwhile Ġonly Ġconsidering Ġtweets Ġhaving Ġa Ġminimum Ġconfidence Ġscore Ġand Ġvarying Ġthe Ġsize Ġof Ġthe Ġhold out Ġset . ĠSpecifically , Ġour Ġexperiments Ġconsidered Ġconfidence Ġthresholds Ġof Ġ0 Ġ( all Ġtweets Ġconsidered ), Ġ. 35 Ġ( only Ġtweets Ġwhere Ġat Ġleast Ġ35 % Ġof Ġcontributors Ġagreed Ġon Ġa Ġclass Ġwere Ġconsidered ), Ġand Ġ. 70 Ġ( only Ġtweets Ġwhere Ġat Ġleast Ġ70 % Ġof Ġthe Ġcontributors Ġagreed Ġon Ġa Ġclass Ġwere Ġconsidered ) Ġand Ġvaried Ġthe Ġhold out Ġset Ġsizes Ġbetween Ġ5 % Ġand Ġ95 % Ġof Ġall Ġtweets Ġmeeting Ġthe Ġconfidence Ġthreshold Ġset Ġfor Ġthe Ġexperiment . </s> The Ġresults Ġillustrated Ġin Ġfig : class ifier - performance Ġshow Ġthe Ġperformance Ġof Ġthe Ġclass ifier Ġwhile Ġevaluating Ġthe Ġcorresponding Ġhold out Ġset . ĠWe Ġmake Ġseveral Ġconclusions Ġfrom Ġthese Ġresults : </s> Beyond Ġa Ġ( fair ly Ġlow ) Ġthreshold , Ġthe Ġsize Ġof Ġthe Ġtraining Ġand Ġvalidation Ġset Ġhas Ġlittle Ġimpact Ġon Ġclass ifier Ġperformance . ĠWe Ġsee Ġthat Ġthe Ġaccuracy , Ġprecision , Ġand Ġrecall Ġhave , Ġat Ġbest , Ġmarginal Ġimprovements Ġwith Ġhold out Ġset Ġsizes Ġsmaller Ġthan Ġ60 %. ĠThis Ġimplies Ġthat Ġthe ĠCrowd Fl ower Ġdataset Ġis Ġsufficient Ġfor Ġbuilding Ġan Ġoffensive Ġspeech Ġclass ifier . </s> Quality Ġof Ġmanual Ġlabeling Ġhas Ġa Ġsignificant Ġimpact Ġon Ġthe Ġaccuracy Ġand Ġprecision Ġof Ġthe Ġclass ifier . ĠUsing Ġonly Ġtweets Ġwhich Ġhad Ġat Ġleast Ġ70 % Ġof Ġcontributors Ġagreeing Ġon Ġa Ġclass Ġresulted Ġin Ġbetween Ġ5 - 7 % Ġhigher Ġaccuracy Ġand Ġup Ġto Ġ5 % Ġhigher Ġprecision . </s> Our Ġclass ifier Ġachieves Ġprecision Ġof Ġover Ġ95 % Ġand Ġrecall Ġof Ġover Ġ85 % Ġwhen Ġconsidering Ġonly Ġhigh Ġconfidence Ġsamples . ĠThis Ġimplies Ġthat Ġthe Ġclass ifier Ġis Ġmore Ġlikely Ġto Ġunderestimate Ġthe Ġpresence Ġof Ġoffensive Ġspeech ĠâĢĵ Ġi . e ., Ġour Ġresults Ġlikely Ġprovide Ġa Ġlower - bound Ġon Ġthe Ġquantity Ġof Ġobserved Ġoffensive Ġspeech . </s> Measure ments </s> In Ġthis Ġsection Ġwe Ġquantify Ġand Ġcharacterize Ġoff ens iveness Ġin Ġthe Ġpolitical Ġand Ġgeneral Ġcontexts Ġusing Ġour Ġoffensive Ġspeech Ġclass ifier Ġand Ġthe ĠReddit Ġcomments Ġdataset Ġwhich Ġconsiders Ġa Ġrandom Ġsample Ġof Ġcomments Ġmade Ġbetween ĠJanuary Ġ2015 Ġand ĠJanuary Ġ2017 . </s> Off ens iveness Ġover Ġtime . ĠWe Ġfind Ġthat Ġon Ġaverage Ġ8 . 4 % Ġof Ġall Ġpolitical Ġcomments Ġare Ġoffensive Ġcompared Ġto Ġ7 . 8 % Ġof Ġall Ġap olitical Ġcomments . Ġfig : offensive - speech - tim eline Ġillustrates Ġthe Ġfraction Ġof Ġoffensive Ġpolitical Ġand Ġap olitical Ġcomments Ġmade Ġduring Ġeach Ġweek Ġin Ġour Ġstudy . ĠWe Ġsee Ġthat Ġwhile Ġthe Ġfraction Ġof Ġap olitical Ġoffensive Ġcomments Ġhas Ġstayed Ġsteady , Ġthere Ġhas Ġbeen Ġan Ġincrease Ġin Ġthe Ġfraction Ġof Ġoffensive Ġpolitical Ġcomments Ġstarting Ġin ĠJuly Ġ2016 . ĠNotably , Ġthis Ġincrease Ġis Ġobserved Ġafter Ġthe Ġconclusion Ġof Ġthe ĠUS Ġpresidential Ġprimaries Ġand Ġduring Ġthe Ġperiod Ġof Ġthe ĠDemocratic Ġand ĠRepublican ĠNational ĠCon ventions Ġand Ġdoes Ġnot Ġreduce Ġeven Ġafter Ġthe Ġconclusion Ġof Ġthe ĠUS Ġpresidential Ġelections Ġheld Ġon ĠNovember Ġ8 . ĠParticipants Ġin Ġpolitical Ġsubreddits Ġwere Ġ2 . 6 % Ġmore Ġlikely Ġto Ġobserve Ġoffensive Ġcomments Ġprior Ġto ĠJuly Ġ2016 Ġbut Ġ14 . 9 % Ġmore Ġlikely Ġto Ġobserve Ġoffensive Ġcomments Ġfrom ĠJuly Ġ2016 Ġonwards . </s> Re actions Ġto Ġoffensive Ġcomments . ĠWe Ġuse Ġthe Ġcomment Ġscore , Ġroughly Ġthe Ġdifference Ġbetween Ġup - votes Ġand Ġdown - votes Ġreceived , Ġas Ġa Ġproxy Ġfor Ġunderstanding Ġhow Ġusers Ġreacted Ġto Ġoffensive Ġcomments . ĠWe Ġfind Ġthat Ġcomments Ġthat Ġwere Ġoffensive : Ġ( 1 ) Ġon Ġaverage , Ġhad Ġa Ġhigher Ġscore Ġthan Ġnon - offensive Ġcomments Ġ( average Ġscores : Ġ8 . 9 Ġvs . Ġ6 . 7 ) Ġand Ġ( 2 ) Ġwere Ġbetter Ġreceived Ġwhen Ġthey Ġwere Ġposted Ġin Ġthe Ġgeneral Ġcontext Ġthan Ġin Ġthe Ġpolitical Ġcontext Ġ( average Ġscores : Ġ8 . 6 Ġvs . Ġ9 . 0 ). ĠTo Ġunderstand Ġhow Ġpeoples Ġreactions Ġto Ġoffensive Ġcomments Ġevolved Ġover Ġtime , Ġfig : offensive - sc ores - tim eline Ġshows Ġthe Ġaverage Ġscores Ġreceived Ġby Ġoffensive Ġcomments Ġover Ġtime . ĠAgain , Ġwe Ġobserve Ġan Ġincreasing Ġtrend Ġin Ġaverage Ġscores Ġreceived Ġby Ġoffensive Ġand Ġpolitical Ġcomments Ġafter ĠJuly Ġ2016 . </s> Character istics Ġof Ġoffensive Ġauthors . ĠWe Ġnow Ġfocus Ġon Ġunderstanding Ġcharacteristics Ġof Ġauthors Ġof Ġoffensive Ġcomments . ĠSpecifically , Ġwe Ġare Ġinterested Ġin Ġidentifying Ġthe Ġuse Ġof Ġthrow away Ġand Ġtroll Ġaccounts . ĠFor Ġthe Ġpurposes Ġof Ġthis Ġstudy , Ġwe Ġcharacterize Ġthrow away Ġaccounts Ġas Ġthose Ġwith Ġless Ġthan Ġfive Ġtotal Ġcomments ĠâĢĵ Ġi . e ., Ġaccounts Ġthat Ġare Ġused Ġto Ġmake Ġa Ġsmall Ġnumber Ġof Ġcomments . ĠSimilarly , Ġwe Ġdefine Ġtroll Ġaccounts Ġas Ġthose Ġwith Ġover Ġ15 Ġcomments Ġof Ġwhich Ġover Ġ75 % Ġare Ġclassified Ġas Ġoffensive ĠâĢĵ Ġi . e ., Ġaccounts Ġthat Ġare Ġused Ġto Ġmake Ġa Ġlarger Ġnumber Ġof Ġcomments , Ġof Ġwhich Ġa Ġsignificant Ġmajority Ġare Ġoffensive . ĠWe Ġfind Ġthat Ġ93 . 7 % Ġof Ġthe Ġaccounts Ġwhich Ġhave Ġover Ġ75 % Ġof Ġtheir Ġcomments Ġtagged Ġas Ġoffensive Ġare Ġthrow aways Ġand Ġ1 . 3 % Ġare Ġtrolls . ĠComplete Ġresults Ġare Ġillustrated Ġin Ġfig : offensive - authors - c df . </s> Character istics Ġof Ġoffensive Ġcommunities . ĠWe Ġbreakdown Ġsubreddits Ġby Ġtheir Ġcategory Ġ( default , Ġpolitical , Ġand Ġother ) Ġand Ġidentify Ġthe Ġmost Ġand Ġleast Ġoffensive Ġcommunities Ġin Ġeach . Ġfig : sub reddit - c df Ġshows Ġthe Ġdistribution Ġof Ġthe Ġfraction Ġof Ġoffensive Ġcomments Ġin Ġeach Ġcategory Ġand Ġtab : sub reddit - break down Ġshows Ġthe Ġmost Ġand Ġleast Ġoffensive Ġsubreddits Ġin Ġthe Ġpolitical Ġand Ġdefault Ġcategories Ġ( we Ġexclude Ġthe ĠâĢ ľ other âĢ Ŀ Ġcategory Ġdue Ġto Ġthe Ġin appropri at eness Ġof Ġtheir Ġnames ). ĠWe Ġfind Ġthat Ġless Ġthan Ġ19 % Ġof Ġall Ġsubreddits Ġ( that Ġaccount Ġfor Ġover Ġ23 % Ġof Ġall Ġcomments ) Ġhave Ġover Ġ10 % Ġoffensive Ġcomments . ĠFurther , Ġseveral Ġdefault Ġand Ġpolitical Ġsubreddits Ġfall Ġin Ġthis Ġcategory , Ġincluding Ġr / the ĠIN LINE FORM 0 Ġdon ald ĠâĢĵ Ġthe Ġmost Ġoffensive Ġpolitical Ġsubreddit Ġand Ġthe Ġsubreddit Ġdedicated Ġto Ġthe ĠUS ĠPresident . </s> Flow Ġof Ġoffensive Ġauthors . ĠFinally , Ġwe Ġuncover Ġpatterns Ġin Ġthe Ġmovement Ġof Ġoffensive Ġauthors Ġbetween Ġcommunities . ĠIn Ġfig : offensive - flow Ġwe Ġshow Ġthe Ġcommunities Ġin Ġwhich Ġlarge Ġnumber Ġof Ġauthors Ġof Ġoffensive Ġcontent Ġon Ġthe Ġr / politics Ġsubreddit Ġhad Ġpreviously Ġmade Ġoffensive Ġcomments Ġ( we Ġrefer Ġto Ġthese Ġcommunities Ġas Ġsources ). ĠUn surprisingly , Ġthe Ġmost Ġpopular Ġsources Ġbelonged Ġto Ġthe Ġdefault Ġsubreddits Ġ( e . g ., Ġr / world news , Ġr / w tf , Ġr / videos , Ġr / ask reddit , Ġand Ġr / news ). ĠWe Ġfind Ġthat Ġseveral Ġother Ġpolitical Ġsubreddits Ġalso Ġserve Ġas Ġlarge Ġsources Ġof Ġoffensive Ġauthors . ĠIn Ġfact , Ġthe Ġsubreddits Ġdedicated Ġto Ġthe Ġthree Ġmost Ġpopular ĠUS Ġpresidential Ġcandidates ĠâĢĵ Ġr / the ĠIN LINE FORM 0 Ġdon ald , Ġr / sand ers for president , Ġand Ġr / hillary clinton Ġrank Ġin Ġthe Ġtop Ġthree . ĠFinally , Ġoutside Ġof Ġthe Ġdefault Ġand Ġpolitical Ġsubreddits , Ġwe Ġfind Ġthat Ġr / n fl , Ġr / cons piracy , Ġr / d ota 2 , Ġr / re action gif s , Ġr / black people twitter , Ġand Ġr / im going to hell forth is Ġwere Ġthe Ġlargest Ġsources Ġof Ġoffensive Ġpolitical Ġauthors . </s> Con clusions Ġand ĠFuture ĠWork </s> We Ġdevelop Ġand Ġvalidate Ġan Ġoffensive Ġspeech Ġclass ifier Ġto Ġquantify Ġthe Ġpresence Ġof Ġoffensive Ġonline Ġcomments Ġfrom ĠJanuary Ġ2015 Ġthrough ĠJanuary Ġ2017 . ĠWe Ġfind Ġthat Ġpolitical Ġdiscussions Ġon ĠReddit Ġbecame Ġincreasingly Ġless Ġcivil ĠâĢĵ Ġas Ġmeasured Ġby Ġthe Ġincidence Ġof Ġoffensive Ġcomments ĠâĢĵ Ġduring Ġthe Ġ2016 Ġgeneral Ġelection Ġcampaign . ĠIn Ġfact , Ġduring Ġthe Ġheight Ġof Ġthe Ġcampaign , Ġnearly Ġone Ġof Ġevery Ġ10 Ġcomments Ġposted Ġon Ġa Ġpolitical Ġsubreddit Ġwere Ġclassified Ġas Ġoffensive . ĠOffensive Ġcomments Ġalso Ġreceived Ġmore Ġpositive Ġfeedback Ġfrom Ġthe Ġcommunity , Ġeven Ġthough Ġmost Ġof Ġthe Ġaccounts Ġresponsible Ġfor Ġsuch Ġcomments Ġappear Ġto Ġbe Ġthrow away Ġaccounts . ĠWhile Ġoffensive Ġposts Ġwere Ġincreasingly Ġcommon Ġon Ġpolitical Ġsubreddits Ġas Ġthe Ġcampaign Ġwore Ġon , Ġthere Ġwas Ġno Ġsuch Ġincrease Ġin Ġnon - political Ġfor a . ĠThis Ġcontrast Ġprovides Ġadditional Ġevidence Ġthat Ġthe Ġincreasing Ġuse Ġof Ġoffensive Ġspeech Ġwas Ġdirectly Ġrelated Ġto Ġthe Ġramp ing Ġup Ġof Ġthe Ġgeneral Ġelection Ġcampaign Ġfor Ġpresident . </s> Even Ġthough Ġour Ġstudy Ġrelies Ġon Ġjust Ġa Ġsingle Ġsource Ġof Ġonline Ġpolitical Ġdiscussions ĠâĢĵ ĠReddit , Ġwe Ġbelieve Ġthat Ġour Ġfindings Ġgenerally Ġpresent Ġan Ġupper - bound Ġon Ġthe Ġincidence Ġof Ġoff ens iveness Ġin Ġonline Ġpolitical Ġdiscussions Ġfor Ġthe Ġfollowing Ġreasons : ĠFirst , ĠReddit Ġallows Ġthe Ġuse Ġof Ġpseud onymous Ġidentities Ġthat Ġenables Ġthe Ġonline Ġdis in hibition Ġeffect Ġ( un like Ġsocial - media Ġplatforms Ġsuch Ġas ĠFacebook ). ĠSecond , ĠReddit Ġenables Ġusers Ġto Ġengage Ġin Ġcomplex Ġdiscussions Ġthat Ġare Ġunrestricted Ġin Ġlength Ġ( un like ĠTwitter ). ĠFinally , ĠReddit Ġis Ġknown Ġfor Ġenabling Ġa Ġgeneral Ġculture Ġof Ġfree Ġspeech Ġand Ġdeleg ating Ġcontent Ġregulation Ġto Ġmoderators Ġof Ġindividual Ġsubreddits . ĠThis Ġprovides Ġusers Ġholding Ġfringe Ġviews Ġa Ġvariety Ġof Ġsubreddits Ġin Ġwhich Ġtheir Ġcontent Ġis Ġwelcome . </s> Our Ġfindings Ġprovide Ġa Ġunique Ġand Ġimportant Ġmapping Ġof Ġthe Ġincreasing Ġinc iv ility Ġof Ġonline Ġpolitical Ġdiscourse Ġduring Ġthe Ġ2016 Ġcampaign . ĠSuch Ġan Ġinvestigation Ġis Ġimportant Ġbecause Ġscholars Ġhave Ġoutlined Ġmany Ġconsequences Ġfor Ġinc iv ility Ġin Ġpolitical Ġdiscourse . ĠInc iv ility Ġtends Ġto ĠâĢ ľ turn Ġoff âĢ Ŀ Ġpolitical Ġmoderates , Ġleading Ġto Ġincreasing Ġpolarization Ġamong Ġthose Ġwho Ġare Ġactively Ġengaged Ġin Ġpolitics ĠB IB REF 4 Ġ. ĠMore Ġimportantly , Ġa Ġlack Ġof Ġciv ility Ġin Ġpolitical Ġdiscussions Ġgenerally Ġreduces Ġthe Ġdegree Ġto Ġwhich Ġpeople Ġview Ġopponents Ġas Ġholding Ġlegitimate Ġviewpoints . ĠThis Ġdynamic Ġmakes Ġit Ġdifficult Ġfor Ġpeople Ġto Ġfind Ġcommon Ġground Ġwith Ġthose Ġwho Ġdisagree Ġwith Ġthem ĠB IB REF 15 Ġand Ġit Ġmay Ġultimately Ġlead Ġcitizens Ġto Ġview Ġelectoral Ġvictories Ġby Ġopponents Ġas Ġlacking Ġlegitimacy ĠB IB REF 1 Ġ. ĠThus , Ġfrom Ġa Ġnormative Ġstandpoint , Ġthe Ġfact Ġthat Ġthe Ġ2016 Ġcampaign Ġsparked Ġa Ġmarked Ġincrease Ġin Ġthe Ġoff ens iveness Ġof Ġpolitical Ġcomments Ġposted Ġto ĠReddit Ġis Ġof Ġconcern Ġin Ġits Ġown Ġright ; Ġthat Ġthe Ġincidence Ġof Ġoffensive Ġpolitical Ġcomments Ġhas Ġremained Ġhigh Ġeven Ġthree Ġmonths Ġafter Ġthe Ġelection Ġis Ġall Ġthe Ġmore Ġtroubling . </s> In Ġfuture Ġwork , Ġwe Ġwill Ġextend Ġour Ġanalysis Ġof ĠReddit Ġback Ġto Ġ2007 Ġwith Ġthe Ġaim Ġof Ġform ulating Ġa Ġmore Ġcomplete Ġunderstanding Ġof Ġthe Ġdynamics Ġof Ġpolitical Ġinc iv ility . ĠFor Ġexample , Ġwe Ġseek Ġto Ġunderstand Ġwhether Ġthe Ġhigh Ġincidence Ġof Ġoffensive Ġspeech Ġwe Ġfind Ġin Ġ2016 Ġis Ġunique Ġto Ġthis Ġparticular Ġcampaign Ġor Ġif Ġprevious Ġpresidential Ġcampaigns Ġwitnessed Ġsimilar Ġspikes Ġin Ġinc iv ility . ĠWe Ġwill Ġalso Ġexamine Ġwhether Ġthere Ġis Ġa Ġmore Ġgeneral Ġlong - term Ġtrend Ġtoward Ġoffensive Ġonline Ġpolitical Ġspeech , Ġwhich Ġwould Ġbe Ġconsistent Ġwith Ġwhat Ġscholars Ġhave Ġfound Ġwhen Ġstudying Ġpolitical Ġelites ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ.
Introduction The apparent rise in political incivility has attracted substantial attention from scholars in recent years. These studies have largely focused on the extent to which politicians and elected officials are increasingly employing rhetoric that appears to violate norms of civility BIBREF0 , BIBREF1 . For the purposes of our work, we use the incidence of offensive rhetoric as a stand in for incivility. The 2016 US presidential election was an especially noteworthy case in this regard, particularly in terms of Donald Trump's campaign which frequently violated norms of civility both in how he spoke about broad groups in the public (such as Muslims, Mexicans, and African Americans) and the attacks he leveled at his opponents BIBREF2 . The consequences of incivility are thought to be crucial to the functioning of democracy since “public civility and interpersonal politeness sustain social harmony and allow people who disagree with one another to maintain ongoing relationships" BIBREF3 . While political incivility appears to be on the rise among elites, it is less clear whether this is true among the mass public as well. Is political discourse particularly lacking in civility compared to discourse more generally? Does the incivility of mass political discourse respond to the dynamics of political campaigns? Addressing these questions has been difficult for political scientists because traditional tools for studying mass behavior, such as public opinion surveys, are ill-equipped to measure how citizens discuss politics with one another. Survey data does reveal that the public tends to perceive politics as becoming increasingly less civil during the course of a political campaign BIBREF4 . Yet, it is not clear whether these perceptions match the reality, particularly in terms of the types of discussions that citizens have with each other. An additional question is how incivility is received by others. On one hand, violations of norms regarding offensive discourse may be policed by members of a community, rendering such speech ineffectual. On the other hand, offensive speech may be effective as a means for drawing attention to a particular argument. Indeed, there is evidence that increasing incivility in political speech results in higher levels of attention from the public BIBREF1 . During the 2016 campaign, the use of swearing in comments posted on Donald Trump's YouTube channel tended to result in additional responses that mimicked such swearing BIBREF5 . Thus, offensive speech in online fora may attract more attention from the community and lead to the spread of even more offensive speech in subsequent posts. To address these questions regarding political incivility, we examine the use of offensive speech in political discussions housed on Reddit. Scholars tend to define uncivil discourse as “communication that violates the norms of politeness" BIBREF1 a definition that clearly includes offensive remarks. Reddit fora represent a “most likely" case for the study of offensive political speech due its strong free speech culture BIBREF6 and the ability of participants to use pseudonymous identities. That is, if political incivility in the public did increase during the 2016 campaign, this should be especially evident on fora such as Reddit. Tracking Reddit discussions throughout all of 2015 and 2016, we find that online political discussions became increasingly more offensive as the general election campaign intensified. By comparison, discussions on non-political subreddits did not become increasingly offensive during this period. Additionally, we find that the presence of offensive comments did not subside even three months after the election. Datasets Our study makes use of multiple datasets in order to identify and characterize trends in offensive speech. The CrowdFlower hate speech dataset. The CrowdFlower hate speech dataset BIBREF7 contains 14.5K tweets, each receiving labels from at least three contributors. Contributors were allowed to classify each tweet into one of three classes: Not Offensive (NO), Offensive but not hateful (O), and Offensive and hateful (OH). Of the 14.5K tweets, only 37.6% had a decisive class – i.e., the same class was assigned by all contributors. For indecisive cases, the majority class was selected and a class confidence score (fraction of contributors that selected the majority class) was made available. Using this approach, 50.4%, 33.1%, and 16.5% of the tweets were categorized as NO, O, and OH, respectively. Since our goal is to identify any offensive speech (not just hate speech), we consolidate assigned classes into Offensive and Not Offensive by relabeling OH tweets as Offensive. We use this modified dataset to train, validate, and test our offensive speech classifier. To the best of our knowledge, this is the only dataset that provides offensive and not offensive annotations to a large dataset. Offensive word lists. We also use two offensive word lists as auxiliary input to our classifier: (1) The Hatebase hate speech vocabulary BIBREF8 consisting of 1122 hateful words and (2) 422 offensive words banned from Google's What Do You Love project BIBREF9 . Reddit comments dataset. Finally, after building our offensive speech classifier using the above datasets, we use it to classify comments made on Reddit. While the complete Reddit dataset contains 2B comments made between the period of January 2015 and January 2017, we only analyze only 168M. We select comments to be analyzed using the following process: (1) we exclude comments shorter than 10 characters in length, (2) we exclude comments made by [deleted] authors, and (3) we randomly sample and include 10% of all remaining comments. We categorize comments made in any of 21 popular political subreddits as political and the remainder as apolitical. Our final dataset contains 129M apolitical and 39M political comments. fig:comment-timeline shows the number of comments in our dataset that were made during each week included in our study. We see an increasing number of political comments per week starting in February 2016 – the start of the 2016 US presidential primaries. Offensive Speech Classification In order to identify offensive speech, we propose a fully automated technique that classifies comments into two classes: Offensive and Not Offensive. Classification approach At a high-level, our approach works as follows: Build a word embedding. We construct a 100-dimensional word embedding using all comments from our complete Reddit dataset (2B comments). Construct a hate vector. We construct a list of offensive and hateful words identified from external data and map them into a single vector within the high-dimensional word embedding. Text transformation and classification. Finally, we transform text to be classified into scalars representing their distance from the constructed hate vector and use these as input to a Random Forest classifier. Building a word embedding. At a high-level, a word embedding maps words into a high-dimensional continuous vector space in such a way that semantic similarities between words are maintained. This mapping is achieved by exploiting the distributional properties of words and their occurrences in the input corpus. Rather than using an off-the-shelf word embedding (e.g., the GloVe embeddings BIBREF10 trained using public domain data sources such as Wikipedia and news articles), we construct a 100-dimensional embedding using the complete Reddit dataset (2B comments) as the input corpus. The constructed embedding consists of over 400M unique words (words occurring less than 25 times in the entire corpus are excluded) using the Word2Vec BIBREF11 implementation provided by the Gensim library BIBREF12 . Prior to constructing the embedding, we perform stop-word removal and lemmatize each word in the input corpus using the SpaCy NLP framework BIBREF13 . The main reason for building a custom embedding is to ensure that our embeddings capture semantics specific to the data being measured (Reddit) – e.g., while the word “karma” in the non-Reddit context may be associated with spirituality, it is associated with points (comment and submission scores) on Reddit. Constructing a hate vector. We use two lists of words associated with hate BIBREF8 and offensive BIBREF9 speech to construct a hate vector in our word embedding. This is done by mapping each word in the list into the 100-dimensional embedding and computing the mean vector. This vector represents the average of all known offensive words. The main idea behind creating a hate vector is to capture the point (in our embedding) to which the most offensive observed comments are likely to be near. Although clustering our offensive word lists into similar groups and constructing multiple hate vectors – one for each cluster – results in marginally better accuracy for our classifier, we use this approach due to the fact that our classification cost grows linearly with the number of hate vectors – i.e., we need to perform INLINEFORM0 distance computations per hate vector to classify string INLINEFORM1 . Transforming and classifying text. We first remove stop-words and perform lemmatization of each word in the text to be classified. We then obtain the vector representing each word in the text and compute its similarity to the constructed hate vector using the cosine similarity metric. A 0-vector is used to represent words in the text that are not present in the embedding. Finally, the maximum cosine similarity score is used to represent the comment. Equation EQREF7 shows the transformation function on a string INLINEFORM0 = INLINEFORM1 where INLINEFORM2 is the vector representing the INLINEFORM3 lemmatized non-stop-word, INLINEFORM4 is the cosine-similarity function, and INLINEFORM5 is the hate vector. DISPLAYFORM0 In words, the numerical value assigned to a text is the cosine similarity between the hate vector and the vector representing the word (in the text) closest to the hate vector. This approach allows us to transform a string of text into a single numerical value that captures its semantic similarity to the most offensive comment. We use these scalars as input to a random forest classifier to perform classification into Offensive and Not Offensive classes. fig:reduced-dimension-classes shows the proximity of Offensive and Non Offensive comments to our constructed hate vector after using t-distributed Stochastic Neighbor Embedding (t-SNE) BIBREF14 to reduce our 100-dimension vector space into 2 dimensions. Classifier evaluation We now present results to (1) validate our choice of classifier and (2) demonstrate the impact of training/validation sample count on our classifiers performance. Classifier selection methodology. To identify the most suitable classifier for classifying the scalars associated with each text, we perform evaluations using the stochastic gradient descent, naive bayes, decision tree, and random forest classifiers. For each classifier, we split the CrowdFlower hate speech dataset into a training/validation set (75%), and a holdout set (25%). We perform 10-fold cross-validation on the training/validation set to identify the best classifier model and parameters (using a grid search). Based on the results of this evaluation, we select a 100-estimator entropy-based splitting random forest model as our classifier. tab:classifiers shows the mean accuracy and F1-score for each evaluated classifier during the 10-fold cross-validation. Real-world classifier performance. To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively. These results show that in addition to superior accuracy during training and validation, our chosen classifier is also robust against over-fitting. Impact of dataset quality and size. To understand how the performance of our chosen classifier model and parameters are impacted by: (1) the quality and consistency of manually assigned classes in the CrowdFlower dataset and (2) the size of the dataset, we re-evaluate the classifier while only considering tweets having a minimum confidence score and varying the size of the holdout set. Specifically, our experiments considered confidence thresholds of 0 (all tweets considered), .35 (only tweets where at least 35% of contributors agreed on a class were considered), and .70 (only tweets where at least 70% of the contributors agreed on a class were considered) and varied the holdout set sizes between 5% and 95% of all tweets meeting the confidence threshold set for the experiment. The results illustrated in fig:classifier-performance show the performance of the classifier while evaluating the corresponding holdout set. We make several conclusions from these results: Beyond a (fairly low) threshold, the size of the training and validation set has little impact on classifier performance. We see that the accuracy, precision, and recall have, at best, marginal improvements with holdout set sizes smaller than 60%. This implies that the CrowdFlower dataset is sufficient for building an offensive speech classifier. Quality of manual labeling has a significant impact on the accuracy and precision of the classifier. Using only tweets which had at least 70% of contributors agreeing on a class resulted in between 5-7% higher accuracy and up to 5% higher precision. Our classifier achieves precision of over 95% and recall of over 85% when considering only high confidence samples. This implies that the classifier is more likely to underestimate the presence of offensive speech – i.e., our results likely provide a lower-bound on the quantity of observed offensive speech. Measurements In this section we quantify and characterize offensiveness in the political and general contexts using our offensive speech classifier and the Reddit comments dataset which considers a random sample of comments made between January 2015 and January 2017. Offensiveness over time. We find that on average 8.4% of all political comments are offensive compared to 7.8% of all apolitical comments. fig:offensive-speech-timeline illustrates the fraction of offensive political and apolitical comments made during each week in our study. We see that while the fraction of apolitical offensive comments has stayed steady, there has been an increase in the fraction of offensive political comments starting in July 2016. Notably, this increase is observed after the conclusion of the US presidential primaries and during the period of the Democratic and Republican National Conventions and does not reduce even after the conclusion of the US presidential elections held on November 8. Participants in political subreddits were 2.6% more likely to observe offensive comments prior to July 2016 but 14.9% more likely to observe offensive comments from July 2016 onwards. Reactions to offensive comments. We use the comment score, roughly the difference between up-votes and down-votes received, as a proxy for understanding how users reacted to offensive comments. We find that comments that were offensive: (1) on average, had a higher score than non-offensive comments (average scores: 8.9 vs. 6.7) and (2) were better received when they were posted in the general context than in the political context (average scores: 8.6 vs. 9.0). To understand how peoples reactions to offensive comments evolved over time, fig:offensive-scores-timeline shows the average scores received by offensive comments over time. Again, we observe an increasing trend in average scores received by offensive and political comments after July 2016. Characteristics of offensive authors. We now focus on understanding characteristics of authors of offensive comments. Specifically, we are interested in identifying the use of throwaway and troll accounts. For the purposes of this study, we characterize throwaway accounts as those with less than five total comments – i.e., accounts that are used to make a small number of comments. Similarly, we define troll accounts as those with over 15 comments of which over 75% are classified as offensive – i.e., accounts that are used to make a larger number of comments, of which a significant majority are offensive. We find that 93.7% of the accounts which have over 75% of their comments tagged as offensive are throwaways and 1.3% are trolls. Complete results are illustrated in fig:offensive-authors-cdf. Characteristics of offensive communities. We breakdown subreddits by their category (default, political, and other) and identify the most and least offensive communities in each. fig:subreddit-cdf shows the distribution of the fraction of offensive comments in each category and tab:subreddit-breakdown shows the most and least offensive subreddits in the political and default categories (we exclude the “other” category due to the inappropriateness of their names). We find that less than 19% of all subreddits (that account for over 23% of all comments) have over 10% offensive comments. Further, several default and political subreddits fall in this category, including r/the INLINEFORM0 donald – the most offensive political subreddit and the subreddit dedicated to the US President. Flow of offensive authors. Finally, we uncover patterns in the movement of offensive authors between communities. In fig:offensive-flow we show the communities in which large number of authors of offensive content on the r/politics subreddit had previously made offensive comments (we refer to these communities as sources). Unsurprisingly, the most popular sources belonged to the default subreddits (e.g., r/worldnews, r/wtf, r/videos, r/askreddit, and r/news). We find that several other political subreddits also serve as large sources of offensive authors. In fact, the subreddits dedicated to the three most popular US presidential candidates – r/the INLINEFORM0 donald, r/sandersforpresident, and r/hillaryclinton rank in the top three. Finally, outside of the default and political subreddits, we find that r/nfl, r/conspiracy, r/dota2, r/reactiongifs, r/blackpeopletwitter, and r/imgoingtohellforthis were the largest sources of offensive political authors. Conclusions and Future Work We develop and validate an offensive speech classifier to quantify the presence of offensive online comments from January 2015 through January 2017. We find that political discussions on Reddit became increasingly less civil – as measured by the incidence of offensive comments – during the 2016 general election campaign. In fact, during the height of the campaign, nearly one of every 10 comments posted on a political subreddit were classified as offensive. Offensive comments also received more positive feedback from the community, even though most of the accounts responsible for such comments appear to be throwaway accounts. While offensive posts were increasingly common on political subreddits as the campaign wore on, there was no such increase in non-political fora. This contrast provides additional evidence that the increasing use of offensive speech was directly related to the ramping up of the general election campaign for president. Even though our study relies on just a single source of online political discussions – Reddit, we believe that our findings generally present an upper-bound on the incidence of offensiveness in online political discussions for the following reasons: First, Reddit allows the use of pseudonymous identities that enables the online disinhibition effect (unlike social-media platforms such as Facebook). Second, Reddit enables users to engage in complex discussions that are unrestricted in length (unlike Twitter). Finally, Reddit is known for enabling a general culture of free speech and delegating content regulation to moderators of individual subreddits. This provides users holding fringe views a variety of subreddits in which their content is welcome. Our findings provide a unique and important mapping of the increasing incivility of online political discourse during the 2016 campaign. Such an investigation is important because scholars have outlined many consequences for incivility in political discourse. Incivility tends to “turn off” political moderates, leading to increasing polarization among those who are actively engaged in politics BIBREF4 . More importantly, a lack of civility in political discussions generally reduces the degree to which people view opponents as holding legitimate viewpoints. This dynamic makes it difficult for people to find common ground with those who disagree with them BIBREF15 and it may ultimately lead citizens to view electoral victories by opponents as lacking legitimacy BIBREF1 . Thus, from a normative standpoint, the fact that the 2016 campaign sparked a marked increase in the offensiveness of political comments posted to Reddit is of concern in its own right; that the incidence of offensive political comments has remained high even three months after the election is all the more troubling. In future work, we will extend our analysis of Reddit back to 2007 with the aim of formulating a more complete understanding of the dynamics of political incivility. For example, we seek to understand whether the high incidence of offensive speech we find in 2016 is unique to this particular campaign or if previous presidential campaigns witnessed similar spikes in incivility. We will also examine whether there is a more general long-term trend toward offensive online political speech, which would be consistent with what scholars have found when studying political elites BIBREF16 , BIBREF17 .
What was their system's performance?
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What Ġwas Ġtheir Ġsystem 's Ġperformance ?
acc uracy Ġand ĠF 1 - score Ġof Ġ89 . 6 % Ġand Ġ89 . 2 %, Ġrespectively
<s> What Ġwas Ġtheir Ġsystem 's Ġperformance ? </s> Introduction </s> The Ġapparent Ġrise Ġin Ġpolitical Ġinc iv ility Ġhas Ġattracted Ġsubstantial Ġattention Ġfrom Ġscholars Ġin Ġrecent Ġyears . ĠThese Ġstudies Ġhave Ġlargely Ġfocused Ġon Ġthe Ġextent Ġto Ġwhich Ġpoliticians Ġand Ġelected Ġofficials Ġare Ġincreasingly Ġemploying Ġrhetoric Ġthat Ġappears Ġto Ġviolate Ġnorms Ġof Ġciv ility ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ. ĠFor Ġthe Ġpurposes Ġof Ġour Ġwork , Ġwe Ġuse Ġthe Ġincidence Ġof Ġoffensive Ġrhetoric Ġas Ġa Ġstand Ġin Ġfor Ġinc iv ility . ĠThe Ġ2016 ĠUS Ġpresidential Ġelection Ġwas Ġan Ġespecially Ġnoteworthy Ġcase Ġin Ġthis Ġregard , Ġparticularly Ġin Ġterms Ġof ĠDonald ĠTrump 's Ġcampaign Ġwhich Ġfrequently Ġviolated Ġnorms Ġof Ġciv ility Ġboth Ġin Ġhow Ġhe Ġspoke Ġabout Ġbroad Ġgroups Ġin Ġthe Ġpublic Ġ( such Ġas ĠMuslims , ĠMexicans , Ġand ĠAfrican ĠAmericans ) Ġand Ġthe Ġattacks Ġhe Ġleveled Ġat Ġhis Ġopponents ĠB IB REF 2 Ġ. ĠThe Ġconsequences Ġof Ġinc iv ility Ġare Ġthought Ġto Ġbe Ġcrucial Ġto Ġthe Ġfunctioning Ġof Ġdemocracy Ġsince ĠâĢ ľ public Ġciv ility Ġand Ġinterpersonal Ġpolit eness Ġsustain Ġsocial Ġharmony Ġand Ġallow Ġpeople Ġwho Ġdisagree Ġwith Ġone Ġanother Ġto Ġmaintain Ġongoing Ġrelationships " ĠB IB REF 3 Ġ. </s> While Ġpolitical Ġinc iv ility Ġappears Ġto Ġbe Ġon Ġthe Ġrise Ġamong Ġelites , Ġit Ġis Ġless Ġclear Ġwhether Ġthis Ġis Ġtrue Ġamong Ġthe Ġmass Ġpublic Ġas Ġwell . ĠIs Ġpolitical Ġdiscourse Ġparticularly Ġlacking Ġin Ġciv ility Ġcompared Ġto Ġdiscourse Ġmore Ġgenerally ? ĠDoes Ġthe Ġinc iv ility Ġof Ġmass Ġpolitical Ġdiscourse Ġrespond Ġto Ġthe Ġdynamics Ġof Ġpolitical Ġcampaigns ? ĠAdd ressing Ġthese Ġquestions Ġhas Ġbeen Ġdifficult Ġfor Ġpolitical Ġscientists Ġbecause Ġtraditional Ġtools Ġfor Ġstudying Ġmass Ġbehavior , Ġsuch Ġas Ġpublic Ġopinion Ġsurveys , Ġare Ġill - equipped Ġto Ġmeasure Ġhow Ġcitizens Ġdiscuss Ġpolitics Ġwith Ġone Ġanother . ĠSurvey Ġdata Ġdoes Ġreveal Ġthat Ġthe Ġpublic Ġtends Ġto Ġperceive Ġpolitics Ġas Ġbecoming Ġincreasingly Ġless Ġcivil Ġduring Ġthe Ġcourse Ġof Ġa Ġpolitical Ġcampaign ĠB IB REF 4 Ġ. ĠYet , Ġit Ġis Ġnot Ġclear Ġwhether Ġthese Ġperceptions Ġmatch Ġthe Ġreality , Ġparticularly Ġin Ġterms Ġof Ġthe Ġtypes Ġof Ġdiscussions Ġthat Ġcitizens Ġhave Ġwith Ġeach Ġother . </s> An Ġadditional Ġquestion Ġis Ġhow Ġinc iv ility Ġis Ġreceived Ġby Ġothers . ĠOn Ġone Ġhand , Ġviolations Ġof Ġnorms Ġregarding Ġoffensive Ġdiscourse Ġmay Ġbe Ġpolic ed Ġby Ġmembers Ġof Ġa Ġcommunity , Ġrendering Ġsuch Ġspeech Ġin effect ual . ĠOn Ġthe Ġother Ġhand , Ġoffensive Ġspeech Ġmay Ġbe Ġeffective Ġas Ġa Ġmeans Ġfor Ġdrawing Ġattention Ġto Ġa Ġparticular Ġargument . ĠIndeed , Ġthere Ġis Ġevidence Ġthat Ġincreasing Ġinc iv ility Ġin Ġpolitical Ġspeech Ġresults Ġin Ġhigher Ġlevels Ġof Ġattention Ġfrom Ġthe Ġpublic ĠB IB REF 1 Ġ. ĠDuring Ġthe Ġ2016 Ġcampaign , Ġthe Ġuse Ġof Ġswearing Ġin Ġcomments Ġposted Ġon ĠDonald ĠTrump 's ĠYouTube Ġchannel Ġtended Ġto Ġresult Ġin Ġadditional Ġresponses Ġthat Ġmim icked Ġsuch Ġswearing ĠB IB REF 5 Ġ. ĠThus , Ġoffensive Ġspeech Ġin Ġonline Ġfor a Ġmay Ġattract Ġmore Ġattention Ġfrom Ġthe Ġcommunity Ġand Ġlead Ġto Ġthe Ġspread Ġof Ġeven Ġmore Ġoffensive Ġspeech Ġin Ġsubsequent Ġposts . </s> To Ġaddress Ġthese Ġquestions Ġregarding Ġpolitical Ġinc iv ility , Ġwe Ġexamine Ġthe Ġuse Ġof Ġoffensive Ġspeech Ġin Ġpolitical Ġdiscussions Ġhoused Ġon ĠReddit . ĠScholars Ġtend Ġto Ġdefine Ġunc ivil Ġdiscourse Ġas ĠâĢ ľ communication Ġthat Ġviolates Ġthe Ġnorms Ġof Ġpolit eness " ĠB IB REF 1 Ġa Ġdefinition Ġthat Ġclearly Ġincludes Ġoffensive Ġremarks . ĠReddit Ġfor a Ġrepresent Ġa ĠâĢ ľ most Ġlikely " Ġcase Ġfor Ġthe Ġstudy Ġof Ġoffensive Ġpolitical Ġspeech Ġdue Ġits Ġstrong Ġfree Ġspeech Ġculture ĠB IB REF 6 Ġand Ġthe Ġability Ġof Ġparticipants Ġto Ġuse Ġpseud onymous Ġidentities . ĠThat Ġis , Ġif Ġpolitical Ġinc iv ility Ġin Ġthe Ġpublic Ġdid Ġincrease Ġduring Ġthe Ġ2016 Ġcampaign , Ġthis Ġshould Ġbe Ġespecially Ġevident Ġon Ġfor a Ġsuch Ġas ĠReddit . ĠTracking ĠReddit Ġdiscussions Ġthroughout Ġall Ġof Ġ2015 Ġand Ġ2016 , Ġwe Ġfind Ġthat Ġonline Ġpolitical Ġdiscussions Ġbecame Ġincreasingly Ġmore Ġoffensive Ġas Ġthe Ġgeneral Ġelection Ġcampaign Ġintensified . ĠBy Ġcomparison , Ġdiscussions Ġon Ġnon - political Ġsubreddits Ġdid Ġnot Ġbecome Ġincreasingly Ġoffensive Ġduring Ġthis Ġperiod . ĠAdditionally , Ġwe Ġfind Ġthat Ġthe Ġpresence Ġof Ġoffensive Ġcomments Ġdid Ġnot Ġsub side Ġeven Ġthree Ġmonths Ġafter Ġthe Ġelection . </s> Dat as ets </s> Our Ġstudy Ġmakes Ġuse Ġof Ġmultiple Ġdatasets Ġin Ġorder Ġto Ġidentify Ġand Ġcharacterize Ġtrends Ġin Ġoffensive Ġspeech . </s> The ĠCrowd Fl ower Ġhate Ġspeech Ġdataset . ĠThe ĠCrowd Fl ower Ġhate Ġspeech Ġdataset ĠB IB REF 7 Ġcontains Ġ14 . 5 K Ġtweets , Ġeach Ġreceiving Ġlabels Ġfrom Ġat Ġleast Ġthree Ġcontributors . ĠContribut ors Ġwere Ġallowed Ġto Ġclassify Ġeach Ġtweet Ġinto Ġone Ġof Ġthree Ġclasses : ĠNot ĠOffensive Ġ( NO ), ĠOffensive Ġbut Ġnot Ġhateful Ġ( O ), Ġand ĠOffensive Ġand Ġhateful Ġ( OH ). ĠOf Ġthe Ġ14 . 5 K Ġtweets , Ġonly Ġ37 . 6 % Ġhad Ġa Ġdecisive Ġclass ĠâĢĵ Ġi . e ., Ġthe Ġsame Ġclass Ġwas Ġassigned Ġby Ġall Ġcontributors . ĠFor Ġindec isive Ġcases , Ġthe Ġmajority Ġclass Ġwas Ġselected Ġand Ġa Ġclass Ġconfidence Ġscore Ġ( f raction Ġof Ġcontributors Ġthat Ġselected Ġthe Ġmajority Ġclass ) Ġwas Ġmade Ġavailable . ĠUsing Ġthis Ġapproach , Ġ50 . 4 %, Ġ33 . 1 %, Ġand Ġ16 . 5 % Ġof Ġthe Ġtweets Ġwere Ġcategorized Ġas ĠNO , ĠO , Ġand ĠOH , Ġrespectively . ĠSince Ġour Ġgoal Ġis Ġto Ġidentify Ġany Ġoffensive Ġspeech Ġ( not Ġjust Ġhate Ġspeech ), Ġwe Ġconsolidate Ġassigned Ġclasses Ġinto ĠOffensive Ġand ĠNot ĠOffensive Ġby Ġrel abel ing ĠOH Ġtweets Ġas ĠOffensive . ĠWe Ġuse Ġthis Ġmodified Ġdataset Ġto Ġtrain , Ġvalidate , Ġand Ġtest Ġour Ġoffensive Ġspeech Ġclass ifier . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , Ġthis Ġis Ġthe Ġonly Ġdataset Ġthat Ġprovides Ġoffensive Ġand Ġnot Ġoffensive Ġannotations Ġto Ġa Ġlarge Ġdataset . </s> Off ensive Ġword Ġlists . ĠWe Ġalso Ġuse Ġtwo Ġoffensive Ġword Ġlists Ġas Ġauxiliary Ġinput Ġto Ġour Ġclass ifier : Ġ( 1 ) ĠThe ĠHate base Ġhate Ġspeech Ġvocabulary ĠB IB REF 8 Ġconsisting Ġof Ġ112 2 Ġhateful Ġwords Ġand Ġ( 2 ) Ġ422 Ġoffensive Ġwords Ġbanned Ġfrom ĠGoogle 's ĠWhat ĠDo ĠYou ĠLove Ġproject ĠB IB REF 9 Ġ. </s> Reddit Ġcomments Ġdataset . ĠFinally , Ġafter Ġbuilding Ġour Ġoffensive Ġspeech Ġclass ifier Ġusing Ġthe Ġabove Ġdatasets , Ġwe Ġuse Ġit Ġto Ġclassify Ġcomments Ġmade Ġon ĠReddit . ĠWhile Ġthe Ġcomplete ĠReddit Ġdataset Ġcontains Ġ2 B Ġcomments Ġmade Ġbetween Ġthe Ġperiod Ġof ĠJanuary Ġ2015 Ġand ĠJanuary Ġ2017 , Ġwe Ġonly Ġanalyze Ġonly Ġ168 M . ĠWe Ġselect Ġcomments Ġto Ġbe Ġanalyzed Ġusing Ġthe Ġfollowing Ġprocess : Ġ( 1 ) Ġwe Ġexclude Ġcomments Ġshorter Ġthan Ġ10 Ġcharacters Ġin Ġlength , Ġ( 2 ) Ġwe Ġexclude Ġcomments Ġmade Ġby Ġ[ de leted ] Ġauthors , Ġand Ġ( 3 ) Ġwe Ġrandomly Ġsample Ġand Ġinclude Ġ10 % Ġof Ġall Ġremaining Ġcomments . ĠWe Ġcategor ize Ġcomments Ġmade Ġin Ġany Ġof Ġ21 Ġpopular Ġpolitical Ġsubreddits Ġas Ġpolitical Ġand Ġthe Ġremainder Ġas Ġap olitical . ĠOur Ġfinal Ġdataset Ġcontains Ġ129 M Ġap olitical Ġand Ġ39 M Ġpolitical Ġcomments . Ġfig : comment - tim eline Ġshows Ġthe Ġnumber Ġof Ġcomments Ġin Ġour Ġdataset Ġthat Ġwere Ġmade Ġduring Ġeach Ġweek Ġincluded Ġin Ġour Ġstudy . ĠWe Ġsee Ġan Ġincreasing Ġnumber Ġof Ġpolitical Ġcomments Ġper Ġweek Ġstarting Ġin ĠFebruary Ġ2016 ĠâĢĵ Ġthe Ġstart Ġof Ġthe Ġ2016 ĠUS Ġpresidential Ġprimaries . </s> Off ensive ĠSpeech ĠClassification </s> In Ġorder Ġto Ġidentify Ġoffensive Ġspeech , Ġwe Ġpropose Ġa Ġfully Ġautomated Ġtechnique Ġthat Ġclass ifies Ġcomments Ġinto Ġtwo Ġclasses : ĠOffensive Ġand ĠNot ĠOffensive . </s> Class ification Ġapproach </s> At Ġa Ġhigh - level , Ġour Ġapproach Ġworks Ġas Ġfollows : </s> Build Ġa Ġword Ġembed ding . ĠWe Ġconstruct Ġa Ġ100 - dimensional Ġword Ġembed ding Ġusing Ġall Ġcomments Ġfrom Ġour Ġcomplete ĠReddit Ġdataset Ġ( 2 B Ġcomments ). </s> Construct Ġa Ġhate Ġvector . ĠWe Ġconstruct Ġa Ġlist Ġof Ġoffensive Ġand Ġhateful Ġwords Ġidentified Ġfrom Ġexternal Ġdata Ġand Ġmap Ġthem Ġinto Ġa Ġsingle Ġvector Ġwithin Ġthe Ġhigh - dimensional Ġword Ġembed ding . </s> Text Ġtransformation Ġand Ġclassification . ĠFinally , Ġwe Ġtransform Ġtext Ġto Ġbe Ġclassified Ġinto Ġscal ars Ġrepresenting Ġtheir Ġdistance Ġfrom Ġthe Ġconstructed Ġhate Ġvector Ġand Ġuse Ġthese Ġas Ġinput Ġto Ġa ĠRandom ĠForest Ġclass ifier . </s> Building Ġa Ġword Ġembed ding . ĠAt Ġa Ġhigh - level , Ġa Ġword Ġembed ding Ġmaps Ġwords Ġinto Ġa Ġhigh - dimensional Ġcontinuous Ġvector Ġspace Ġin Ġsuch Ġa Ġway Ġthat Ġsemantic Ġsimilarities Ġbetween Ġwords Ġare Ġmaintained . ĠThis Ġmapping Ġis Ġachieved Ġby Ġexploiting Ġthe Ġdistribution al Ġproperties Ġof Ġwords Ġand Ġtheir Ġoccurrences Ġin Ġthe Ġinput Ġcorpus . </s> Rather Ġthan Ġusing Ġan Ġoff - the - she lf Ġword Ġembed ding Ġ( e . g ., Ġthe ĠGl o Ve Ġembed d ings ĠB IB REF 10 Ġtrained Ġusing Ġpublic Ġdomain Ġdata Ġsources Ġsuch Ġas ĠWikipedia Ġand Ġnews Ġarticles ), Ġwe Ġconstruct Ġa Ġ100 - dimensional Ġembed ding Ġusing Ġthe Ġcomplete ĠReddit Ġdataset Ġ( 2 B Ġcomments ) Ġas Ġthe Ġinput Ġcorpus . ĠThe Ġconstructed Ġembed ding Ġconsists Ġof Ġover Ġ400 M Ġunique Ġwords Ġ( words Ġoccurring Ġless Ġthan Ġ25 Ġtimes Ġin Ġthe Ġentire Ġcorpus Ġare Ġexcluded ) Ġusing Ġthe ĠWord 2 V ec ĠB IB REF 11 Ġimplementation Ġprovided Ġby Ġthe ĠG ens im Ġlibrary ĠB IB REF 12 Ġ. ĠPrior Ġto Ġconstructing Ġthe Ġembed ding , Ġwe Ġperform Ġstop - word Ġremoval Ġand Ġle mm at ize Ġeach Ġword Ġin Ġthe Ġinput Ġcorpus Ġusing Ġthe ĠSpa Cy ĠN LP Ġframework ĠB IB REF 13 Ġ. ĠThe Ġmain Ġreason Ġfor Ġbuilding Ġa Ġcustom Ġembed ding Ġis Ġto Ġensure Ġthat Ġour Ġembed d ings Ġcapture Ġsemantics Ġspecific Ġto Ġthe Ġdata Ġbeing Ġmeasured Ġ( Reddit ) ĠâĢĵ Ġe . g ., Ġwhile Ġthe Ġword ĠâĢ ľ k arma âĢ Ŀ Ġin Ġthe Ġnon - Reddit Ġcontext Ġmay Ġbe Ġassociated Ġwith Ġspirituality , Ġit Ġis Ġassociated Ġwith Ġpoints Ġ( comment Ġand Ġsubmission Ġscores ) Ġon ĠReddit . </s> Construct ing Ġa Ġhate Ġvector . ĠWe Ġuse Ġtwo Ġlists Ġof Ġwords Ġassociated Ġwith Ġhate ĠB IB REF 8 Ġand Ġoffensive ĠB IB REF 9 Ġspeech Ġto Ġconstruct Ġa Ġhate Ġvector Ġin Ġour Ġword Ġembed ding . ĠThis Ġis Ġdone Ġby Ġmapping Ġeach Ġword Ġin Ġthe Ġlist Ġinto Ġthe Ġ100 - dimensional Ġembed ding Ġand Ġcomputing Ġthe Ġmean Ġvector . ĠThis Ġvector Ġrepresents Ġthe Ġaverage Ġof Ġall Ġknown Ġoffensive Ġwords . ĠThe Ġmain Ġidea Ġbehind Ġcreating Ġa Ġhate Ġvector Ġis Ġto Ġcapture Ġthe Ġpoint Ġ( in Ġour Ġembed ding ) Ġto Ġwhich Ġthe Ġmost Ġoffensive Ġobserved Ġcomments Ġare Ġlikely Ġto Ġbe Ġnear . ĠAlthough Ġclust ering Ġour Ġoffensive Ġword Ġlists Ġinto Ġsimilar Ġgroups Ġand Ġconstructing Ġmultiple Ġhate Ġvectors ĠâĢĵ Ġone Ġfor Ġeach Ġcluster ĠâĢĵ Ġresults Ġin Ġmarginally Ġbetter Ġaccuracy Ġfor Ġour Ġclass ifier , Ġwe Ġuse Ġthis Ġapproach Ġdue Ġto Ġthe Ġfact Ġthat Ġour Ġclassification Ġcost Ġgrows Ġlin early Ġwith Ġthe Ġnumber Ġof Ġhate Ġvectors ĠâĢĵ Ġi . e ., Ġwe Ġneed Ġto Ġperform ĠIN LINE FORM 0 Ġdistance Ġcomput ations Ġper Ġhate Ġvector Ġto Ġclassify Ġstring ĠIN LINE FORM 1 Ġ. </s> Trans forming Ġand Ġclass ifying Ġtext . ĠWe Ġfirst Ġremove Ġstop - words Ġand Ġperform Ġle mm at ization Ġof Ġeach Ġword Ġin Ġthe Ġtext Ġto Ġbe Ġclassified . ĠWe Ġthen Ġobtain Ġthe Ġvector Ġrepresenting Ġeach Ġword Ġin Ġthe Ġtext Ġand Ġcompute Ġits Ġsimilarity Ġto Ġthe Ġconstructed Ġhate Ġvector Ġusing Ġthe Ġcos ine Ġsimilarity Ġmetric . ĠA Ġ0 - vector Ġis Ġused Ġto Ġrepresent Ġwords Ġin Ġthe Ġtext Ġthat Ġare Ġnot Ġpresent Ġin Ġthe Ġembed ding . ĠFinally , Ġthe Ġmaximum Ġcos ine Ġsimilarity Ġscore Ġis Ġused Ġto Ġrepresent Ġthe Ġcomment . ĠEqu ation ĠEQ REF 7 Ġshows Ġthe Ġtransformation Ġfunction Ġon Ġa Ġstring ĠIN LINE FORM 0 Ġ= ĠIN LINE FORM 1 Ġwhere ĠIN LINE FORM 2 Ġis Ġthe Ġvector Ġrepresenting Ġthe ĠIN LINE FORM 3 Ġle mm at ized Ġnon - stop - word , ĠIN LINE FORM 4 Ġis Ġthe Ġcos ine - similar ity Ġfunction , Ġand ĠIN LINE FORM 5 Ġis Ġthe Ġhate Ġvector . ĠDIS PLAY FORM 0 </s> In Ġwords , Ġthe Ġnumerical Ġvalue Ġassigned Ġto Ġa Ġtext Ġis Ġthe Ġcos ine Ġsimilarity Ġbetween Ġthe Ġhate Ġvector Ġand Ġthe Ġvector Ġrepresenting Ġthe Ġword Ġ( in Ġthe Ġtext ) Ġclosest Ġto Ġthe Ġhate Ġvector . ĠThis Ġapproach Ġallows Ġus Ġto Ġtransform Ġa Ġstring Ġof Ġtext Ġinto Ġa Ġsingle Ġnumerical Ġvalue Ġthat Ġcaptures Ġits Ġsemantic Ġsimilarity Ġto Ġthe Ġmost Ġoffensive Ġcomment . ĠWe Ġuse Ġthese Ġscal ars Ġas Ġinput Ġto Ġa Ġrandom Ġforest Ġclass ifier Ġto Ġperform Ġclassification Ġinto ĠOffensive Ġand ĠNot ĠOffensive Ġclasses . Ġfig : red uced - dimension - classes Ġshows Ġthe Ġproximity Ġof ĠOffensive Ġand ĠNon ĠOffensive Ġcomments Ġto Ġour Ġconstructed Ġhate Ġvector Ġafter Ġusing Ġt - dist ributed ĠSt och astic ĠNeighbor ĠEmb edd ing Ġ( t - S NE ) ĠB IB REF 14 Ġto Ġreduce Ġour Ġ100 - dimension Ġvector Ġspace Ġinto Ġ2 Ġdimensions . </s> Class ifier Ġevaluation </s> We Ġnow Ġpresent Ġresults Ġto Ġ( 1 ) Ġvalidate Ġour Ġchoice Ġof Ġclass ifier Ġand Ġ( 2 ) Ġdemonstrate Ġthe Ġimpact Ġof Ġtraining / valid ation Ġsample Ġcount Ġon Ġour Ġclass ifiers Ġperformance . </s> Class ifier Ġselection Ġmethodology . ĠTo Ġidentify Ġthe Ġmost Ġsuitable Ġclass ifier Ġfor Ġclass ifying Ġthe Ġscal ars Ġassociated Ġwith Ġeach Ġtext , Ġwe Ġperform Ġevaluations Ġusing Ġthe Ġsto ch astic Ġgradient Ġdescent , Ġnaive Ġbay es , Ġdecision Ġtree , Ġand Ġrandom Ġforest Ġclass ifiers . ĠFor Ġeach Ġclass ifier , Ġwe Ġsplit Ġthe ĠCrowd Fl ower Ġhate Ġspeech Ġdataset Ġinto Ġa Ġtraining / valid ation Ġset Ġ( 75 %), Ġand Ġa Ġhold out Ġset Ġ( 25 %). ĠWe Ġperform Ġ10 - fold Ġcross - valid ation Ġon Ġthe Ġtraining / valid ation Ġset Ġto Ġidentify Ġthe Ġbest Ġclass ifier Ġmodel Ġand Ġparameters Ġ( using Ġa Ġgrid Ġsearch ). ĠBased Ġon Ġthe Ġresults Ġof Ġthis Ġevaluation , Ġwe Ġselect Ġa Ġ100 - est im ator Ġentropy - based Ġsplitting Ġrandom Ġforest Ġmodel Ġas Ġour Ġclass ifier . Ġtab : class ifiers Ġshows Ġthe Ġmean Ġaccuracy Ġand ĠF 1 - score Ġfor Ġeach Ġevaluated Ġclass ifier Ġduring Ġthe Ġ10 - fold Ġcross - valid ation . </s> Real - world Ġclass ifier Ġperformance . ĠTo Ġevaluate Ġreal - world Ġperformance Ġof Ġour Ġselected Ġclass ifier Ġ( i . e ., Ġperformance Ġin Ġthe Ġabsence Ġof Ġmodel Ġand Ġparameter Ġbias ), Ġwe Ġperform Ġclassification Ġof Ġthe Ġhold out Ġset . ĠOn Ġthis Ġset , Ġour Ġclass ifier Ġhad Ġan Ġaccuracy Ġand ĠF 1 - score Ġof Ġ89 . 6 % Ġand Ġ89 . 2 %, Ġrespectively . ĠThese Ġresults Ġshow Ġthat Ġin Ġaddition Ġto Ġsuperior Ġaccuracy Ġduring Ġtraining Ġand Ġvalidation , Ġour Ġchosen Ġclass ifier Ġis Ġalso Ġrobust Ġagainst Ġover - fitting . </s> Imp act Ġof Ġdataset Ġquality Ġand Ġsize . ĠTo Ġunderstand Ġhow Ġthe Ġperformance Ġof Ġour Ġchosen Ġclass ifier Ġmodel Ġand Ġparameters Ġare Ġimpacted Ġby : Ġ( 1 ) Ġthe Ġquality Ġand Ġconsistency Ġof Ġmanually Ġassigned Ġclasses Ġin Ġthe ĠCrowd Fl ower Ġdataset Ġand Ġ( 2 ) Ġthe Ġsize Ġof Ġthe Ġdataset , Ġwe Ġre - evaluate Ġthe Ġclass ifier Ġwhile Ġonly Ġconsidering Ġtweets Ġhaving Ġa Ġminimum Ġconfidence Ġscore Ġand Ġvarying Ġthe Ġsize Ġof Ġthe Ġhold out Ġset . ĠSpecifically , Ġour Ġexperiments Ġconsidered Ġconfidence Ġthresholds Ġof Ġ0 Ġ( all Ġtweets Ġconsidered ), Ġ. 35 Ġ( only Ġtweets Ġwhere Ġat Ġleast Ġ35 % Ġof Ġcontributors Ġagreed Ġon Ġa Ġclass Ġwere Ġconsidered ), Ġand Ġ. 70 Ġ( only Ġtweets Ġwhere Ġat Ġleast Ġ70 % Ġof Ġthe Ġcontributors Ġagreed Ġon Ġa Ġclass Ġwere Ġconsidered ) Ġand Ġvaried Ġthe Ġhold out Ġset Ġsizes Ġbetween Ġ5 % Ġand Ġ95 % Ġof Ġall Ġtweets Ġmeeting Ġthe Ġconfidence Ġthreshold Ġset Ġfor Ġthe Ġexperiment . </s> The Ġresults Ġillustrated Ġin Ġfig : class ifier - performance Ġshow Ġthe Ġperformance Ġof Ġthe Ġclass ifier Ġwhile Ġevaluating Ġthe Ġcorresponding Ġhold out Ġset . ĠWe Ġmake Ġseveral Ġconclusions Ġfrom Ġthese Ġresults : </s> Beyond Ġa Ġ( fair ly Ġlow ) Ġthreshold , Ġthe Ġsize Ġof Ġthe Ġtraining Ġand Ġvalidation Ġset Ġhas Ġlittle Ġimpact Ġon Ġclass ifier Ġperformance . ĠWe Ġsee Ġthat Ġthe Ġaccuracy , Ġprecision , Ġand Ġrecall Ġhave , Ġat Ġbest , Ġmarginal Ġimprovements Ġwith Ġhold out Ġset Ġsizes Ġsmaller Ġthan Ġ60 %. ĠThis Ġimplies Ġthat Ġthe ĠCrowd Fl ower Ġdataset Ġis Ġsufficient Ġfor Ġbuilding Ġan Ġoffensive Ġspeech Ġclass ifier . </s> Quality Ġof Ġmanual Ġlabeling Ġhas Ġa Ġsignificant Ġimpact Ġon Ġthe Ġaccuracy Ġand Ġprecision Ġof Ġthe Ġclass ifier . ĠUsing Ġonly Ġtweets Ġwhich Ġhad Ġat Ġleast Ġ70 % Ġof Ġcontributors Ġagreeing Ġon Ġa Ġclass Ġresulted Ġin Ġbetween Ġ5 - 7 % Ġhigher Ġaccuracy Ġand Ġup Ġto Ġ5 % Ġhigher Ġprecision . </s> Our Ġclass ifier Ġachieves Ġprecision Ġof Ġover Ġ95 % Ġand Ġrecall Ġof Ġover Ġ85 % Ġwhen Ġconsidering Ġonly Ġhigh Ġconfidence Ġsamples . ĠThis Ġimplies Ġthat Ġthe Ġclass ifier Ġis Ġmore Ġlikely Ġto Ġunderestimate Ġthe Ġpresence Ġof Ġoffensive Ġspeech ĠâĢĵ Ġi . e ., Ġour Ġresults Ġlikely Ġprovide Ġa Ġlower - bound Ġon Ġthe Ġquantity Ġof Ġobserved Ġoffensive Ġspeech . </s> Measure ments </s> In Ġthis Ġsection Ġwe Ġquantify Ġand Ġcharacterize Ġoff ens iveness Ġin Ġthe Ġpolitical Ġand Ġgeneral Ġcontexts Ġusing Ġour Ġoffensive Ġspeech Ġclass ifier Ġand Ġthe ĠReddit Ġcomments Ġdataset Ġwhich Ġconsiders Ġa Ġrandom Ġsample Ġof Ġcomments Ġmade Ġbetween ĠJanuary Ġ2015 Ġand ĠJanuary Ġ2017 . </s> Off ens iveness Ġover Ġtime . ĠWe Ġfind Ġthat Ġon Ġaverage Ġ8 . 4 % Ġof Ġall Ġpolitical Ġcomments Ġare Ġoffensive Ġcompared Ġto Ġ7 . 8 % Ġof Ġall Ġap olitical Ġcomments . Ġfig : offensive - speech - tim eline Ġillustrates Ġthe Ġfraction Ġof Ġoffensive Ġpolitical Ġand Ġap olitical Ġcomments Ġmade Ġduring Ġeach Ġweek Ġin Ġour Ġstudy . ĠWe Ġsee Ġthat Ġwhile Ġthe Ġfraction Ġof Ġap olitical Ġoffensive Ġcomments Ġhas Ġstayed Ġsteady , Ġthere Ġhas Ġbeen Ġan Ġincrease Ġin Ġthe Ġfraction Ġof Ġoffensive Ġpolitical Ġcomments Ġstarting Ġin ĠJuly Ġ2016 . ĠNotably , Ġthis Ġincrease Ġis Ġobserved Ġafter Ġthe Ġconclusion Ġof Ġthe ĠUS Ġpresidential Ġprimaries Ġand Ġduring Ġthe Ġperiod Ġof Ġthe ĠDemocratic Ġand ĠRepublican ĠNational ĠCon ventions Ġand Ġdoes Ġnot Ġreduce Ġeven Ġafter Ġthe Ġconclusion Ġof Ġthe ĠUS Ġpresidential Ġelections Ġheld Ġon ĠNovember Ġ8 . ĠParticipants Ġin Ġpolitical Ġsubreddits Ġwere Ġ2 . 6 % Ġmore Ġlikely Ġto Ġobserve Ġoffensive Ġcomments Ġprior Ġto ĠJuly Ġ2016 Ġbut Ġ14 . 9 % Ġmore Ġlikely Ġto Ġobserve Ġoffensive Ġcomments Ġfrom ĠJuly Ġ2016 Ġonwards . </s> Re actions Ġto Ġoffensive Ġcomments . ĠWe Ġuse Ġthe Ġcomment Ġscore , Ġroughly Ġthe Ġdifference Ġbetween Ġup - votes Ġand Ġdown - votes Ġreceived , Ġas Ġa Ġproxy Ġfor Ġunderstanding Ġhow Ġusers Ġreacted Ġto Ġoffensive Ġcomments . ĠWe Ġfind Ġthat Ġcomments Ġthat Ġwere Ġoffensive : Ġ( 1 ) Ġon Ġaverage , Ġhad Ġa Ġhigher Ġscore Ġthan Ġnon - offensive Ġcomments Ġ( average Ġscores : Ġ8 . 9 Ġvs . Ġ6 . 7 ) Ġand Ġ( 2 ) Ġwere Ġbetter Ġreceived Ġwhen Ġthey Ġwere Ġposted Ġin Ġthe Ġgeneral Ġcontext Ġthan Ġin Ġthe Ġpolitical Ġcontext Ġ( average Ġscores : Ġ8 . 6 Ġvs . Ġ9 . 0 ). ĠTo Ġunderstand Ġhow Ġpeoples Ġreactions Ġto Ġoffensive Ġcomments Ġevolved Ġover Ġtime , Ġfig : offensive - sc ores - tim eline Ġshows Ġthe Ġaverage Ġscores Ġreceived Ġby Ġoffensive Ġcomments Ġover Ġtime . ĠAgain , Ġwe Ġobserve Ġan Ġincreasing Ġtrend Ġin Ġaverage Ġscores Ġreceived Ġby Ġoffensive Ġand Ġpolitical Ġcomments Ġafter ĠJuly Ġ2016 . </s> Character istics Ġof Ġoffensive Ġauthors . ĠWe Ġnow Ġfocus Ġon Ġunderstanding Ġcharacteristics Ġof Ġauthors Ġof Ġoffensive Ġcomments . ĠSpecifically , Ġwe Ġare Ġinterested Ġin Ġidentifying Ġthe Ġuse Ġof Ġthrow away Ġand Ġtroll Ġaccounts . ĠFor Ġthe Ġpurposes Ġof Ġthis Ġstudy , Ġwe Ġcharacterize Ġthrow away Ġaccounts Ġas Ġthose Ġwith Ġless Ġthan Ġfive Ġtotal Ġcomments ĠâĢĵ Ġi . e ., Ġaccounts Ġthat Ġare Ġused Ġto Ġmake Ġa Ġsmall Ġnumber Ġof Ġcomments . ĠSimilarly , Ġwe Ġdefine Ġtroll Ġaccounts Ġas Ġthose Ġwith Ġover Ġ15 Ġcomments Ġof Ġwhich Ġover Ġ75 % Ġare Ġclassified Ġas Ġoffensive ĠâĢĵ Ġi . e ., Ġaccounts Ġthat Ġare Ġused Ġto Ġmake Ġa Ġlarger Ġnumber Ġof Ġcomments , Ġof Ġwhich Ġa Ġsignificant Ġmajority Ġare Ġoffensive . ĠWe Ġfind Ġthat Ġ93 . 7 % Ġof Ġthe Ġaccounts Ġwhich Ġhave Ġover Ġ75 % Ġof Ġtheir Ġcomments Ġtagged Ġas Ġoffensive Ġare Ġthrow aways Ġand Ġ1 . 3 % Ġare Ġtrolls . ĠComplete Ġresults Ġare Ġillustrated Ġin Ġfig : offensive - authors - c df . </s> Character istics Ġof Ġoffensive Ġcommunities . ĠWe Ġbreakdown Ġsubreddits Ġby Ġtheir Ġcategory Ġ( default , Ġpolitical , Ġand Ġother ) Ġand Ġidentify Ġthe Ġmost Ġand Ġleast Ġoffensive Ġcommunities Ġin Ġeach . Ġfig : sub reddit - c df Ġshows Ġthe Ġdistribution Ġof Ġthe Ġfraction Ġof Ġoffensive Ġcomments Ġin Ġeach Ġcategory Ġand Ġtab : sub reddit - break down Ġshows Ġthe Ġmost Ġand Ġleast Ġoffensive Ġsubreddits Ġin Ġthe Ġpolitical Ġand Ġdefault Ġcategories Ġ( we Ġexclude Ġthe ĠâĢ ľ other âĢ Ŀ Ġcategory Ġdue Ġto Ġthe Ġin appropri at eness Ġof Ġtheir Ġnames ). ĠWe Ġfind Ġthat Ġless Ġthan Ġ19 % Ġof Ġall Ġsubreddits Ġ( that Ġaccount Ġfor Ġover Ġ23 % Ġof Ġall Ġcomments ) Ġhave Ġover Ġ10 % Ġoffensive Ġcomments . ĠFurther , Ġseveral Ġdefault Ġand Ġpolitical Ġsubreddits Ġfall Ġin Ġthis Ġcategory , Ġincluding Ġr / the ĠIN LINE FORM 0 Ġdon ald ĠâĢĵ Ġthe Ġmost Ġoffensive Ġpolitical Ġsubreddit Ġand Ġthe Ġsubreddit Ġdedicated Ġto Ġthe ĠUS ĠPresident . </s> Flow Ġof Ġoffensive Ġauthors . ĠFinally , Ġwe Ġuncover Ġpatterns Ġin Ġthe Ġmovement Ġof Ġoffensive Ġauthors Ġbetween Ġcommunities . ĠIn Ġfig : offensive - flow Ġwe Ġshow Ġthe Ġcommunities Ġin Ġwhich Ġlarge Ġnumber Ġof Ġauthors Ġof Ġoffensive Ġcontent Ġon Ġthe Ġr / politics Ġsubreddit Ġhad Ġpreviously Ġmade Ġoffensive Ġcomments Ġ( we Ġrefer Ġto Ġthese Ġcommunities Ġas Ġsources ). ĠUn surprisingly , Ġthe Ġmost Ġpopular Ġsources Ġbelonged Ġto Ġthe Ġdefault Ġsubreddits Ġ( e . g ., Ġr / world news , Ġr / w tf , Ġr / videos , Ġr / ask reddit , Ġand Ġr / news ). ĠWe Ġfind Ġthat Ġseveral Ġother Ġpolitical Ġsubreddits Ġalso Ġserve Ġas Ġlarge Ġsources Ġof Ġoffensive Ġauthors . ĠIn Ġfact , Ġthe Ġsubreddits Ġdedicated Ġto Ġthe Ġthree Ġmost Ġpopular ĠUS Ġpresidential Ġcandidates ĠâĢĵ Ġr / the ĠIN LINE FORM 0 Ġdon ald , Ġr / sand ers for president , Ġand Ġr / hillary clinton Ġrank Ġin Ġthe Ġtop Ġthree . ĠFinally , Ġoutside Ġof Ġthe Ġdefault Ġand Ġpolitical Ġsubreddits , Ġwe Ġfind Ġthat Ġr / n fl , Ġr / cons piracy , Ġr / d ota 2 , Ġr / re action gif s , Ġr / black people twitter , Ġand Ġr / im going to hell forth is Ġwere Ġthe Ġlargest Ġsources Ġof Ġoffensive Ġpolitical Ġauthors . </s> Con clusions Ġand ĠFuture ĠWork </s> We Ġdevelop Ġand Ġvalidate Ġan Ġoffensive Ġspeech Ġclass ifier Ġto Ġquantify Ġthe Ġpresence Ġof Ġoffensive Ġonline Ġcomments Ġfrom ĠJanuary Ġ2015 Ġthrough ĠJanuary Ġ2017 . ĠWe Ġfind Ġthat Ġpolitical Ġdiscussions Ġon ĠReddit Ġbecame Ġincreasingly Ġless Ġcivil ĠâĢĵ Ġas Ġmeasured Ġby Ġthe Ġincidence Ġof Ġoffensive Ġcomments ĠâĢĵ Ġduring Ġthe Ġ2016 Ġgeneral Ġelection Ġcampaign . ĠIn Ġfact , Ġduring Ġthe Ġheight Ġof Ġthe Ġcampaign , Ġnearly Ġone Ġof Ġevery Ġ10 Ġcomments Ġposted Ġon Ġa Ġpolitical Ġsubreddit Ġwere Ġclassified Ġas Ġoffensive . ĠOffensive Ġcomments Ġalso Ġreceived Ġmore Ġpositive Ġfeedback Ġfrom Ġthe Ġcommunity , Ġeven Ġthough Ġmost Ġof Ġthe Ġaccounts Ġresponsible Ġfor Ġsuch Ġcomments Ġappear Ġto Ġbe Ġthrow away Ġaccounts . ĠWhile Ġoffensive Ġposts Ġwere Ġincreasingly Ġcommon Ġon Ġpolitical Ġsubreddits Ġas Ġthe Ġcampaign Ġwore Ġon , Ġthere Ġwas Ġno Ġsuch Ġincrease Ġin Ġnon - political Ġfor a . ĠThis Ġcontrast Ġprovides Ġadditional Ġevidence Ġthat Ġthe Ġincreasing Ġuse Ġof Ġoffensive Ġspeech Ġwas Ġdirectly Ġrelated Ġto Ġthe Ġramp ing Ġup Ġof Ġthe Ġgeneral Ġelection Ġcampaign Ġfor Ġpresident . </s> Even Ġthough Ġour Ġstudy Ġrelies Ġon Ġjust Ġa Ġsingle Ġsource Ġof Ġonline Ġpolitical Ġdiscussions ĠâĢĵ ĠReddit , Ġwe Ġbelieve Ġthat Ġour Ġfindings Ġgenerally Ġpresent Ġan Ġupper - bound Ġon Ġthe Ġincidence Ġof Ġoff ens iveness Ġin Ġonline Ġpolitical Ġdiscussions Ġfor Ġthe Ġfollowing Ġreasons : ĠFirst , ĠReddit Ġallows Ġthe Ġuse Ġof Ġpseud onymous Ġidentities Ġthat Ġenables Ġthe Ġonline Ġdis in hibition Ġeffect Ġ( un like Ġsocial - media Ġplatforms Ġsuch Ġas ĠFacebook ). ĠSecond , ĠReddit Ġenables Ġusers Ġto Ġengage Ġin Ġcomplex Ġdiscussions Ġthat Ġare Ġunrestricted Ġin Ġlength Ġ( un like ĠTwitter ). ĠFinally , ĠReddit Ġis Ġknown Ġfor Ġenabling Ġa Ġgeneral Ġculture Ġof Ġfree Ġspeech Ġand Ġdeleg ating Ġcontent Ġregulation Ġto Ġmoderators Ġof Ġindividual Ġsubreddits . ĠThis Ġprovides Ġusers Ġholding Ġfringe Ġviews Ġa Ġvariety Ġof Ġsubreddits Ġin Ġwhich Ġtheir Ġcontent Ġis Ġwelcome . </s> Our Ġfindings Ġprovide Ġa Ġunique Ġand Ġimportant Ġmapping Ġof Ġthe Ġincreasing Ġinc iv ility Ġof Ġonline Ġpolitical Ġdiscourse Ġduring Ġthe Ġ2016 Ġcampaign . ĠSuch Ġan Ġinvestigation Ġis Ġimportant Ġbecause Ġscholars Ġhave Ġoutlined Ġmany Ġconsequences Ġfor Ġinc iv ility Ġin Ġpolitical Ġdiscourse . ĠInc iv ility Ġtends Ġto ĠâĢ ľ turn Ġoff âĢ Ŀ Ġpolitical Ġmoderates , Ġleading Ġto Ġincreasing Ġpolarization Ġamong Ġthose Ġwho Ġare Ġactively Ġengaged Ġin Ġpolitics ĠB IB REF 4 Ġ. ĠMore Ġimportantly , Ġa Ġlack Ġof Ġciv ility Ġin Ġpolitical Ġdiscussions Ġgenerally Ġreduces Ġthe Ġdegree Ġto Ġwhich Ġpeople Ġview Ġopponents Ġas Ġholding Ġlegitimate Ġviewpoints . ĠThis Ġdynamic Ġmakes Ġit Ġdifficult Ġfor Ġpeople Ġto Ġfind Ġcommon Ġground Ġwith Ġthose Ġwho Ġdisagree Ġwith Ġthem ĠB IB REF 15 Ġand Ġit Ġmay Ġultimately Ġlead Ġcitizens Ġto Ġview Ġelectoral Ġvictories Ġby Ġopponents Ġas Ġlacking Ġlegitimacy ĠB IB REF 1 Ġ. ĠThus , Ġfrom Ġa Ġnormative Ġstandpoint , Ġthe Ġfact Ġthat Ġthe Ġ2016 Ġcampaign Ġsparked Ġa Ġmarked Ġincrease Ġin Ġthe Ġoff ens iveness Ġof Ġpolitical Ġcomments Ġposted Ġto ĠReddit Ġis Ġof Ġconcern Ġin Ġits Ġown Ġright ; Ġthat Ġthe Ġincidence Ġof Ġoffensive Ġpolitical Ġcomments Ġhas Ġremained Ġhigh Ġeven Ġthree Ġmonths Ġafter Ġthe Ġelection Ġis Ġall Ġthe Ġmore Ġtroubling . </s> In Ġfuture Ġwork , Ġwe Ġwill Ġextend Ġour Ġanalysis Ġof ĠReddit Ġback Ġto Ġ2007 Ġwith Ġthe Ġaim Ġof Ġform ulating Ġa Ġmore Ġcomplete Ġunderstanding Ġof Ġthe Ġdynamics Ġof Ġpolitical Ġinc iv ility . ĠFor Ġexample , Ġwe Ġseek Ġto Ġunderstand Ġwhether Ġthe Ġhigh Ġincidence Ġof Ġoffensive Ġspeech Ġwe Ġfind Ġin Ġ2016 Ġis Ġunique Ġto Ġthis Ġparticular Ġcampaign Ġor Ġif Ġprevious Ġpresidential Ġcampaigns Ġwitnessed Ġsimilar Ġspikes Ġin Ġinc iv ility . ĠWe Ġwill Ġalso Ġexamine Ġwhether Ġthere Ġis Ġa Ġmore Ġgeneral Ġlong - term Ġtrend Ġtoward Ġoffensive Ġonline Ġpolitical Ġspeech , Ġwhich Ġwould Ġbe Ġconsistent Ġwith Ġwhat Ġscholars Ġhave Ġfound Ġwhen Ġstudying Ġpolitical Ġelites ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ.