Amitabh Bachchan, Ariana Grande, Barack Obama, Bill Gates, Donald Trump,
Ellen DeGeneres, J K Rowling, Jimmy Fallon, Justin Bieber, Kevin Durant, Kim Kardashian, Lady Gaga, LeBron James,Narendra Modi, Oprah Winfrey
ABSTRACTIVE
Celebrities from varioius domains - Acting, Music, Politics, Business, TV, Author, Sports, Modeling. 
ABSTRACTIVE
FLOAT SELECTED: Table 1: Twitter celebrities in our dataset, with tweet counts before and after filtering (Foll. denotes followers in millions)
FLOAT SELECTED: Table 1: Twitter celebrities in our dataset, with tweet counts before and after filtering (Foll. denotes followers in millions)
1909.04002
Introduction </s> Social Ġmedia Ġplatforms , Ġparticularly Ġmicro blog ging Ġservices Ġsuch Ġas ĠTwitter , Ġhave Ġbecome Ġincreasingly Ġpopular ĠB IB REF 0 Ġas Ġa Ġmeans Ġto Ġexpress Ġthoughts Ġand Ġopinions . ĠTwitter Ġusers Ġemit Ġtweets Ġabout Ġa Ġwide Ġvariety Ġof Ġtopics , Ġwhich Ġvary Ġin Ġthe Ġextent Ġto Ġwhich Ġthey Ġreflect Ġa Ġuser 's Ġpersonality , Ġbrand Ġand Ġinterests . ĠThis Ġobservation Ġmotiv ates Ġthe Ġquestion Ġwe Ġconsider Ġhere , Ġof Ġhow Ġto Ġquantify Ġthe Ġdegree Ġto Ġwhich Ġtweets Ġare Ġcharacteristic Ġof Ġtheir Ġauthor ? </s> People Ġwho Ġare Ġfamiliar Ġwith Ġa Ġgiven Ġauthor Ġappear Ġto Ġbe Ġable Ġto Ġmake Ġsuch Ġjudgments Ġconfidently . ĠFor Ġexample , Ġconsider Ġthe Ġfollowing Ġpair Ġof Ġtweets Ġwritten Ġby ĠUS ĠPresident ĠDonald ĠTrump , Ġat Ġthe Ġextreme Ġsides Ġof Ġour Ġcharacterization Ġscores Ġ( 0 . 99 96 Ġvs . Ġ0 . 00 13 ) Ġfor Ġhim : </s> Tweet Ġ1 : ĠThank Ġyou Ġfor Ġjoining Ġus Ġat Ġthe ĠLincoln ĠMemorial Ġtonight - Ġa Ġvery Ġspecial Ġevening ! ĠTogether , Ġwe Ġare Ġgoing Ġto ĠMAKE ĠAMERICA ĠGREAT ĠAGA IN ! </s> Tweet Ġ2 : ĠâĢ ľ The Ġbend Ġin Ġthe Ġroad Ġis Ġnot Ġthe Ġend Ġof Ġthe Ġroad Ġunless Ġyou Ġrefuse Ġto Ġtake Ġthe Ġturn ." Ġ- ĠAnonymous </s> Although Ġboth Ġthese Ġtweets Ġare Ġfrom Ġthe Ġsame Ġaccount , Ġwe Ġassert Ġthat ĠTweet Ġ1 Ġsounds Ġmore Ġcharacteristic Ġof ĠDonald ĠTrump Ġthan ĠTweet Ġ2 . ĠWe Ġmight Ġalso Ġguess Ġthat Ġthe Ġfirst Ġis Ġmore Ġpopular Ġthan Ġsecond . ĠIndeed , ĠTweet Ġ1 Ġreceived Ġ155 , 000 Ġlikes Ġas Ġopposed Ġto Ġonly Ġ234 Ġfor ĠTweet Ġ2 . </s> Such Ġan Ġauthor Ġcharacterization Ġscore Ġhas Ġmany Ġpossible Ġapplications . ĠWith Ġthe Ġability Ġto Ġidentify Ġthe Ġmost / le ast Ġcharacteristic Ġtweets Ġfrom Ġa Ġperson , Ġwe Ġcan Ġgenerate Ġreduced Ġexcerpts Ġfor Ġhigh - volume ĠTwitter Ġprofiles . ĠSimilarly , Ġidentifying Ġthe Ġleast Ġcharacteristic Ġtweets Ġcan Ġhighlight Ġunusual Ġcontent Ġor Ġsuspicious Ġactivity . ĠA Ġrun Ġof Ġsufficiently Ġun represent ative Ġtweets Ġmight Ġbe Ġindicative Ġthat Ġa Ġhacker Ġhas Ġtaken Ġcontrol Ġof Ġa Ġuser 's Ġaccount . </s> But Ġmore Ġfundamentally , Ġour Ġwork Ġprovides Ġthe Ġnecessary Ġtool Ġto Ġstudy Ġthe Ġquestion Ġof Ġhow ĠâĢ ľ character istic - ness " Ġor Ġnovelty Ġare Ġrelated Ġto Ġtweet Ġpopularity . ĠDo Ġtweets Ġthat Ġare Ġmore Ġcharacteristic Ġof Ġthe Ġuser Ġget Ġmore Ġlikes , Ġreplies Ġand Ġret we ets ? ĠIs Ġsuch Ġa Ġrelationship Ġuniversal , Ġor Ġdoes Ġit Ġdepend Ġupon Ġthe Ġpersonality Ġor Ġdomain Ġof Ġthe Ġauthor ? ĠTwitter Ġusers Ġwith Ġa Ġlarge Ġfollower Ġbase Ġcan Ġemploy Ġour Ġmethods Ġto Ġunderstand Ġhow Ġcharacteristic Ġa Ġnew Ġpotential Ġtweet Ġsounds , Ġand Ġobtain Ġan Ġestimate Ġof Ġhow Ġpopular Ġit Ġis Ġlikely Ġto Ġbecome . </s> To Ġanswer Ġthese Ġquestions , Ġwe Ġformally Ġdefine Ġthe Ġproblem Ġof Ġauthor Ġrepresent at iveness Ġtesting , Ġand Ġmodel Ġthe Ġtask Ġas Ġa Ġbinary Ġclassification Ġproblem . ĠOur Ġprimary Ġcontributions Ġin Ġthis Ġpaper Ġinclude : </s> Five Ġapproaches Ġto Ġauthors hip Ġverification : ĠAs Ġa Ġproxy Ġfor Ġthe Ġquestion Ġof Ġrepresent at iveness Ġtesting Ġ( which Ġhas Ġno Ġconvincing Ġsource Ġof Ġground Ġtruth Ġwithout Ġextensive Ġhuman Ġannotation ), Ġwe Ġconsider Ġthe Ġtask Ġof Ġdistinguishing Ġtweets Ġwritten Ġby Ġa Ġgiven Ġauthor Ġfrom Ġothers Ġthey Ġdid Ġnot Ġwrite . ĠWe Ġcompare Ġfive Ġdistinct Ġcomputational Ġapproaches Ġto Ġsuch Ġbinary Ġtweet Ġclassification Ġ( user Ġvs . Ġnon - user ). ĠOur Ġbest Ġmodel Ġachieves Ġa Ġtest Ġaccuracy Ġof Ġ90 . 37 % Ġover Ġa Ġdataset Ġof Ġ15 ĠTwitter Ġcelebrities . ĠWe Ġuse Ġthe Ġbest Ġperforming Ġmodel Ġto Ġcompute Ġa Ġscore Ġ( the Ġprobability Ġof Ġauthors hip ), Ġwhich Ġquant ifies Ġhow Ġcharacteristic Ġof Ġthe Ġuser Ġa Ġgiven Ġtweet Ġis . </s> Human Ġevaluation Ġstudy : ĠTo Ġverify Ġthat Ġour Ġresults Ġare Ġin Ġagreement Ġwith Ġhuman Ġjudgment Ġof Ġhow Ġ` character istic ' Ġa Ġtweet Ġis , Ġwe Ġask Ġhuman Ġeval u ators Ġwhich Ġof Ġa Ġpair Ġof Ġtweets Ġsounds Ġmore Ġcharacteristic Ġof Ġthe Ġgiven Ġcelebrity . ĠThe Ġhuman Ġeval u ators Ġare Ġin Ġagreement Ġwith Ġour Ġmodel Ġ70 . 40 % Ġof Ġthe Ġtime , Ġsignificant Ġabove Ġthe Ġ$ 0 . 05 $ Ġlevel Ġfor Ġeach Ġof Ġour Ġ15 Ġcelebrities . </s> Cor relation Ġanalysis Ġfor Ġpopularity : ĠOur Ġcharacterization Ġscore Ġexhibits Ġstrikingly Ġhigh Ġabsolute Ġcorrelation Ġwith Ġpopularity Ġ( l ikes , Ġreplies Ġand Ġret we ets ), Ġdespite Ġthe Ġfact Ġthat Ġtweet Ġtext Ġis Ġthe Ġonly Ġfeature Ġused Ġto Ġtrain Ġthe Ġclass ifier Ġwhich Ġyields Ġthese Ġscores . </s> For Ġ13 Ġof Ġthe Ġ15 Ġcelebrities Ġin Ġour Ġdataset , Ġwe Ġobserve Ġa Ġstatistically Ġsignificant Ġcorrelation Ġbetween Ġcharacterization Ġscore Ġand Ġpopularity . ĠFigure ĠFIG REF 4 Ġshows Ġthe Ġrelation Ġbetween Ġtweet Ġscore Ġand Ġtweet Ġpopularity Ġfor ĠDonald ĠTrump Ġand ĠJustin ĠBieber Ġrespectively . ĠThe Ġfigure Ġshows Ġthat Ġthe Ġsign Ġof Ġthis Ġassociation Ġdiffers Ġfor Ġvarious Ġcelebrities , Ġreflecting Ġwhether Ġtheir Ġaudience Ġseeks Ġnovelty Ġor Ġreinforcement . </s> Iter ative Ġsampling Ġfor Ġclass Ġimbalance : ĠOur Ġtask Ġrequires Ġdistinguishing Ġa Ġuser 's Ġtweets Ġ( perhaps Ġ1 , 000 Ġpositive Ġtraining Ġexamples ) Ġfrom Ġthe Ġsea Ġof Ġall Ġother Ġuser 's Ġtweets Ġ( im ply ing Ġbillions Ġof Ġpossible Ġnegative Ġtraining Ġexamples ). ĠWe Ġpresent Ġan Ġiter ative Ġsampling Ġtechnique Ġto Ġexploit Ġthis Ġclass Ġimbalance , Ġwhich Ġimproves Ġthe Ġtest Ġaccuracy Ġfor Ġnegative Ġexamples Ġby Ġ2 . 62 %. </s> Problem ĠForm ulation </s> We Ġformally Ġdefine Ġthe Ġauthor Ġrepresent at iveness Ġproblem Ġas Ġfollows : </s> Input : ĠA ĠTwitter Ġauthor Ġ$ U $ Ġand Ġthe Ġcollection Ġof Ġtheir Ġtweets , Ġand Ġa Ġnew Ġtweet Ġ$ T $. </s> Problem : ĠComp ute Ġ$\ text rm Ġ{ score } ( T , ĠU ) $, Ġthe Ġprobability Ġthat Ġ$ T $ Ġwas Ġwritten Ġby Ġ$ U $. ĠThis Ġscore Ġquant ifies Ġhow Ġcharacteristic Ġof Ġwriter Ġ$ U $, Ġtweet Ġ$ T $ Ġis . </s> Problem ĠForm ulation Ġ: :: ĠMethod ology </s> In Ġorder Ġto Ġobtain Ġthis Ġrepresent at iveness Ġscore , Ġwe Ġmodel Ġour Ġtask Ġas Ġa Ġclassification Ġproblem , Ġwhere Ġwe Ġseek Ġto Ġdistinguish Ġtweets Ġfrom Ġ$ U $ Ġagainst Ġtweets Ġfrom Ġall Ġother Ġusers . </s> By Ġmodeling Ġthis Ġas Ġa Ġbinary Ġclassification Ġproblem , Ġit Ġbecomes Ġpossible Ġto Ġquantify Ġhow Ġcharacteristic Ġof Ġa Ġwriter Ġa Ġtweet Ġis , Ġas Ġa Ġprobability Ġimplied Ġby Ġits Ġdistance Ġfrom Ġthe Ġdecision Ġboundary . ĠThus , Ġwe Ġobtain Ġa Ġcharacterization Ġscore Ġbetween Ġ0 Ġand Ġ1 Ġfor Ġeach Ġtweet . </s> Chall enges : ĠIn Ġtraining Ġa Ġclass ifier Ġto Ġdistinguish Ġbetween Ġuser Ġand Ġnon - user Ġtweets , Ġwe Ġshould Ġideally Ġhave Ġan Ġequal Ġamount Ġof Ġexamples Ġof Ġboth Ġclasses . ĠUser Ġtweets Ġare Ġsimply Ġall Ġthe Ġtweets Ġfrom Ġthat Ġuser 's ĠTwitter Ġaccount , Ġand Ġmeasure Ġperhaps Ġin Ġthe Ġthousands . ĠIndeed , Ġthe Ġnumber Ġof Ġtweets Ġper Ġuser Ġper Ġday Ġis Ġlimited Ġto Ġ2400 Ġper Ġday Ġby Ġcurrent ĠTwitter Ġpolicy Ġ( https :// help . twitter . com / en / rules - and - p olic ies / twitter - limits ). ĠThe Ġnegative Ġexamples Ġconsist Ġof Ġall Ġtweets Ġwritten Ġby Ġother ĠTwitter Ġusers , Ġa Ġtotal Ġof Ġapproximately Ġ500 Ġmillion Ġper Ġday Ġ( https :// business . twitter . com ). ĠThus Ġthere Ġis Ġan Ġextreme Ġclass Ġimbalance Ġbetween Ġuser Ġand Ġnon - user Ġtweets . ĠMoreover , Ġthe Ġnature Ġof Ġlanguage Ġused Ġon ĠTwitter Ġdoes Ġnot Ġconform Ġto Ġformal Ġsynt actic Ġor Ġsemantic Ġrules . ĠThe Ġsentences Ġtend Ġto Ġbe Ġhighly Ġun struct ured , Ġand Ġthe Ġvocabulary Ġis Ġnot Ġrestricted Ġto Ġa Ġparticular Ġdictionary . </s> Problem ĠForm ulation Ġ: :: ĠData </s> For Ġthe Ġbinary Ġclassification Ġtask Ġdescribed Ġin ĠSection ĠSEC REF 6 , Ġwe Ġterm Ġtweets Ġfrom Ġ$ U $ Ġas Ġpositive Ġexamples , Ġand Ġtweets Ġfrom Ġother Ġusers Ġas Ġnegative Ġexamples . </s> Pos itive Ġexamples : ĠWe Ġtake Ġtweets Ġwritten Ġby Ġ15 Ġcelebrities Ġfrom Ġvarious Ġdomains , Ġfrom Ġ01 - Jan - 2008 Ġto Ġ01 - Dec - 2018 , Ġas Ġpositive Ġexamples . ĠProperties Ġof Ġthese ĠTwitter Ġcelebrities Ġare Ġprovided Ġin ĠTable ĠT AB REF 10 . </s> Neg ative Ġexamples : ĠWe Ġhave Ġcollected Ġ1 % Ġof Ġtweets Ġfrom ĠTwitter 's Ġdaily Ġfeed Ġusing Ġthe ĠTwitter ĠAPI Ġ( https :// develop er . twitter . com / en / docs . html ) Ġto Ġuse Ġas Ġnegative Ġexamples . </s> Pre processing Ġand ĠFil tering : ĠWe Ġhave Ġpre process ed Ġand Ġfiltered Ġthe Ġdata Ġto Ġremove Ġtweets Ġthat Ġare Ġun represent ative Ġor Ġtoo Ġshort Ġfor Ġanalysis . ĠAll Ġtext Ġhas Ġbeen Ġconverted Ġto Ġlower case , Ġand Ġstripped Ġof Ġpunct uation Ġmarks Ġand ĠURLs . ĠThis Ġis Ġbecause Ġour Ġapproaches Ġare Ġcentered Ġaround Ġword Ġusage . ĠHowever , Ġin Ġfuture Ġmodels , Ġpunct uation Ġmay Ġprove Ġeffective Ġas Ġa Ġfeature . ĠFurther , Ġwe Ġrestrict Ġanalysis Ġto ĠEnglish Ġlanguage Ġtweets Ġcontaining Ġno Ġattached Ġimages . ĠWe Ġselect Ġonly Ġtweets Ġwhich Ġare Ġmore Ġthan Ġ10 Ġwords Ġlong , Ġand Ġcontain Ġat Ġleast Ġ5 Ġlegitimate Ġ( d ictionary ) ĠEnglish Ġwords . ĠWe Ġdefine Ġan Ġun edited Ġtransfer Ġof Ġan Ġoriginal Ġtweet Ġas Ġa Ġretweet , Ġand Ġremove Ġthese Ġfrom Ġour Ġdataset . ĠSince Ġcomments Ġon Ġret we ets Ġare Ġwritten Ġby Ġthe Ġuser Ġthemselves , Ġwe Ġretain Ġthese Ġin Ġour Ġdataset . </s> We Ġnote Ġthat Ġcelebrity ĠTwitter Ġaccounts Ġcan Ġbe Ġhandled Ġby ĠPR Ġagencies , Ġin Ġaddition Ġto Ġthe Ġowner Ġthemselves . ĠBecause Ġour Ġaim Ġis Ġto Ġcharacterize ĠTwitter Ġprofiles Ġas Ġentities , Ġwe Ġhave Ġnot Ġattempted Ġto Ġdistinguish Ġbetween Ġuser - written Ġand Ġagency - written Ġtweets . ĠHowever , Ġthis Ġis Ġan Ġinteresting Ġdirection Ġfor Ġfuture Ġresearch . </s> We Ġuse Ġa Ġtrain - test Ġsplit Ġof Ġ70 - 30 % Ġon Ġthe Ġpositive Ġexamples , Ġand Ġgenerate Ġnegative Ġtraining Ġand Ġtest Ġsets Ġof Ġthe Ġsame Ġsizes Ġfor Ġeach Ġuser , Ġby Ġrandomly Ġsampling Ġfrom Ġthe Ġlarge Ġset Ġof Ġnegative Ġexamples . </s> Related Ġwork Ġ: :: ĠAuthor Ġidentification Ġand Ġverification </s> The Ġchallenge Ġof Ġauthor Ġidentification Ġhas Ġa Ġlong Ġhistory Ġin ĠN LP . ĠPAN Ġ2013 ĠB IB REF 1 Ġintroduced Ġthe Ġquestion : ĠâĢ ľ Given Ġa Ġset Ġof Ġdocuments Ġby Ġthe Ġsame Ġauthor , Ġis Ġan Ġadditional Ġ( out - of - set ) Ġdocument Ġalso Ġby Ġthat Ġauthor ? âĢ Ŀ ĠThe Ġcorpus Ġis Ġcomprised Ġof Ġtext Ġpieces Ġfrom Ġtextbooks , Ġnewspaper Ġarticles , Ġand Ġfiction . ĠSub missions Ġto ĠPAN Ġ2014 ĠB IB REF 2 Ġalso Ġmodel Ġauthors hip Ġverification Ġas Ġbinary Ġclassification , Ġby Ġusing Ġnon - author Ġdocuments Ġas Ġnegative Ġexamples . ĠThe Ġbest Ġsubmission ĠB IB REF 3 Ġin ĠPAN Ġ2013 Ġuses Ġthe ĠGeneral ĠIm post ors Ġ( GI ) Ġmethod , Ġwhich Ġis Ġa Ġmodification Ġof Ġthe ĠIm post ors ĠMethod ĠB IB REF 4 . ĠThe Ġbest Ġsubmission ĠB IB REF 5 Ġin ĠPAN Ġ2014 Ġpresents Ġa Ġmodification Ġof Ġthe ĠGI Ġmethod . ĠThese Ġmethods Ġare Ġbased Ġon Ġthe Ġimp ost ors Ġframework ĠB IB REF 6 . </s> comp ression ve en man Ġused Ġcompression Ġdistance Ġas Ġa Ġdocument Ġrepresentation , Ġfor Ġauthors hip Ġverification Ġin ĠPAN Ġ2013 . Ġword dist Ġpresent Ġa Ġglobal Ġfeature Ġextraction Ġapproach Ġand Ġachieve Ġstate - of - the - art Ġaccuracy Ġfor Ġthe ĠPAN Ġ2014 Ġcorpus . ĠThe Ġbest Ġsubmission ĠB IB REF 7 Ġin ĠPAN Ġ2015 ĠB IB REF 8 Ġuses Ġa Ġcharacter - level ĠR NN Ġmodel Ġfor Ġauthor Ġidentification , Ġin Ġwhich Ġeach Ġauthor Ġis Ġrepresented Ġas Ġa Ġsub - model , Ġand Ġthe Ġrecurrent Ġlayer Ġis Ġshared Ġby Ġall Ġsub - models . ĠThis Ġis Ġuseful Ġif Ġthe Ġnumber Ġof Ġauthors Ġis Ġfixed , Ġand Ġthe Ġproblem Ġis Ġmodeled Ġas Ġmulti - class Ġclassification . Ġdeep learning auth id Ġalso Ġapproach Ġmulti - class Ġauthor Ġidentification , Ġusing Ġdeep Ġlearning Ġfor Ġfeature Ġextraction , Ġand Ġforensic Ġusing Ġhierarchical Ġclust ering . </s> int r ins ica uthor ver ification Ġpropose Ġan Ġintrinsic Ġprofile - based Ġverification Ġmethod Ġthat Ġuses Ġlatent Ġsemantic Ġindex ing Ġ( LS I ), Ġwhich Ġis Ġeffective Ġfor Ġlonger Ġtexts . Ġauth one class Ġand Ġlimited data Ġexplore Ġmethods Ġfor Ġauthors hip Ġverification Ġfor Ġlarger Ġdocuments Ġsuch Ġas Ġessays Ġand Ġnovels . Ġemail auth id Ġand Ġemail 2 Ġexplore Ġauthor Ġidentification Ġfor Ġemails , Ġand Ġtask guided Ġfor Ġscientific Ġpapers . Ġaz arb ony ad 2015 time Ġmake Ġuse Ġof Ġtemporal Ġchanges Ġin Ġword Ġusage Ġto Ġidentify Ġauthors Ġof Ġtweets Ġand Ġemails . Ġun ig rams and big rams , Ġfeature eval , Ġand Ġun struct ured Ġevaluate Ġthe Ġutility Ġof Ġvarious Ġfeatures Ġfor Ġthis Ġtask . Ġauth id using text sam pling Ġproposes Ġtext Ġsampling Ġto Ġaddress Ġthe Ġlack Ġof Ġtext Ġsamples Ġof Ġundis puted Ġauthors hip , Ġto Ġproduce Ġa Ġdesirable Ġdistribution Ġover Ġclasses . </s> ko ppel 2009 com put ational Ġcompare Ġmethods Ġfor Ġvariants Ġof Ġthe Ġauthors hip Ġattribution Ġproblem . Ġb h arg ava 2013 st yl ometric Ġapply Ġstyl ometric Ġanalysis Ġto Ġtweets Ġto Ġdetermine Ġthe Ġauthor . Ġl opez 2015 disc rim in ative Ġpropose Ġa Ġdocument Ġrepresentation Ġcapturing Ġdiscrim in ative Ġand Ġsub profile - specific Ġinformation Ġof Ġterms . Ġro cha 2016 auth orship Ġreview Ġmethods Ġfor Ġauthors hip Ġattribution Ġfor Ġsocial Ġmedia Ġfore nsics . Ġp eng 2016 bit Ġuse Ġbit - level Ġn - gram s Ġfor Ġdetermining Ġauthors hip Ġfor Ġonline Ġnews . Ġp eng 2016 ast rot ur fing Ġapply Ġthis Ġmethod Ġto Ġdetect Ġast rot ur fing Ġon Ġsocial Ġmedia . Ġthe oph ilo 2019 need le Ġemploy Ġdeep Ġlearning Ġspecifically Ġfor Ġauthors hip Ġattribution Ġof Ġshort Ġmessages . </s> Related Ġwork Ġ: :: ĠPredict ing Ġtweet Ġpopularity </s> su h 2010 want Ġlever ages Ġfeatures Ġsuch Ġas ĠURL , Ġnumber Ġof Ġhasht ags , Ġnumber Ġof Ġfollowers Ġand Ġfollow ees Ġetc . Ġin Ġa Ġgeneralized Ġlinear Ġmodel , Ġto Ġpredict Ġthe Ġnumber Ġof Ġret we ets . Ġn ave ed 2011 bad Ġextend Ġthis Ġapproach Ġto Ġperform Ġcontent - based Ġretweet Ġprediction Ġusing Ġseveral Ġfeatures Ġincluding Ġsentiments , Ġemot icons , Ġpunct uations Ġetc . Ġband ari 2012 p ulse Ġapply Ġthe Ġsame Ġapproach Ġfor Ġregression Ġas Ġwell Ġas Ġclassification , Ġto Ġpredict Ġthe Ġnumber Ġof Ġret we ets Ġspecifically Ġfor Ġnews Ġarticles . Ġz aman 2014 bay esian Ġpresent Ġa ĠBay esian Ġmodel Ġfor Ġretweet Ġprediction Ġusing Ġearly Ġretweet Ġtimes , Ġret we ets Ġof Ġother Ġtweets , Ġand Ġthe Ġuser 's Ġfollower Ġgraph . Ġtan 2014 effect Ġanalyze Ġwhether Ġdifferent Ġwording Ġof Ġa Ġtweet Ġby Ġthe Ġsame Ġauthor Ġaffects Ġits Ġpopularity . ĠSE ISM IC ĠB IB REF 9 Ġand ĠP SE ISM IC ĠB IB REF 10 Ġare Ġstatistical Ġmethods Ġto Ġpredict Ġthe Ġfinal Ġnumber Ġof Ġret we ets . Ġz hang 2018 p redict ing Ġapproach Ġretweet Ġprediction Ġas Ġa Ġmulti - class Ġclassification Ġproblem , Ġand Ġpresent Ġa Ġfeature - weight ed Ġmodel , Ġwhere Ġweights Ġare Ġcomputed Ġusing Ġinformation Ġgain . </s> Related Ġwork Ġ: :: ĠTraining Ġwith Ġim balanced Ġdatasets </s> Various Ġmethods Ġto Ġhandle Ġim balanced Ġdatasets Ġhave Ġbeen Ġdescribed Ġby Ġk ots iant is 2006 hand ling . ĠThese Ġinclude Ġunders am pling ĠB IB REF 11 , Ġovers am pling , Ġand Ġfeature Ġselection ĠB IB REF 12 Ġat Ġthe Ġdata Ġlevel . ĠHowever , Ġdue Ġto Ġrandom Ġunders am pling , Ġpotentially Ġuseful Ġsamples Ġcan Ġbe Ġdiscarded , Ġwhile Ġrandom Ġovers am pling Ġposes Ġthe Ġrisk Ġof Ġover fitting . ĠThis Ġproblem Ġcan Ġbe Ġhandled Ġat Ġthe Ġalgorith mic Ġlevel Ġas Ġwell : Ġthe Ġthreshold Ġmethod ĠB IB REF 13 Ġproduces Ġseveral Ġclass ifiers Ġby Ġvarying Ġthe Ġthreshold Ġof Ġthe Ġclass ifier Ġscore . ĠOne - class Ġclassification Ġcan Ġbe Ġperformed Ġusing Ġa Ġdivide - and - con quer Ġapproach , Ġto Ġiter atively Ġbuild Ġrules Ġto Ġcover Ġnew Ġtraining Ġinstances ĠB IB REF 14 . ĠCost - sensitive Ġlearning ĠB IB REF 15 Ġuses Ġunequal Ġmis class ification Ġcosts Ġto Ġaddress Ġthe Ġclass Ġimbalance Ġproblem . </s> App ro aches Ġto Ġauthors hip Ġverification </s> As Ġdescribed Ġin ĠSection ĠSEC REF 6 , Ġwe Ġbuild Ġclassification Ġmodels Ġto Ġdistinguish Ġbetween Ġuser Ġand Ġnon - user Ġtweets . ĠWe Ġhave Ġexplored Ġfive Ġdistinct Ġapproaches Ġto Ġbuild Ġsuch Ġmodels . </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠApproach Ġ1 : ĠComp ression </s> This Ġapproach Ġis Ġinspired Ġfrom ĠKol m og or ov Ġcomplexity ĠB IB REF 16 , Ġwhich Ġargues Ġthat Ġthe Ġcompress ibility Ġof Ġa Ġtext Ġreflects Ġthe Ġquality Ġof Ġthe Ġunderlying Ġmodel . ĠWe Ġuse Ġthe ĠLem pel - Z iv - W el ch Ġ( L Z W ) Ġcompression Ġalgorithm ĠB IB REF 17 Ġto Ġapproximate ĠKol m og or ov Ġcomplexity Ġby Ġdynamically Ġbuilding Ġa Ġdictionary Ġto Ġencode Ġword Ġpatterns Ġfrom Ġthe Ġtraining Ġcorpus . ĠThe Ġlongest Ġoccurring Ġpattern Ġmatch Ġpresent Ġin Ġthe Ġdictionary Ġis Ġused Ġto Ġencode Ġthe Ġtext . </s> We Ġhypothes ize Ġthat Ġthe Ġlength Ġof Ġa Ġtweet Ġ$ T $ Ġfrom Ġuser Ġ$ U $, Ġcompressed Ġusing Ġa Ġdictionary Ġbuilt Ġfrom Ġpositive Ġexamples , Ġwill Ġbe Ġless Ġthan Ġthe Ġlength Ġof Ġthe Ġsame Ġtweet Ġcompressed Ġusing Ġa Ġdictionary Ġbuilt Ġfrom Ġnegative Ġexamples . </s> We Ġuse Ġthe Ġfollowing Ġsetup Ġto Ġclassify Ġtest Ġtweets Ġfor Ġeach ĠTwitter Ġuser Ġin Ġour Ġdataset : </s> Build Ġan Ġencoding Ġdictionary Ġusing Ġpositive Ġexamples Ġ($ \ text rm Ġ{ train } _{ \ text rm Ġ{ pos }} $ ), Ġand Ġan Ġencoding Ġdictionary Ġusing Ġnegative Ġexamples Ġ($ \ text rm Ġ{ train } _{ \ text rm Ġ{ neg }} $ ). </s> En code Ġthe Ġnew Ġtweet Ġ$ T $ Ġusing Ġboth Ġthese Ġdiction aries , Ġto Ġobtain Ġ$ T _{ \ text rm Ġ{ pos }} Ġ= Ġ\ text rm Ġ{ en code } _{ \ text rm Ġ{ pos }} ( T ) $ Ġand Ġ$ T _{ \ text rm Ġ{ neg }} Ġ= Ġ\ text rm Ġ{ en code } _{ \ text rm Ġ{ neg }} ( T ) $ Ġrespectively . </s> If Ġthe Ġlength Ġof Ġ$ T _{ \ text rm Ġ{ pos }} $ Ġis Ġless Ġthan Ġthat Ġof Ġ$ T _{ \ text rm Ġ{ neg }} $, Ġclassify Ġ$ T $ Ġas Ġpositive ; Ġelse , Ġclassify Ġit Ġas Ġnegative . </s> This Ġgives Ġus Ġthe Ġclass Ġlabel Ġfor Ġeach Ġnew Ġtweet Ġ$ T $. ĠIn Ġaddition , Ġwe Ġcompute Ġthe Ġcharacterization Ġscore Ġof Ġtweet Ġ$ T $ Ġwith Ġrespect Ġto Ġuser Ġ$ U $, Ġas Ġdescribed Ġin ĠEqu ation ĠDIS PLAY _ FORM 18 . </s> Thus Ġthe Ġshorter Ġthe Ġlength Ġof Ġthe Ġencoded Ġtweet , Ġthe Ġmore Ġcharacteristic Ġof Ġthe Ġuser Ġ$ T $ Ġis . </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠApproach Ġ2 : ĠTopic Ġmodeling </s> We Ġhypothes ize Ġthat Ġeach Ġuser Ġwrites Ġabout Ġtopics Ġwith Ġa Ġparticular Ġprobability Ġdistribution , Ġand Ġthat Ġeach Ġtweet Ġreflects Ġthe Ġprobability Ġdistribution Ġover Ġthese Ġtopics . ĠWe Ġtrain Ġa Ġtopic Ġmodel Ġusing ĠLat ent ĠDir ich let ĠAll ocation Ġ( L DA ) ĠB IB REF 18 Ġon Ġa Ġlarge Ġcorpus Ġof Ġtweets , Ġand Ġuse Ġthis Ġtopic Ġmodel Ġto Ġcompute Ġtopic Ġdistributions Ġfor Ġindividual Ġtweets . ĠWe Ġthen Ġuse Ġthese Ġvalues Ġas Ġfeatures . ĠWe Ġexperiment Ġwith Ġtwo Ġtypes Ġof Ġclass ifiers : ĠLog istic ĠReg ression Ġ( LR ), Ġand ĠMulti ĠLinear ĠPer cept ron Ġ( ML P ) Ġof Ġsize Ġ$( 5 , Ġ5 , Ġ5 ) $. ĠWe Ġrepresent Ġeach Ġtweet Ġas Ġa Ġdistribution Ġover Ġ$ n = 500 $ Ġtopics . </s> The Ġcharacterization Ġscore Ġof Ġa Ġtweet Ġ$ T $ Ġis Ġgiven Ġby Ġthe Ġclass ifier 's Ġconfidence Ġthat Ġ$ T $ Ġbelongs Ġto Ġthe Ġpositive Ġclass . </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠApproach Ġ3 : Ġn - gram Ġprobability </s> We Ġhypothes ize Ġthat Ġa ĠTwitter Ġuser Ġcan Ġbe Ġcharacterized Ġby Ġusage Ġof Ġwords Ġand Ġtheir Ġfrequencies Ġin Ġtweets , Ġand Ġmodel Ġthis Ġusing Ġn - gram Ġfrequencies . </s> We Ġuse Ġthe Ġfollowing Ġsetup Ġto Ġclassify Ġtest Ġtweets Ġfor Ġeach ĠTwitter Ġuser Ġin Ġour Ġdataset : </s> Build Ġa Ġfrequency Ġdictionary Ġof Ġall Ġn - gram s Ġin Ġpositive Ġexamples Ġ($ \ text rm Ġ{ train } _{ \ text rm Ġ{ pos }} $ ), Ġand Ġa Ġfrequency Ġdictionary Ġof Ġall Ġn - gram s Ġin Ġnegative Ġexamples Ġ($ \ text rm Ġ{ train } _{ \ text rm Ġ{ neg }} $ ). </s> Comp ute Ġthe Ġaverage Ġprobability Ġof Ġall Ġn - gram Ġsequences Ġin Ġthe Ġnew Ġtweet Ġ$ T $ Ġusing Ġboth Ġthese Ġdiction aries , Ġto Ġobtain Ġ$\ text rm Ġ{ pro b } _{ \ text rm Ġ{ pos }} ( T ) $ Ġand Ġ$\ text rm Ġ{ pro b } _{ \ text rm Ġ{ neg }} ( T ) $ Ġrespectively . ĠHere , Ġwe Ġuse Ġadd - one Ġsmoot hing Ġand Ġconditional Ġback off Ġto Ġcompute Ġthese Ġprobability Ġvalues . </s> If Ġ$\ text rm Ġ{ pro b } _{ \ text rm Ġ{ pos }} ( T ) $ Ġis Ġgreater Ġthan Ġ$\ text rm Ġ{ pro b } _{ \ text rm Ġ{ neg }} ( T ) $, Ġclassify Ġ$ T $ Ġas Ġpositive ; Ġelse , Ġclassify Ġit Ġas Ġnegative . </s> The Ġcharacterization Ġscore Ġof Ġtweet Ġ$ T $ Ġis Ġgiven Ġby Ġthe Ġaverage Ġn - gram Ġprobability Ġcomputed Ġusing Ġthe Ġfrequency Ġdictionary Ġof Ġ$\ text rm Ġ{ train } _{ \ text rm Ġ{ pos }} $. ĠWe Ġexperiment Ġwith Ġ$ n Ġ= Ġ1 $ Ġ( un ig rams ) Ġand Ġ$ n Ġ= Ġ2 $ Ġ( big rams ). </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠApproach Ġ4 : ĠDocument Ġembed d ings </s> We Ġhypothes ize Ġthat Ġif Ġwe Ġobtain Ġlatent Ġrepresentations Ġof Ġtweets Ġas Ġdocuments , Ġtweets Ġfrom Ġthe Ġsame Ġauthor Ġwill Ġcluster Ġtogether , Ġand Ġwill Ġbe Ġdifferent iable Ġfrom Ġtweets Ġfrom Ġothers . ĠTo Ġthat Ġend , Ġwe Ġuse Ġthe Ġfollowing Ġsetup : </s> We Ġobtain Ġrepresentations Ġof Ġtweets Ġas Ġdocument Ġembed d ings . ĠWe Ġexperiment Ġwith Ġtwo Ġtypes Ġof Ġdocument Ġembed d ings : ĠFast Text ĠB IB REF 19 Ġ( embed ding Ġsize Ġ= Ġ100 ) Ġand ĠB ERT - Base , Ġunc ased ĠB IB REF 20 Ġ( embed ding Ġsize Ġ= Ġ768 ). </s> We Ġthen Ġuse Ġthese Ġembed d ings Ġas Ġfeatures Ġto Ġtrain Ġa Ġclassification Ġmodel . ĠWe Ġexperiment Ġwith Ġtwo Ġtypes Ġof Ġclass ifiers : ĠLog istic ĠReg ression Ġ( LR ) Ġand ĠMulti ĠLinear ĠPer cept ron Ġ( ML P ) Ġof Ġsize Ġ$( 5 , Ġ5 , Ġ5 ) $. </s> The Ġcharacterization Ġscore Ġof Ġtweet Ġ$ T $ Ġis Ġgiven Ġby Ġthe Ġclass ifier 's Ġconfidence Ġthat Ġ$ T $ Ġbelongs Ġto Ġthe Ġpositive Ġclass . </s> Iter ative Ġsampling : ĠAs Ġdescribed Ġin ĠSection ĠSEC REF 6 , Ġthere Ġexists Ġan Ġextreme Ġclass Ġimbalance Ġfor Ġthis Ġbinary Ġclassification Ġtask , Ġin Ġthat Ġthe Ġnumber Ġof Ġnegative Ġexamples Ġis Ġfar Ġmore Ġthan Ġthe Ġnumber Ġof Ġpositive Ġexamples . ĠHere , Ġwe Ġexplore Ġan Ġiter ative Ġsampling Ġtechnique Ġto Ġaddress Ġthis Ġproblem . ĠWe Ġtrain Ġour Ġclass ifier Ġfor Ġmultiple Ġiterations , Ġcoupling Ġthe Ġsame Ġ$\ text rm Ġ{ train } _{ \ text rm Ġ{ pos }} $ Ġwith Ġa Ġnew Ġrandomly Ġsampled Ġ$\ text rm Ġ{ train } _{ \ text rm Ġ{ neg }} $ Ġset Ġin Ġeach Ġiteration . </s> We Ġconduct Ġthis Ġexperiment Ġfor Ġall Ġusers Ġwith Ġthe Ġbest Ġperforming Ġmodel Ġfor Ġthis Ġapproach , Ġi . e . Ġwe Ġuse ĠB ERT Ġembed d ings Ġas Ġfeatures , Ġand ĠML P Ġfor Ġclassification . ĠWe Ġtrain Ġthis Ġclass ifier Ġfor Ġ40 Ġiterations , Ġand Ġcompare Ġthe Ġmodel 's Ġperformance Ġwhen Ġwe Ġuse Ġthe Ġsame Ġset Ġof Ġnegative Ġexamples Ġvs . Ġwhen Ġwe Ġrandomly Ġsample Ġnew Ġnegative Ġexamples Ġin Ġeach Ġiteration . </s> Figure ĠFIG REF 28 Ġshows Ġthe Ġmean Ġtrain Ġand Ġtest Ġaccuracy Ġfor Ġall Ġusers Ġover Ġ40 Ġiterations . ĠAs Ġexpected , Ġthe Ġtraining Ġaccuracy Ġis Ġhigher Ġif Ġwe Ġdo Ġnot Ġsample , Ġas Ġthe Ġmodel Ġgets Ġtrained Ġon Ġthe Ġsame Ġdata Ġrepeatedly Ġin Ġeach Ġiteration . ĠHowever , Ġif Ġwe Ġperform Ġrandom Ġsampling , Ġthe Ġmodel Ġis Ġexposed Ġto Ġa Ġlarger Ġnumber Ġof Ġnegative Ġexamples , Ġwhich Ġresults Ġin Ġa Ġhigher Ġtest Ġaccuracy Ġ(+ Ġ1 . 08 %), Ġspecifically Ġfor Ġnegative Ġtest Ġexamples Ġ(+ Ġ2 . 62 %). </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠApproach Ġ5 : ĠToken Ġembed d ings Ġand Ġsequential Ġmodeling </s> In Ġthis Ġapproach , Ġwe Ġtoken ize Ġeach Ġtweet , Ġand Ġobtain Ġembed d ings Ġfor Ġeach Ġtoken . ĠWe Ġthen Ġsequ entially Ġgive Ġthese Ġembed d ings Ġas Ġinput Ġto Ġa Ġclass ifier . </s> We Ġuse Ġa Ġpret rained Ġmodel Ġ( BER T - Base , ĠUnc ased : Ġ12 - layer , Ġ768 - hidden , Ġ12 - heads , Ġ110 M Ġparameters ) Ġto Ġgenerate Ġtoken Ġembed d ings Ġof Ġsize Ġ768 , Ġand Ġpass Ġthese Ġto Ġa ĠLong ĠShort ĠTerm ĠMemory Ġ( L ST M ) ĠB IB REF 21 Ġclass ifier . ĠWe Ġuse Ġan ĠL ST M Ġlayer Ġwith Ġ768 Ġunits Ġwith Ġdrop out Ġand Ġrecurrent Ġdrop out Ġratio Ġ0 . 2 , Ġfollowed Ġby Ġa Ġdense Ġlayer Ġwith Ġs igm oid Ġactivation . ĠWe Ġtrain Ġthis Ġmodel Ġusing Ġthe ĠAdam Ġoptim izer ĠB IB REF 22 Ġand Ġbinary Ġcross - ent ropy Ġloss , Ġwith Ġaccuracy Ġas Ġthe Ġtraining Ġmetric . </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠResults Ġand ĠComparison </s> Table ĠT AB REF 24 Ġpresents Ġthe Ġuser - wise Ġtest Ġaccuracy Ġof Ġthe Ġfive Ġapproaches Ġunder Ġthe Ġspecified Ġconfigurations . ĠNote Ġthat Ġthe Ġtest Ġset Ġcontains Ġan Ġequal Ġnumber Ġof Ġpositive Ġand Ġnegative Ġexamples Ġfor Ġeach Ġauthor . </s> Other Ġbas elines Ġthat Ġwe Ġattempted Ġto Ġcompare Ġagainst Ġinclude Ġthe Ġbest Ġsubmissions Ġto Ġthe ĠPAN Ġ2013 Ġand Ġ2014 Ġauthor Ġverification Ġchallenge : Ġpan 2013 winner Ġand Ġslightly modified im post or , Ġwhich Ġare Ġvariants Ġof Ġthe ĠIm post ors ĠMethod . ĠThis Ġchallenge Ġemployed Ġsignificantly Ġlonger Ġdocuments Ġ( with Ġan Ġaverage Ġof Ġ10 39 , Ġ8 45 , Ġand Ġ4 393 Ġwords Ġper Ġdocument Ġfor Ġarticles , Ġessays Ġand Ġnovels Ġrespectively , Ġas Ġopposed Ġto Ġan Ġaverage Ġof Ġ19 Ġwords Ġper Ġtweet ) Ġand Ġsignificantly Ġfewer Ġdocuments Ġper Ġauthor Ġ( an Ġaverage Ġof Ġ3 . 2 , Ġ2 . 6 Ġand Ġ1 Ġdocument / s Ġper Ġauthor , Ġas Ġopposed Ġto Ġan Ġaverage Ġof Ġ67 38 Ġtweets Ġper Ġuser ). ĠOur Ġexperiments Ġwith Ġthe Ġauthors hip Ġverification Ġclass ifier ĠB IB REF 23 Ġshowed Ġthat Ġthe ĠIm post ors ĠMethod Ġis Ġprohib itive ly Ġexpensive Ġon Ġlarger Ġcorpor a , Ġand Ġalso Ġperformed Ġtoo Ġinaccur ately Ġon Ġshort Ġtexts Ġto Ġprovide Ġa Ġmeaningful Ġbaseline . </s> For Ġ13 Ġof Ġthe Ġ15 Ġusers Ġin Ġour Ġdataset , ĠApproach ĠSEC REF 29 Ġ( token Ġembed d ings Ġfollowed Ġby Ġsequential Ġmodeling ) Ġhas Ġthe Ġhighest Ġaccuracy . ĠThis Ġmodel Ġcorrectly Ġidentifies Ġthe Ġauthor Ġof Ġ90 . 37 % Ġof Ġall Ġtweets Ġin Ġour Ġstudy , Ġand Ġwill Ġbe Ġused Ġto Ġdefine Ġthe Ġcharacterization Ġscore Ġfor Ġour Ġsubsequent Ġstudies . </s> User Ġstudy </s> To Ġverify Ġwhether Ġhuman Ġeval u ators Ġare Ġin Ġagreement Ġwith Ġour Ġcharacterization Ġmodel , Ġwe Ġconducted Ġa Ġuser Ġstudy Ġusing ĠM Tur k ĠB IB REF 24 . </s> User Ġstudy Ġ: :: ĠSetup </s> For Ġeach Ġuser Ġin Ġour Ġdataset , Ġwe Ġbuild Ġa Ġset Ġof Ġ20 Ġtweet Ġpairs , Ġwith Ġone Ġtweet Ġeach Ġfrom Ġthe Ġ50 Ġtop - scoring Ġand Ġbottom - scoring Ġtweets Ġwritten Ġby Ġthe Ġuser . ĠWe Ġask Ġthe Ġhuman Ġeval u ator Ġto Ġchoose Ġwhich Ġtweet Ġsounds Ġmore Ġcharacteristic Ġof Ġthe Ġuser . ĠTo Ġvalidate Ġthat Ġthe ĠM Tur k Ġworker Ġknows Ġenough Ġabout Ġthe ĠTwitter Ġuser Ġto Ġpick Ġa Ġcharacteristic Ġtweet , Ġwe Ġuse Ġa Ġqualification Ġtest Ġcontaining Ġa Ġbasic Ġset Ġof Ġquestions Ġabout Ġthe ĠTwitter Ġuser . ĠWe Ġwere Ġunable Ġto Ġfind Ġequal Ġnumbers Ġof ĠTurk ers Ġfamiliar Ġwith Ġeach Ġsubject , Ġso Ġour Ġnumber Ġof Ġeval u ators Ġ$ n $ Ġdiffers Ġaccording Ġto Ġauthor . </s> User Ġstudy Ġ: :: ĠResults </s> Table ĠT AB REF 33 Ġdescribes Ġthe Ġresults Ġobtained Ġin Ġthe Ġuser Ġstudy : Ġthe Ġmean Ġand Ġstandard Ġdeviation Ġof Ġpercentage Ġof Ġanswers Ġin Ġagreement Ġwith Ġour Ġmodel , Ġthe Ġp - value , Ġand Ġthe Ġnumber Ġof ĠM Tur k Ġworkers Ġwho Ġcompleted Ġeach Ġtask . ĠWe Ġfind Ġthat Ġthe Ġaverage Ġagreement Ġof Ġhuman Ġeval u ators Ġwith Ġour Ġmodel Ġis Ġ70 . 40 % Ġover Ġall Ġ15 Ġusers Ġin Ġour Ġdataset . </s> For Ġeach Ġof Ġthe Ġ15 Ġcelebrities , Ġthe Ġhuman Ġeval u ators Ġagree Ġwith Ġour Ġmodel Ġabove Ġa Ġsignificance Ġlevel Ġof Ġ0 . 05 , Ġand Ġin Ġ13 Ġof Ġ15 Ġcases Ġabove Ġa Ġlevel Ġof Ġ$ 10 ^{ - 5 } $. ĠThis Ġmakes Ġclear Ġour Ġscores Ġare Ġmeasuring Ġwhat Ġwe Ġintend Ġto Ġbe Ġmeasuring . </s> M apping Ġwith Ġpopularity Ġ: :: ĠCor relation </s> We Ġnow Ġexplore Ġthe Ġrelationship Ġbetween Ġcharacterization Ġscore Ġand Ġtweet Ġpopularity Ġfor Ġeach Ġof Ġthe Ġusers Ġin Ġour Ġdataset . ĠTo Ġanalyze Ġthis Ġrelationship , Ġwe Ġperform Ġthe Ġfollowing Ġprocedure Ġfor Ġeach Ġauthor Ġ$ U $ : </s> Sort Ġall Ġtweets Ġwritten Ġby Ġ$ U $ Ġin Ġascending Ġorder Ġof Ġcharacterization Ġscore . </s> B ucket Ġthe Ġsorted Ġtweets Ġby Ġpercentile Ġscore Ġ( 1 Ġto Ġ100 ). </s> For Ġeach Ġbucket , Ġcalculate Ġthe Ġmean Ġnumber Ġof Ġlikes , Ġreplies , Ġand Ġret we ets . </s> Comp ute Ġthe Ġcorrelation Ġof Ġthis Ġmean Ġand Ġthe Ġpercentile Ġscore . </s> The ĠPearson Ġcorrelation Ġcoefficients Ġ( r - values ) Ġare Ġlisted Ġin ĠTable ĠT AB REF 39 . ĠThe Ġusers Ġat Ġthe Ġtop Ġ( Trump , ĠBach chan , ĠModi ) Ġall Ġdisplay Ġvery Ġstrong Ġpositive Ġcorrelation . ĠWe Ġname Ġthis Ġgroup ĠU PC Ġ( Users Ġwith ĠPositive ĠCor relation ), Ġand Ġthe Ġgroup Ġof Ġusers Ġat Ġthe Ġbottom Ġ( Grand e , ĠBieber , ĠKardashian ) Ġas ĠUNC Ġ( Users Ġwith ĠNegative ĠCor relation ). </s> M apping Ġwith Ġpopularity Ġ: :: ĠInterpret ation </s> For Ġusers Ġwith Ġpositive Ġcorrelation , Ġthe Ġhigher Ġthe Ġtweet 's Ġcharacterization Ġscore , Ġthe Ġmore Ġpopular Ġit Ġbecomes , Ġi . e . Ġthe Ġmore Ġlikes , Ġreplies , Ġand Ġret we ets Ġit Ġreceives . ĠIn Ġcontrast , Ġfor Ġusers Ġwith Ġnegative Ġcorrelation , Ġthe Ġhigher Ġthe Ġtweet Ġscore , Ġthe Ġless Ġpopular Ġit Ġbecomes . </s> Figure ĠFIG REF 41 Ġshows Ġthe Ġplot Ġof Ġlog Ġmean Ġnumber Ġof Ġlikes Ġper Ġbucket Ġvs . Ġtweet Ġscore Ġpercentile , Ġfor Ġusers Ġwith Ġthe Ġhighest Ġpositive Ġcorrelation . ĠSimilarly , ĠFigure ĠFIG REF 42 Ġshows Ġthe Ġplot Ġof Ġlog Ġmean Ġnumber Ġof Ġlikes Ġper Ġbucket Ġvs . Ġtweet Ġscore Ġpercentile , Ġfor Ġusers Ġwith Ġthe Ġhighest Ġnegative Ġcorrelation . </s> One Ġmay Ġquestion Ġwhether Ġthese Ġresults Ġare Ġdue Ġto Ġtemporal Ġeffects : Ġuser 's Ġpopularity Ġvary Ġwith Ġtime , Ġand Ġperhaps Ġthe Ġmodel 's Ġmore Ġcharacteristic Ġtweets Ġsimply Ġreflect Ġperiods Ġof Ġauthors hip . ĠFigures ĠFIG REF 41 Ġand ĠFIG REF 42 Ġdispro ve Ġthis Ġhypothesis . ĠHere Ġthe Ġcolor Ġof Ġeach Ġpoint Ġdenotes Ġthe Ġyear Ġfor Ġwhich Ġmost Ġtweets Ġare Ġpresent Ġin Ġthe Ġcorresponding Ġbucket . ĠSince Ġthe Ġdistribution Ġof Ġcolors Ġover Ġtime Ġis Ġnot Ġclustered , Ġwe Ġinfer Ġthat Ġthe Ġobserved Ġresult Ġis Ġnot Ġan Ġartifact Ġof Ġtemporal Ġeffects . ĠIn Ġboth Ġcases , Ġthere Ġis Ġa Ġstrong Ġtrend Ġin Ġtweet Ġpopularity Ġbased Ġon Ġtweet Ġscore . ĠWe Ġnote Ġthat Ġthe Ġplots Ġare Ġpresented Ġon Ġthe Ġlog Ġscale , Ġmeaning Ġthe Ġtrends Ġhere Ġare Ġexponential . </s> M apping Ġwith Ġpopularity Ġ: :: ĠQual itative ĠAnalysis </s> We Ġpresent Ġexamples Ġof Ġthe Ġmost Ġand Ġleast Ġcharacteristic Ġtweets Ġfor Ġcelebrities Ġfrom Ġthree Ġcategories , Ġalong Ġwith Ġtheir Ġcorresponding Ġcharacterization Ġscores Ġcomputed Ġusing ĠApproach ĠSEC REF 29 .
Introduction Social media platforms, particularly microblogging services such as Twitter, have become increasingly popular BIBREF0 as a means to express thoughts and opinions. Twitter users emit tweets about a wide variety of topics, which vary in the extent to which they reflect a user's personality, brand and interests. This observation motivates the question we consider here, of how to quantify the degree to which tweets are characteristic of their author? People who are familiar with a given author appear to be able to make such judgments confidently. For example, consider the following pair of tweets written by US President Donald Trump, at the extreme sides of our characterization scores (0.9996 vs. 0.0013) for him: Tweet 1: Thank you for joining us at the Lincoln Memorial tonight- a very special evening! Together, we are going to MAKE AMERICA GREAT AGAIN! Tweet 2: “The bend in the road is not the end of the road unless you refuse to take the turn." - Anonymous Although both these tweets are from the same account, we assert that Tweet 1 sounds more characteristic of Donald Trump than Tweet 2. We might also guess that the first is more popular than second. Indeed, Tweet 1 received 155,000 likes as opposed to only 234 for Tweet 2. Such an author characterization score has many possible applications. With the ability to identify the most/least characteristic tweets from a person, we can generate reduced excerpts for high-volume Twitter profiles. Similarly, identifying the least characteristic tweets can highlight unusual content or suspicious activity. A run of sufficiently unrepresentative tweets might be indicative that a hacker has taken control of a user's account. But more fundamentally, our work provides the necessary tool to study the question of how “characteristic-ness" or novelty are related to tweet popularity. Do tweets that are more characteristic of the user get more likes, replies and retweets? Is such a relationship universal, or does it depend upon the personality or domain of the author? Twitter users with a large follower base can employ our methods to understand how characteristic a new potential tweet sounds, and obtain an estimate of how popular it is likely to become. To answer these questions, we formally define the problem of author representativeness testing, and model the task as a binary classification problem. Our primary contributions in this paper include: Five approaches to authorship verification: As a proxy for the question of representativeness testing (which has no convincing source of ground truth without extensive human annotation), we consider the task of distinguishing tweets written by a given author from others they did not write. We compare five distinct computational approaches to such binary tweet classification (user vs. non-user). Our best model achieves a test accuracy of 90.37% over a dataset of 15 Twitter celebrities. We use the best performing model to compute a score (the probability of authorship), which quantifies how characteristic of the user a given tweet is. Human evaluation study: To verify that our results are in agreement with human judgment of how `characteristic' a tweet is, we ask human evaluators which of a pair of tweets sounds more characteristic of the given celebrity. The human evaluators are in agreement with our model 70.40% of the time, significant above the $0.05$ level for each of our 15 celebrities. Correlation analysis for popularity: Our characterization score exhibits strikingly high absolute correlation with popularity (likes, replies and retweets), despite the fact that tweet text is the only feature used to train the classifier which yields these scores. For 13 of the 15 celebrities in our dataset, we observe a statistically significant correlation between characterization score and popularity. Figure FIGREF4 shows the relation between tweet score and tweet popularity for Donald Trump and Justin Bieber respectively. The figure shows that the sign of this association differs for various celebrities, reflecting whether their audience seeks novelty or reinforcement. Iterative sampling for class imbalance: Our task requires distinguishing a user's tweets (perhaps 1,000 positive training examples) from the sea of all other user's tweets (implying billions of possible negative training examples). We present an iterative sampling technique to exploit this class imbalance, which improves the test accuracy for negative examples by 2.62%. Problem Formulation We formally define the author representativeness problem as follows: Input: A Twitter author $U$ and the collection of their tweets, and a new tweet $T$. Problem: Compute $\textrm {score}(T, U)$, the probability that $T$ was written by $U$. This score quantifies how characteristic of writer $U$, tweet $T$ is. Problem Formulation ::: Methodology In order to obtain this representativeness score, we model our task as a classification problem, where we seek to distinguish tweets from $U$ against tweets from all other users. By modeling this as a binary classification problem, it becomes possible to quantify how characteristic of a writer a tweet is, as a probability implied by its distance from the decision boundary. Thus, we obtain a characterization score between 0 and 1 for each tweet. Challenges: In training a classifier to distinguish between user and non-user tweets, we should ideally have an equal amount of examples of both classes. User tweets are simply all the tweets from that user's Twitter account, and measure perhaps in the thousands. Indeed, the number of tweets per user per day is limited to 2400 per day by current Twitter policy (https://help.twitter.com/en/rules-and-policies/twitter-limits). The negative examples consist of all tweets written by other Twitter users, a total of approximately 500 million per day (https://business.twitter.com). Thus there is an extreme class imbalance between user and non-user tweets. Moreover, the nature of language used on Twitter does not conform to formal syntactic or semantic rules. The sentences tend to be highly unstructured, and the vocabulary is not restricted to a particular dictionary. Problem Formulation ::: Data For the binary classification task described in Section SECREF6, we term tweets from $U$ as positive examples, and tweets from other users as negative examples. Positive examples: We take tweets written by 15 celebrities from various domains, from 01-Jan-2008 to 01-Dec-2018, as positive examples. Properties of these Twitter celebrities are provided in Table TABREF10. Negative examples: We have collected 1% of tweets from Twitter's daily feed using the Twitter API (https://developer.twitter.com/en/docs.html) to use as negative examples. Preprocessing and Filtering: We have preprocessed and filtered the data to remove tweets that are unrepresentative or too short for analysis. All text has been converted to lowercase, and stripped of punctuation marks and URLs. This is because our approaches are centered around word usage. However, in future models, punctuation may prove effective as a feature. Further, we restrict analysis to English language tweets containing no attached images. We select only tweets which are more than 10 words long, and contain at least 5 legitimate (dictionary) English words. We define an unedited transfer of an original tweet as a retweet, and remove these from our dataset. Since comments on retweets are written by the user themselves, we retain these in our dataset. We note that celebrity Twitter accounts can be handled by PR agencies, in addition to the owner themselves. Because our aim is to characterize Twitter profiles as entities, we have not attempted to distinguish between user-written and agency-written tweets. However, this is an interesting direction for future research. We use a train-test split of 70-30% on the positive examples, and generate negative training and test sets of the same sizes for each user, by randomly sampling from the large set of negative examples. Related work ::: Author identification and verification The challenge of author identification has a long history in NLP. PAN 2013 BIBREF1 introduced the question: “Given a set of documents by the same author, is an additional (out-of-set) document also by that author?” The corpus is comprised of text pieces from textbooks, newspaper articles, and fiction. Submissions to PAN 2014 BIBREF2 also model authorship verification as binary classification, by using non-author documents as negative examples. The best submission BIBREF3 in PAN 2013 uses the General Impostors (GI) method, which is a modification of the Impostors Method BIBREF4. The best submission BIBREF5 in PAN 2014 presents a modification of the GI method. These methods are based on the impostors framework BIBREF6. compressionveenman used compression distance as a document representation, for authorship verification in PAN 2013. worddist present a global feature extraction approach and achieve state-of-the-art accuracy for the PAN 2014 corpus. The best submission BIBREF7 in PAN 2015 BIBREF8 uses a character-level RNN model for author identification, in which each author is represented as a sub-model, and the recurrent layer is shared by all sub-models. This is useful if the number of authors is fixed, and the problem is modeled as multi-class classification. deeplearningauthid also approach multi-class author identification, using deep learning for feature extraction, and forensic using hierarchical clustering. intrinsicauthorverification propose an intrinsic profile-based verification method that uses latent semantic indexing (LSI), which is effective for longer texts. authoneclass and limiteddata explore methods for authorship verification for larger documents such as essays and novels. emailauthid and email2 explore author identification for emails, and taskguided for scientific papers. azarbonyad2015time make use of temporal changes in word usage to identify authors of tweets and emails. unigramsandbigrams, featureeval, and unstructured evaluate the utility of various features for this task. authidusingtextsampling proposes text sampling to address the lack of text samples of undisputed authorship, to produce a desirable distribution over classes. koppel2009computational compare methods for variants of the authorship attribution problem. bhargava2013stylometric apply stylometric analysis to tweets to determine the author. lopez2015discriminative propose a document representation capturing discriminative and subprofile-specific information of terms. rocha2016authorship review methods for authorship attribution for social media forensics. peng2016bit use bit-level n-grams for determining authorship for online news. peng2016astroturfing apply this method to detect astroturfing on social media. theophilo2019needle employ deep learning specifically for authorship attribution of short messages. Related work ::: Predicting tweet popularity suh2010want leverages features such as URL, number of hashtags, number of followers and followees etc. in a generalized linear model, to predict the number of retweets. naveed2011bad extend this approach to perform content-based retweet prediction using several features including sentiments, emoticons, punctuations etc. bandari2012pulse apply the same approach for regression as well as classification, to predict the number of retweets specifically for news articles. zaman2014bayesian present a Bayesian model for retweet prediction using early retweet times, retweets of other tweets, and the user's follower graph. tan2014effect analyze whether different wording of a tweet by the same author affects its popularity. SEISMIC BIBREF9 and PSEISMIC BIBREF10 are statistical methods to predict the final number of retweets. zhang2018predicting approach retweet prediction as a multi-class classification problem, and present a feature-weighted model, where weights are computed using information gain. Related work ::: Training with imbalanced datasets Various methods to handle imbalanced datasets have been described by kotsiantis2006handling. These include undersampling BIBREF11, oversampling, and feature selection BIBREF12 at the data level. However, due to random undersampling, potentially useful samples can be discarded, while random oversampling poses the risk of overfitting. This problem can be handled at the algorithmic level as well: the threshold method BIBREF13 produces several classifiers by varying the threshold of the classifier score. One-class classification can be performed using a divide-and-conquer approach, to iteratively build rules to cover new training instances BIBREF14. Cost-sensitive learning BIBREF15 uses unequal misclassification costs to address the class imbalance problem. Approaches to authorship verification As described in Section SECREF6, we build classification models to distinguish between user and non-user tweets. We have explored five distinct approaches to build such models. Approaches to authorship verification ::: Approach 1: Compression This approach is inspired from Kolmogorov complexity BIBREF16, which argues that the compressibility of a text reflects the quality of the underlying model. We use the Lempel-Ziv-Welch (LZW) compression algorithm BIBREF17 to approximate Kolmogorov complexity by dynamically building a dictionary to encode word patterns from the training corpus. The longest occurring pattern match present in the dictionary is used to encode the text. We hypothesize that the length of a tweet $T$ from user $U$, compressed using a dictionary built from positive examples, will be less than the length of the same tweet compressed using a dictionary built from negative examples. We use the following setup to classify test tweets for each Twitter user in our dataset: Build an encoding dictionary using positive examples ($\textrm {train}_{\textrm {pos}}$), and an encoding dictionary using negative examples ($\textrm {train}_{\textrm {neg}}$). Encode the new tweet $T$ using both these dictionaries, to obtain $T_{\textrm {pos}} = \textrm {encode}_{\textrm {pos}}(T)$ and $T_{\textrm {neg}} = \textrm {encode}_{\textrm {neg}}(T)$ respectively. If the length of $T_{\textrm {pos}}$ is less than that of $T_{\textrm {neg}}$, classify $T$ as positive; else, classify it as negative. This gives us the class label for each new tweet $T$. In addition, we compute the characterization score of tweet $T$ with respect to user $U$, as described in Equation DISPLAY_FORM18. Thus the shorter the length of the encoded tweet, the more characteristic of the user $T$ is. Approaches to authorship verification ::: Approach 2: Topic modeling We hypothesize that each user writes about topics with a particular probability distribution, and that each tweet reflects the probability distribution over these topics. We train a topic model using Latent Dirichlet Allocation (LDA) BIBREF18 on a large corpus of tweets, and use this topic model to compute topic distributions for individual tweets. We then use these values as features. We experiment with two types of classifiers: Logistic Regression (LR), and Multi Linear Perceptron (MLP) of size $(5, 5, 5)$. We represent each tweet as a distribution over $n=500$ topics. The characterization score of a tweet $T$ is given by the classifier's confidence that $T$ belongs to the positive class. Approaches to authorship verification ::: Approach 3: n-gram probability We hypothesize that a Twitter user can be characterized by usage of words and their frequencies in tweets, and model this using n-gram frequencies. We use the following setup to classify test tweets for each Twitter user in our dataset: Build a frequency dictionary of all n-grams in positive examples ($\textrm {train}_{\textrm {pos}}$), and a frequency dictionary of all n-grams in negative examples ($\textrm {train}_{\textrm {neg}}$). Compute the average probability of all n-gram sequences in the new tweet $T$ using both these dictionaries, to obtain $\textrm {prob}_{\textrm {pos}}(T)$ and $\textrm {prob}_{\textrm {neg}}(T)$ respectively. Here, we use add-one smoothing and conditional backoff to compute these probability values. If $\textrm {prob}_{\textrm {pos}}(T)$ is greater than $\textrm {prob}_{\textrm {neg}}(T)$, classify $T$ as positive; else, classify it as negative. The characterization score of tweet $T$ is given by the average n-gram probability computed using the frequency dictionary of $\textrm {train}_{\textrm {pos}}$. We experiment with $n = 1$ (unigrams) and $n = 2$ (bigrams). Approaches to authorship verification ::: Approach 4: Document embeddings We hypothesize that if we obtain latent representations of tweets as documents, tweets from the same author will cluster together, and will be differentiable from tweets from others. To that end, we use the following setup: We obtain representations of tweets as document embeddings. We experiment with two types of document embeddings: FastText BIBREF19 (embedding size = 100) and BERT-Base, uncased BIBREF20 (embedding size = 768). We then use these embeddings as features to train a classification model. We experiment with two types of classifiers: Logistic Regression (LR) and Multi Linear Perceptron (MLP) of size $(5, 5, 5)$. The characterization score of tweet $T$ is given by the classifier's confidence that $T$ belongs to the positive class. Iterative sampling: As described in Section SECREF6, there exists an extreme class imbalance for this binary classification task, in that the number of negative examples is far more than the number of positive examples. Here, we explore an iterative sampling technique to address this problem. We train our classifier for multiple iterations, coupling the same $\textrm {train}_{\textrm {pos}}$ with a new randomly sampled $\textrm {train}_{\textrm {neg}}$ set in each iteration. We conduct this experiment for all users with the best performing model for this approach, i.e. we use BERT embeddings as features, and MLP for classification. We train this classifier for 40 iterations, and compare the model's performance when we use the same set of negative examples vs. when we randomly sample new negative examples in each iteration. Figure FIGREF28 shows the mean train and test accuracy for all users over 40 iterations. As expected, the training accuracy is higher if we do not sample, as the model gets trained on the same data repeatedly in each iteration. However, if we perform random sampling, the model is exposed to a larger number of negative examples, which results in a higher test accuracy (+ 1.08%), specifically for negative test examples (+ 2.62%). Approaches to authorship verification ::: Approach 5: Token embeddings and sequential modeling In this approach, we tokenize each tweet, and obtain embeddings for each token. We then sequentially give these embeddings as input to a classifier. We use a pretrained model (BERT-Base, Uncased: 12-layer, 768-hidden, 12-heads, 110M parameters) to generate token embeddings of size 768, and pass these to a Long Short Term Memory (LSTM) BIBREF21 classifier. We use an LSTM layer with 768 units with dropout and recurrent dropout ratio 0.2, followed by a dense layer with sigmoid activation. We train this model using the Adam optimizer BIBREF22 and binary cross-entropy loss, with accuracy as the training metric. Approaches to authorship verification ::: Results and Comparison Table TABREF24 presents the user-wise test accuracy of the five approaches under the specified configurations. Note that the test set contains an equal number of positive and negative examples for each author. Other baselines that we attempted to compare against include the best submissions to the PAN 2013 and 2014 author verification challenge: pan2013winner and slightlymodifiedimpostor, which are variants of the Impostors Method. This challenge employed significantly longer documents (with an average of 1039, 845, and 4393 words per document for articles, essays and novels respectively, as opposed to an average of 19 words per tweet) and significantly fewer documents per author (an average of 3.2, 2.6 and 1 document/s per author, as opposed to an average of 6738 tweets per user). Our experiments with the authorship verification classifier BIBREF23 showed that the Impostors Method is prohibitively expensive on larger corpora, and also performed too inaccurately on short texts to provide a meaningful baseline. For 13 of the 15 users in our dataset, Approach SECREF29 (token embeddings followed by sequential modeling) has the highest accuracy. This model correctly identifies the author of 90.37% of all tweets in our study, and will be used to define the characterization score for our subsequent studies. User study To verify whether human evaluators are in agreement with our characterization model, we conducted a user study using MTurk BIBREF24. User study ::: Setup For each user in our dataset, we build a set of 20 tweet pairs, with one tweet each from the 50 top-scoring and bottom-scoring tweets written by the user. We ask the human evaluator to choose which tweet sounds more characteristic of the user. To validate that the MTurk worker knows enough about the Twitter user to pick a characteristic tweet, we use a qualification test containing a basic set of questions about the Twitter user. We were unable to find equal numbers of Turkers familiar with each subject, so our number of evaluators $n$ differs according to author. User study ::: Results Table TABREF33 describes the results obtained in the user study: the mean and standard deviation of percentage of answers in agreement with our model, the p-value, and the number of MTurk workers who completed each task. We find that the average agreement of human evaluators with our model is 70.40% over all 15 users in our dataset. For each of the 15 celebrities, the human evaluators agree with our model above a significance level of 0.05, and in 13 of 15 cases above a level of $10^{-5}$. This makes clear our scores are measuring what we intend to be measuring. Mapping with popularity ::: Correlation We now explore the relationship between characterization score and tweet popularity for each of the users in our dataset. To analyze this relationship, we perform the following procedure for each author $U$: Sort all tweets written by $U$ in ascending order of characterization score. Bucket the sorted tweets by percentile score (1 to 100). For each bucket, calculate the mean number of likes, replies, and retweets. Compute the correlation of this mean and the percentile score. The Pearson correlation coefficients (r-values) are listed in Table TABREF39. The users at the top (Trump, Bachchan, Modi) all display very strong positive correlation. We name this group UPC (Users with Positive Correlation), and the group of users at the bottom (Grande, Bieber, Kardashian) as UNC (Users with Negative Correlation). Mapping with popularity ::: Interpretation For users with positive correlation, the higher the tweet's characterization score, the more popular it becomes, i.e. the more likes, replies, and retweets it receives. In contrast, for users with negative correlation, the higher the tweet score, the less popular it becomes. Figure FIGREF41 shows the plot of log mean number of likes per bucket vs. tweet score percentile, for users with the highest positive correlation. Similarly, Figure FIGREF42 shows the plot of log mean number of likes per bucket vs. tweet score percentile, for users with the highest negative correlation. One may question whether these results are due to temporal effects: user's popularity vary with time, and perhaps the model's more characteristic tweets simply reflect periods of authorship. Figures FIGREF41 and FIGREF42 disprove this hypothesis. Here the color of each point denotes the year for which most tweets are present in the corresponding bucket. Since the distribution of colors over time is not clustered, we infer that the observed result is not an artifact of temporal effects. In both cases, there is a strong trend in tweet popularity based on tweet score. We note that the plots are presented on the log scale, meaning the trends here are exponential. Mapping with popularity ::: Qualitative Analysis We present examples of the most and least characteristic tweets for celebrities from three categories, along with their corresponding characterization scores computed using Approach SECREF29.
What kind of celebrities do they obtain tweets from?
4d28c99750095763c81bcd5544491a0ba51d9070
What Ġkind Ġof Ġcelebrities Ġdo Ġthey Ġobtain Ġtweets Ġfrom ?
A mit ab h ĠBach chan , ĠAri ana ĠGrande , ĠBarack ĠObama , ĠBill ĠGates , ĠDonald ĠTrump , Ċ Ell en ĠDe Gene res , ĠJ ĠK ĠRowling , ĠJimmy ĠFallon , ĠJustin ĠBieber , ĠKevin ĠDurant , ĠKim ĠKardashian , ĠLady ĠGaga , ĠLeBron ĠJames , N are ndra ĠModi , ĠOprah ĠWin frey
<s> What Ġkind Ġof Ġcelebrities Ġdo Ġthey Ġobtain Ġtweets Ġfrom ? </s> Introduction </s> Social Ġmedia Ġplatforms , Ġparticularly Ġmicro blog ging Ġservices Ġsuch Ġas ĠTwitter , Ġhave Ġbecome Ġincreasingly Ġpopular ĠB IB REF 0 Ġas Ġa Ġmeans Ġto Ġexpress Ġthoughts Ġand Ġopinions . ĠTwitter Ġusers Ġemit Ġtweets Ġabout Ġa Ġwide Ġvariety Ġof Ġtopics , Ġwhich Ġvary Ġin Ġthe Ġextent Ġto Ġwhich Ġthey Ġreflect Ġa Ġuser 's Ġpersonality , Ġbrand Ġand Ġinterests . ĠThis Ġobservation Ġmotiv ates Ġthe Ġquestion Ġwe Ġconsider Ġhere , Ġof Ġhow Ġto Ġquantify Ġthe Ġdegree Ġto Ġwhich Ġtweets Ġare Ġcharacteristic Ġof Ġtheir Ġauthor ? </s> People Ġwho Ġare Ġfamiliar Ġwith Ġa Ġgiven Ġauthor Ġappear Ġto Ġbe Ġable Ġto Ġmake Ġsuch Ġjudgments Ġconfidently . ĠFor Ġexample , Ġconsider Ġthe Ġfollowing Ġpair Ġof Ġtweets Ġwritten Ġby ĠUS ĠPresident ĠDonald ĠTrump , Ġat Ġthe Ġextreme Ġsides Ġof Ġour Ġcharacterization Ġscores Ġ( 0 . 99 96 Ġvs . Ġ0 . 00 13 ) Ġfor Ġhim : </s> Tweet Ġ1 : ĠThank Ġyou Ġfor Ġjoining Ġus Ġat Ġthe ĠLincoln ĠMemorial Ġtonight - Ġa Ġvery Ġspecial Ġevening ! ĠTogether , Ġwe Ġare Ġgoing Ġto ĠMAKE ĠAMERICA ĠGREAT ĠAGA IN ! </s> Tweet Ġ2 : ĠâĢ ľ The Ġbend Ġin Ġthe Ġroad Ġis Ġnot Ġthe Ġend Ġof Ġthe Ġroad Ġunless Ġyou Ġrefuse Ġto Ġtake Ġthe Ġturn ." Ġ- ĠAnonymous </s> Although Ġboth Ġthese Ġtweets Ġare Ġfrom Ġthe Ġsame Ġaccount , Ġwe Ġassert Ġthat ĠTweet Ġ1 Ġsounds Ġmore Ġcharacteristic Ġof ĠDonald ĠTrump Ġthan ĠTweet Ġ2 . ĠWe Ġmight Ġalso Ġguess Ġthat Ġthe Ġfirst Ġis Ġmore Ġpopular Ġthan Ġsecond . ĠIndeed , ĠTweet Ġ1 Ġreceived Ġ155 , 000 Ġlikes Ġas Ġopposed Ġto Ġonly Ġ234 Ġfor ĠTweet Ġ2 . </s> Such Ġan Ġauthor Ġcharacterization Ġscore Ġhas Ġmany Ġpossible Ġapplications . ĠWith Ġthe Ġability Ġto Ġidentify Ġthe Ġmost / le ast Ġcharacteristic Ġtweets Ġfrom Ġa Ġperson , Ġwe Ġcan Ġgenerate Ġreduced Ġexcerpts Ġfor Ġhigh - volume ĠTwitter Ġprofiles . ĠSimilarly , Ġidentifying Ġthe Ġleast Ġcharacteristic Ġtweets Ġcan Ġhighlight Ġunusual Ġcontent Ġor Ġsuspicious Ġactivity . ĠA Ġrun Ġof Ġsufficiently Ġun represent ative Ġtweets Ġmight Ġbe Ġindicative Ġthat Ġa Ġhacker Ġhas Ġtaken Ġcontrol Ġof Ġa Ġuser 's Ġaccount . </s> But Ġmore Ġfundamentally , Ġour Ġwork Ġprovides Ġthe Ġnecessary Ġtool Ġto Ġstudy Ġthe Ġquestion Ġof Ġhow ĠâĢ ľ character istic - ness " Ġor Ġnovelty Ġare Ġrelated Ġto Ġtweet Ġpopularity . ĠDo Ġtweets Ġthat Ġare Ġmore Ġcharacteristic Ġof Ġthe Ġuser Ġget Ġmore Ġlikes , Ġreplies Ġand Ġret we ets ? ĠIs Ġsuch Ġa Ġrelationship Ġuniversal , Ġor Ġdoes Ġit Ġdepend Ġupon Ġthe Ġpersonality Ġor Ġdomain Ġof Ġthe Ġauthor ? ĠTwitter Ġusers Ġwith Ġa Ġlarge Ġfollower Ġbase Ġcan Ġemploy Ġour Ġmethods Ġto Ġunderstand Ġhow Ġcharacteristic Ġa Ġnew Ġpotential Ġtweet Ġsounds , Ġand Ġobtain Ġan Ġestimate Ġof Ġhow Ġpopular Ġit Ġis Ġlikely Ġto Ġbecome . </s> To Ġanswer Ġthese Ġquestions , Ġwe Ġformally Ġdefine Ġthe Ġproblem Ġof Ġauthor Ġrepresent at iveness Ġtesting , Ġand Ġmodel Ġthe Ġtask Ġas Ġa Ġbinary Ġclassification Ġproblem . ĠOur Ġprimary Ġcontributions Ġin Ġthis Ġpaper Ġinclude : </s> Five Ġapproaches Ġto Ġauthors hip Ġverification : ĠAs Ġa Ġproxy Ġfor Ġthe Ġquestion Ġof Ġrepresent at iveness Ġtesting Ġ( which Ġhas Ġno Ġconvincing Ġsource Ġof Ġground Ġtruth Ġwithout Ġextensive Ġhuman Ġannotation ), Ġwe Ġconsider Ġthe Ġtask Ġof Ġdistinguishing Ġtweets Ġwritten Ġby Ġa Ġgiven Ġauthor Ġfrom Ġothers Ġthey Ġdid Ġnot Ġwrite . ĠWe Ġcompare Ġfive Ġdistinct Ġcomputational Ġapproaches Ġto Ġsuch Ġbinary Ġtweet Ġclassification Ġ( user Ġvs . Ġnon - user ). ĠOur Ġbest Ġmodel Ġachieves Ġa Ġtest Ġaccuracy Ġof Ġ90 . 37 % Ġover Ġa Ġdataset Ġof Ġ15 ĠTwitter Ġcelebrities . ĠWe Ġuse Ġthe Ġbest Ġperforming Ġmodel Ġto Ġcompute Ġa Ġscore Ġ( the Ġprobability Ġof Ġauthors hip ), Ġwhich Ġquant ifies Ġhow Ġcharacteristic Ġof Ġthe Ġuser Ġa Ġgiven Ġtweet Ġis . </s> Human Ġevaluation Ġstudy : ĠTo Ġverify Ġthat Ġour Ġresults Ġare Ġin Ġagreement Ġwith Ġhuman Ġjudgment Ġof Ġhow Ġ` character istic ' Ġa Ġtweet Ġis , Ġwe Ġask Ġhuman Ġeval u ators Ġwhich Ġof Ġa Ġpair Ġof Ġtweets Ġsounds Ġmore Ġcharacteristic Ġof Ġthe Ġgiven Ġcelebrity . ĠThe Ġhuman Ġeval u ators Ġare Ġin Ġagreement Ġwith Ġour Ġmodel Ġ70 . 40 % Ġof Ġthe Ġtime , Ġsignificant Ġabove Ġthe Ġ$ 0 . 05 $ Ġlevel Ġfor Ġeach Ġof Ġour Ġ15 Ġcelebrities . </s> Cor relation Ġanalysis Ġfor Ġpopularity : ĠOur Ġcharacterization Ġscore Ġexhibits Ġstrikingly Ġhigh Ġabsolute Ġcorrelation Ġwith Ġpopularity Ġ( l ikes , Ġreplies Ġand Ġret we ets ), Ġdespite Ġthe Ġfact Ġthat Ġtweet Ġtext Ġis Ġthe Ġonly Ġfeature Ġused Ġto Ġtrain Ġthe Ġclass ifier Ġwhich Ġyields Ġthese Ġscores . </s> For Ġ13 Ġof Ġthe Ġ15 Ġcelebrities Ġin Ġour Ġdataset , Ġwe Ġobserve Ġa Ġstatistically Ġsignificant Ġcorrelation Ġbetween Ġcharacterization Ġscore Ġand Ġpopularity . ĠFigure ĠFIG REF 4 Ġshows Ġthe Ġrelation Ġbetween Ġtweet Ġscore Ġand Ġtweet Ġpopularity Ġfor ĠDonald ĠTrump Ġand ĠJustin ĠBieber Ġrespectively . ĠThe Ġfigure Ġshows Ġthat Ġthe Ġsign Ġof Ġthis Ġassociation Ġdiffers Ġfor Ġvarious Ġcelebrities , Ġreflecting Ġwhether Ġtheir Ġaudience Ġseeks Ġnovelty Ġor Ġreinforcement . </s> Iter ative Ġsampling Ġfor Ġclass Ġimbalance : ĠOur Ġtask Ġrequires Ġdistinguishing Ġa Ġuser 's Ġtweets Ġ( perhaps Ġ1 , 000 Ġpositive Ġtraining Ġexamples ) Ġfrom Ġthe Ġsea Ġof Ġall Ġother Ġuser 's Ġtweets Ġ( im ply ing Ġbillions Ġof Ġpossible Ġnegative Ġtraining Ġexamples ). ĠWe Ġpresent Ġan Ġiter ative Ġsampling Ġtechnique Ġto Ġexploit Ġthis Ġclass Ġimbalance , Ġwhich Ġimproves Ġthe Ġtest Ġaccuracy Ġfor Ġnegative Ġexamples Ġby Ġ2 . 62 %. </s> Problem ĠForm ulation </s> We Ġformally Ġdefine Ġthe Ġauthor Ġrepresent at iveness Ġproblem Ġas Ġfollows : </s> Input : ĠA ĠTwitter Ġauthor Ġ$ U $ Ġand Ġthe Ġcollection Ġof Ġtheir Ġtweets , Ġand Ġa Ġnew Ġtweet Ġ$ T $. </s> Problem : ĠComp ute Ġ$\ text rm Ġ{ score } ( T , ĠU ) $, Ġthe Ġprobability Ġthat Ġ$ T $ Ġwas Ġwritten Ġby Ġ$ U $. ĠThis Ġscore Ġquant ifies Ġhow Ġcharacteristic Ġof Ġwriter Ġ$ U $, Ġtweet Ġ$ T $ Ġis . </s> Problem ĠForm ulation Ġ: :: ĠMethod ology </s> In Ġorder Ġto Ġobtain Ġthis Ġrepresent at iveness Ġscore , Ġwe Ġmodel Ġour Ġtask Ġas Ġa Ġclassification Ġproblem , Ġwhere Ġwe Ġseek Ġto Ġdistinguish Ġtweets Ġfrom Ġ$ U $ Ġagainst Ġtweets Ġfrom Ġall Ġother Ġusers . </s> By Ġmodeling Ġthis Ġas Ġa Ġbinary Ġclassification Ġproblem , Ġit Ġbecomes Ġpossible Ġto Ġquantify Ġhow Ġcharacteristic Ġof Ġa Ġwriter Ġa Ġtweet Ġis , Ġas Ġa Ġprobability Ġimplied Ġby Ġits Ġdistance Ġfrom Ġthe Ġdecision Ġboundary . ĠThus , Ġwe Ġobtain Ġa Ġcharacterization Ġscore Ġbetween Ġ0 Ġand Ġ1 Ġfor Ġeach Ġtweet . </s> Chall enges : ĠIn Ġtraining Ġa Ġclass ifier Ġto Ġdistinguish Ġbetween Ġuser Ġand Ġnon - user Ġtweets , Ġwe Ġshould Ġideally Ġhave Ġan Ġequal Ġamount Ġof Ġexamples Ġof Ġboth Ġclasses . ĠUser Ġtweets Ġare Ġsimply Ġall Ġthe Ġtweets Ġfrom Ġthat Ġuser 's ĠTwitter Ġaccount , Ġand Ġmeasure Ġperhaps Ġin Ġthe Ġthousands . ĠIndeed , Ġthe Ġnumber Ġof Ġtweets Ġper Ġuser Ġper Ġday Ġis Ġlimited Ġto Ġ2400 Ġper Ġday Ġby Ġcurrent ĠTwitter Ġpolicy Ġ( https :// help . twitter . com / en / rules - and - p olic ies / twitter - limits ). ĠThe Ġnegative Ġexamples Ġconsist Ġof Ġall Ġtweets Ġwritten Ġby Ġother ĠTwitter Ġusers , Ġa Ġtotal Ġof Ġapproximately Ġ500 Ġmillion Ġper Ġday Ġ( https :// business . twitter . com ). ĠThus Ġthere Ġis Ġan Ġextreme Ġclass Ġimbalance Ġbetween Ġuser Ġand Ġnon - user Ġtweets . ĠMoreover , Ġthe Ġnature Ġof Ġlanguage Ġused Ġon ĠTwitter Ġdoes Ġnot Ġconform Ġto Ġformal Ġsynt actic Ġor Ġsemantic Ġrules . ĠThe Ġsentences Ġtend Ġto Ġbe Ġhighly Ġun struct ured , Ġand Ġthe Ġvocabulary Ġis Ġnot Ġrestricted Ġto Ġa Ġparticular Ġdictionary . </s> Problem ĠForm ulation Ġ: :: ĠData </s> For Ġthe Ġbinary Ġclassification Ġtask Ġdescribed Ġin ĠSection ĠSEC REF 6 , Ġwe Ġterm Ġtweets Ġfrom Ġ$ U $ Ġas Ġpositive Ġexamples , Ġand Ġtweets Ġfrom Ġother Ġusers Ġas Ġnegative Ġexamples . </s> Pos itive Ġexamples : ĠWe Ġtake Ġtweets Ġwritten Ġby Ġ15 Ġcelebrities Ġfrom Ġvarious Ġdomains , Ġfrom Ġ01 - Jan - 2008 Ġto Ġ01 - Dec - 2018 , Ġas Ġpositive Ġexamples . ĠProperties Ġof Ġthese ĠTwitter Ġcelebrities Ġare Ġprovided Ġin ĠTable ĠT AB REF 10 . </s> Neg ative Ġexamples : ĠWe Ġhave Ġcollected Ġ1 % Ġof Ġtweets Ġfrom ĠTwitter 's Ġdaily Ġfeed Ġusing Ġthe ĠTwitter ĠAPI Ġ( https :// develop er . twitter . com / en / docs . html ) Ġto Ġuse Ġas Ġnegative Ġexamples . </s> Pre processing Ġand ĠFil tering : ĠWe Ġhave Ġpre process ed Ġand Ġfiltered Ġthe Ġdata Ġto Ġremove Ġtweets Ġthat Ġare Ġun represent ative Ġor Ġtoo Ġshort Ġfor Ġanalysis . ĠAll Ġtext Ġhas Ġbeen Ġconverted Ġto Ġlower case , Ġand Ġstripped Ġof Ġpunct uation Ġmarks Ġand ĠURLs . ĠThis Ġis Ġbecause Ġour Ġapproaches Ġare Ġcentered Ġaround Ġword Ġusage . ĠHowever , Ġin Ġfuture Ġmodels , Ġpunct uation Ġmay Ġprove Ġeffective Ġas Ġa Ġfeature . ĠFurther , Ġwe Ġrestrict Ġanalysis Ġto ĠEnglish Ġlanguage Ġtweets Ġcontaining Ġno Ġattached Ġimages . ĠWe Ġselect Ġonly Ġtweets Ġwhich Ġare Ġmore Ġthan Ġ10 Ġwords Ġlong , Ġand Ġcontain Ġat Ġleast Ġ5 Ġlegitimate Ġ( d ictionary ) ĠEnglish Ġwords . ĠWe Ġdefine Ġan Ġun edited Ġtransfer Ġof Ġan Ġoriginal Ġtweet Ġas Ġa Ġretweet , Ġand Ġremove Ġthese Ġfrom Ġour Ġdataset . ĠSince Ġcomments Ġon Ġret we ets Ġare Ġwritten Ġby Ġthe Ġuser Ġthemselves , Ġwe Ġretain Ġthese Ġin Ġour Ġdataset . </s> We Ġnote Ġthat Ġcelebrity ĠTwitter Ġaccounts Ġcan Ġbe Ġhandled Ġby ĠPR Ġagencies , Ġin Ġaddition Ġto Ġthe Ġowner Ġthemselves . ĠBecause Ġour Ġaim Ġis Ġto Ġcharacterize ĠTwitter Ġprofiles Ġas Ġentities , Ġwe Ġhave Ġnot Ġattempted Ġto Ġdistinguish Ġbetween Ġuser - written Ġand Ġagency - written Ġtweets . ĠHowever , Ġthis Ġis Ġan Ġinteresting Ġdirection Ġfor Ġfuture Ġresearch . </s> We Ġuse Ġa Ġtrain - test Ġsplit Ġof Ġ70 - 30 % Ġon Ġthe Ġpositive Ġexamples , Ġand Ġgenerate Ġnegative Ġtraining Ġand Ġtest Ġsets Ġof Ġthe Ġsame Ġsizes Ġfor Ġeach Ġuser , Ġby Ġrandomly Ġsampling Ġfrom Ġthe Ġlarge Ġset Ġof Ġnegative Ġexamples . </s> Related Ġwork Ġ: :: ĠAuthor Ġidentification Ġand Ġverification </s> The Ġchallenge Ġof Ġauthor Ġidentification Ġhas Ġa Ġlong Ġhistory Ġin ĠN LP . ĠPAN Ġ2013 ĠB IB REF 1 Ġintroduced Ġthe Ġquestion : ĠâĢ ľ Given Ġa Ġset Ġof Ġdocuments Ġby Ġthe Ġsame Ġauthor , Ġis Ġan Ġadditional Ġ( out - of - set ) Ġdocument Ġalso Ġby Ġthat Ġauthor ? âĢ Ŀ ĠThe Ġcorpus Ġis Ġcomprised Ġof Ġtext Ġpieces Ġfrom Ġtextbooks , Ġnewspaper Ġarticles , Ġand Ġfiction . ĠSub missions Ġto ĠPAN Ġ2014 ĠB IB REF 2 Ġalso Ġmodel Ġauthors hip Ġverification Ġas Ġbinary Ġclassification , Ġby Ġusing Ġnon - author Ġdocuments Ġas Ġnegative Ġexamples . ĠThe Ġbest Ġsubmission ĠB IB REF 3 Ġin ĠPAN Ġ2013 Ġuses Ġthe ĠGeneral ĠIm post ors Ġ( GI ) Ġmethod , Ġwhich Ġis Ġa Ġmodification Ġof Ġthe ĠIm post ors ĠMethod ĠB IB REF 4 . ĠThe Ġbest Ġsubmission ĠB IB REF 5 Ġin ĠPAN Ġ2014 Ġpresents Ġa Ġmodification Ġof Ġthe ĠGI Ġmethod . ĠThese Ġmethods Ġare Ġbased Ġon Ġthe Ġimp ost ors Ġframework ĠB IB REF 6 . </s> comp ression ve en man Ġused Ġcompression Ġdistance Ġas Ġa Ġdocument Ġrepresentation , Ġfor Ġauthors hip Ġverification Ġin ĠPAN Ġ2013 . Ġword dist Ġpresent Ġa Ġglobal Ġfeature Ġextraction Ġapproach Ġand Ġachieve Ġstate - of - the - art Ġaccuracy Ġfor Ġthe ĠPAN Ġ2014 Ġcorpus . ĠThe Ġbest Ġsubmission ĠB IB REF 7 Ġin ĠPAN Ġ2015 ĠB IB REF 8 Ġuses Ġa Ġcharacter - level ĠR NN Ġmodel Ġfor Ġauthor Ġidentification , Ġin Ġwhich Ġeach Ġauthor Ġis Ġrepresented Ġas Ġa Ġsub - model , Ġand Ġthe Ġrecurrent Ġlayer Ġis Ġshared Ġby Ġall Ġsub - models . ĠThis Ġis Ġuseful Ġif Ġthe Ġnumber Ġof Ġauthors Ġis Ġfixed , Ġand Ġthe Ġproblem Ġis Ġmodeled Ġas Ġmulti - class Ġclassification . Ġdeep learning auth id Ġalso Ġapproach Ġmulti - class Ġauthor Ġidentification , Ġusing Ġdeep Ġlearning Ġfor Ġfeature Ġextraction , Ġand Ġforensic Ġusing Ġhierarchical Ġclust ering . </s> int r ins ica uthor ver ification Ġpropose Ġan Ġintrinsic Ġprofile - based Ġverification Ġmethod Ġthat Ġuses Ġlatent Ġsemantic Ġindex ing Ġ( LS I ), Ġwhich Ġis Ġeffective Ġfor Ġlonger Ġtexts . Ġauth one class Ġand Ġlimited data Ġexplore Ġmethods Ġfor Ġauthors hip Ġverification Ġfor Ġlarger Ġdocuments Ġsuch Ġas Ġessays Ġand Ġnovels . Ġemail auth id Ġand Ġemail 2 Ġexplore Ġauthor Ġidentification Ġfor Ġemails , Ġand Ġtask guided Ġfor Ġscientific Ġpapers . Ġaz arb ony ad 2015 time Ġmake Ġuse Ġof Ġtemporal Ġchanges Ġin Ġword Ġusage Ġto Ġidentify Ġauthors Ġof Ġtweets Ġand Ġemails . Ġun ig rams and big rams , Ġfeature eval , Ġand Ġun struct ured Ġevaluate Ġthe Ġutility Ġof Ġvarious Ġfeatures Ġfor Ġthis Ġtask . Ġauth id using text sam pling Ġproposes Ġtext Ġsampling Ġto Ġaddress Ġthe Ġlack Ġof Ġtext Ġsamples Ġof Ġundis puted Ġauthors hip , Ġto Ġproduce Ġa Ġdesirable Ġdistribution Ġover Ġclasses . </s> ko ppel 2009 com put ational Ġcompare Ġmethods Ġfor Ġvariants Ġof Ġthe Ġauthors hip Ġattribution Ġproblem . Ġb h arg ava 2013 st yl ometric Ġapply Ġstyl ometric Ġanalysis Ġto Ġtweets Ġto Ġdetermine Ġthe Ġauthor . Ġl opez 2015 disc rim in ative Ġpropose Ġa Ġdocument Ġrepresentation Ġcapturing Ġdiscrim in ative Ġand Ġsub profile - specific Ġinformation Ġof Ġterms . Ġro cha 2016 auth orship Ġreview Ġmethods Ġfor Ġauthors hip Ġattribution Ġfor Ġsocial Ġmedia Ġfore nsics . Ġp eng 2016 bit Ġuse Ġbit - level Ġn - gram s Ġfor Ġdetermining Ġauthors hip Ġfor Ġonline Ġnews . Ġp eng 2016 ast rot ur fing Ġapply Ġthis Ġmethod Ġto Ġdetect Ġast rot ur fing Ġon Ġsocial Ġmedia . Ġthe oph ilo 2019 need le Ġemploy Ġdeep Ġlearning Ġspecifically Ġfor Ġauthors hip Ġattribution Ġof Ġshort Ġmessages . </s> Related Ġwork Ġ: :: ĠPredict ing Ġtweet Ġpopularity </s> su h 2010 want Ġlever ages Ġfeatures Ġsuch Ġas ĠURL , Ġnumber Ġof Ġhasht ags , Ġnumber Ġof Ġfollowers Ġand Ġfollow ees Ġetc . Ġin Ġa Ġgeneralized Ġlinear Ġmodel , Ġto Ġpredict Ġthe Ġnumber Ġof Ġret we ets . Ġn ave ed 2011 bad Ġextend Ġthis Ġapproach Ġto Ġperform Ġcontent - based Ġretweet Ġprediction Ġusing Ġseveral Ġfeatures Ġincluding Ġsentiments , Ġemot icons , Ġpunct uations Ġetc . Ġband ari 2012 p ulse Ġapply Ġthe Ġsame Ġapproach Ġfor Ġregression Ġas Ġwell Ġas Ġclassification , Ġto Ġpredict Ġthe Ġnumber Ġof Ġret we ets Ġspecifically Ġfor Ġnews Ġarticles . Ġz aman 2014 bay esian Ġpresent Ġa ĠBay esian Ġmodel Ġfor Ġretweet Ġprediction Ġusing Ġearly Ġretweet Ġtimes , Ġret we ets Ġof Ġother Ġtweets , Ġand Ġthe Ġuser 's Ġfollower Ġgraph . Ġtan 2014 effect Ġanalyze Ġwhether Ġdifferent Ġwording Ġof Ġa Ġtweet Ġby Ġthe Ġsame Ġauthor Ġaffects Ġits Ġpopularity . ĠSE ISM IC ĠB IB REF 9 Ġand ĠP SE ISM IC ĠB IB REF 10 Ġare Ġstatistical Ġmethods Ġto Ġpredict Ġthe Ġfinal Ġnumber Ġof Ġret we ets . Ġz hang 2018 p redict ing Ġapproach Ġretweet Ġprediction Ġas Ġa Ġmulti - class Ġclassification Ġproblem , Ġand Ġpresent Ġa Ġfeature - weight ed Ġmodel , Ġwhere Ġweights Ġare Ġcomputed Ġusing Ġinformation Ġgain . </s> Related Ġwork Ġ: :: ĠTraining Ġwith Ġim balanced Ġdatasets </s> Various Ġmethods Ġto Ġhandle Ġim balanced Ġdatasets Ġhave Ġbeen Ġdescribed Ġby Ġk ots iant is 2006 hand ling . ĠThese Ġinclude Ġunders am pling ĠB IB REF 11 , Ġovers am pling , Ġand Ġfeature Ġselection ĠB IB REF 12 Ġat Ġthe Ġdata Ġlevel . ĠHowever , Ġdue Ġto Ġrandom Ġunders am pling , Ġpotentially Ġuseful Ġsamples Ġcan Ġbe Ġdiscarded , Ġwhile Ġrandom Ġovers am pling Ġposes Ġthe Ġrisk Ġof Ġover fitting . ĠThis Ġproblem Ġcan Ġbe Ġhandled Ġat Ġthe Ġalgorith mic Ġlevel Ġas Ġwell : Ġthe Ġthreshold Ġmethod ĠB IB REF 13 Ġproduces Ġseveral Ġclass ifiers Ġby Ġvarying Ġthe Ġthreshold Ġof Ġthe Ġclass ifier Ġscore . ĠOne - class Ġclassification Ġcan Ġbe Ġperformed Ġusing Ġa Ġdivide - and - con quer Ġapproach , Ġto Ġiter atively Ġbuild Ġrules Ġto Ġcover Ġnew Ġtraining Ġinstances ĠB IB REF 14 . ĠCost - sensitive Ġlearning ĠB IB REF 15 Ġuses Ġunequal Ġmis class ification Ġcosts Ġto Ġaddress Ġthe Ġclass Ġimbalance Ġproblem . </s> App ro aches Ġto Ġauthors hip Ġverification </s> As Ġdescribed Ġin ĠSection ĠSEC REF 6 , Ġwe Ġbuild Ġclassification Ġmodels Ġto Ġdistinguish Ġbetween Ġuser Ġand Ġnon - user Ġtweets . ĠWe Ġhave Ġexplored Ġfive Ġdistinct Ġapproaches Ġto Ġbuild Ġsuch Ġmodels . </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠApproach Ġ1 : ĠComp ression </s> This Ġapproach Ġis Ġinspired Ġfrom ĠKol m og or ov Ġcomplexity ĠB IB REF 16 , Ġwhich Ġargues Ġthat Ġthe Ġcompress ibility Ġof Ġa Ġtext Ġreflects Ġthe Ġquality Ġof Ġthe Ġunderlying Ġmodel . ĠWe Ġuse Ġthe ĠLem pel - Z iv - W el ch Ġ( L Z W ) Ġcompression Ġalgorithm ĠB IB REF 17 Ġto Ġapproximate ĠKol m og or ov Ġcomplexity Ġby Ġdynamically Ġbuilding Ġa Ġdictionary Ġto Ġencode Ġword Ġpatterns Ġfrom Ġthe Ġtraining Ġcorpus . ĠThe Ġlongest Ġoccurring Ġpattern Ġmatch Ġpresent Ġin Ġthe Ġdictionary Ġis Ġused Ġto Ġencode Ġthe Ġtext . </s> We Ġhypothes ize Ġthat Ġthe Ġlength Ġof Ġa Ġtweet Ġ$ T $ Ġfrom Ġuser Ġ$ U $, Ġcompressed Ġusing Ġa Ġdictionary Ġbuilt Ġfrom Ġpositive Ġexamples , Ġwill Ġbe Ġless Ġthan Ġthe Ġlength Ġof Ġthe Ġsame Ġtweet Ġcompressed Ġusing Ġa Ġdictionary Ġbuilt Ġfrom Ġnegative Ġexamples . </s> We Ġuse Ġthe Ġfollowing Ġsetup Ġto Ġclassify Ġtest Ġtweets Ġfor Ġeach ĠTwitter Ġuser Ġin Ġour Ġdataset : </s> Build Ġan Ġencoding Ġdictionary Ġusing Ġpositive Ġexamples Ġ($ \ text rm Ġ{ train } _{ \ text rm Ġ{ pos }} $ ), Ġand Ġan Ġencoding Ġdictionary Ġusing Ġnegative Ġexamples Ġ($ \ text rm Ġ{ train } _{ \ text rm Ġ{ neg }} $ ). </s> En code Ġthe Ġnew Ġtweet Ġ$ T $ Ġusing Ġboth Ġthese Ġdiction aries , Ġto Ġobtain Ġ$ T _{ \ text rm Ġ{ pos }} Ġ= Ġ\ text rm Ġ{ en code } _{ \ text rm Ġ{ pos }} ( T ) $ Ġand Ġ$ T _{ \ text rm Ġ{ neg }} Ġ= Ġ\ text rm Ġ{ en code } _{ \ text rm Ġ{ neg }} ( T ) $ Ġrespectively . </s> If Ġthe Ġlength Ġof Ġ$ T _{ \ text rm Ġ{ pos }} $ Ġis Ġless Ġthan Ġthat Ġof Ġ$ T _{ \ text rm Ġ{ neg }} $, Ġclassify Ġ$ T $ Ġas Ġpositive ; Ġelse , Ġclassify Ġit Ġas Ġnegative . </s> This Ġgives Ġus Ġthe Ġclass Ġlabel Ġfor Ġeach Ġnew Ġtweet Ġ$ T $. ĠIn Ġaddition , Ġwe Ġcompute Ġthe Ġcharacterization Ġscore Ġof Ġtweet Ġ$ T $ Ġwith Ġrespect Ġto Ġuser Ġ$ U $, Ġas Ġdescribed Ġin ĠEqu ation ĠDIS PLAY _ FORM 18 . </s> Thus Ġthe Ġshorter Ġthe Ġlength Ġof Ġthe Ġencoded Ġtweet , Ġthe Ġmore Ġcharacteristic Ġof Ġthe Ġuser Ġ$ T $ Ġis . </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠApproach Ġ2 : ĠTopic Ġmodeling </s> We Ġhypothes ize Ġthat Ġeach Ġuser Ġwrites Ġabout Ġtopics Ġwith Ġa Ġparticular Ġprobability Ġdistribution , Ġand Ġthat Ġeach Ġtweet Ġreflects Ġthe Ġprobability Ġdistribution Ġover Ġthese Ġtopics . ĠWe Ġtrain Ġa Ġtopic Ġmodel Ġusing ĠLat ent ĠDir ich let ĠAll ocation Ġ( L DA ) ĠB IB REF 18 Ġon Ġa Ġlarge Ġcorpus Ġof Ġtweets , Ġand Ġuse Ġthis Ġtopic Ġmodel Ġto Ġcompute Ġtopic Ġdistributions Ġfor Ġindividual Ġtweets . ĠWe Ġthen Ġuse Ġthese Ġvalues Ġas Ġfeatures . ĠWe Ġexperiment Ġwith Ġtwo Ġtypes Ġof Ġclass ifiers : ĠLog istic ĠReg ression Ġ( LR ), Ġand ĠMulti ĠLinear ĠPer cept ron Ġ( ML P ) Ġof Ġsize Ġ$( 5 , Ġ5 , Ġ5 ) $. ĠWe Ġrepresent Ġeach Ġtweet Ġas Ġa Ġdistribution Ġover Ġ$ n = 500 $ Ġtopics . </s> The Ġcharacterization Ġscore Ġof Ġa Ġtweet Ġ$ T $ Ġis Ġgiven Ġby Ġthe Ġclass ifier 's Ġconfidence Ġthat Ġ$ T $ Ġbelongs Ġto Ġthe Ġpositive Ġclass . </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠApproach Ġ3 : Ġn - gram Ġprobability </s> We Ġhypothes ize Ġthat Ġa ĠTwitter Ġuser Ġcan Ġbe Ġcharacterized Ġby Ġusage Ġof Ġwords Ġand Ġtheir Ġfrequencies Ġin Ġtweets , Ġand Ġmodel Ġthis Ġusing Ġn - gram Ġfrequencies . </s> We Ġuse Ġthe Ġfollowing Ġsetup Ġto Ġclassify Ġtest Ġtweets Ġfor Ġeach ĠTwitter Ġuser Ġin Ġour Ġdataset : </s> Build Ġa Ġfrequency Ġdictionary Ġof Ġall Ġn - gram s Ġin Ġpositive Ġexamples Ġ($ \ text rm Ġ{ train } _{ \ text rm Ġ{ pos }} $ ), Ġand Ġa Ġfrequency Ġdictionary Ġof Ġall Ġn - gram s Ġin Ġnegative Ġexamples Ġ($ \ text rm Ġ{ train } _{ \ text rm Ġ{ neg }} $ ). </s> Comp ute Ġthe Ġaverage Ġprobability Ġof Ġall Ġn - gram Ġsequences Ġin Ġthe Ġnew Ġtweet Ġ$ T $ Ġusing Ġboth Ġthese Ġdiction aries , Ġto Ġobtain Ġ$\ text rm Ġ{ pro b } _{ \ text rm Ġ{ pos }} ( T ) $ Ġand Ġ$\ text rm Ġ{ pro b } _{ \ text rm Ġ{ neg }} ( T ) $ Ġrespectively . ĠHere , Ġwe Ġuse Ġadd - one Ġsmoot hing Ġand Ġconditional Ġback off Ġto Ġcompute Ġthese Ġprobability Ġvalues . </s> If Ġ$\ text rm Ġ{ pro b } _{ \ text rm Ġ{ pos }} ( T ) $ Ġis Ġgreater Ġthan Ġ$\ text rm Ġ{ pro b } _{ \ text rm Ġ{ neg }} ( T ) $, Ġclassify Ġ$ T $ Ġas Ġpositive ; Ġelse , Ġclassify Ġit Ġas Ġnegative . </s> The Ġcharacterization Ġscore Ġof Ġtweet Ġ$ T $ Ġis Ġgiven Ġby Ġthe Ġaverage Ġn - gram Ġprobability Ġcomputed Ġusing Ġthe Ġfrequency Ġdictionary Ġof Ġ$\ text rm Ġ{ train } _{ \ text rm Ġ{ pos }} $. ĠWe Ġexperiment Ġwith Ġ$ n Ġ= Ġ1 $ Ġ( un ig rams ) Ġand Ġ$ n Ġ= Ġ2 $ Ġ( big rams ). </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠApproach Ġ4 : ĠDocument Ġembed d ings </s> We Ġhypothes ize Ġthat Ġif Ġwe Ġobtain Ġlatent Ġrepresentations Ġof Ġtweets Ġas Ġdocuments , Ġtweets Ġfrom Ġthe Ġsame Ġauthor Ġwill Ġcluster Ġtogether , Ġand Ġwill Ġbe Ġdifferent iable Ġfrom Ġtweets Ġfrom Ġothers . ĠTo Ġthat Ġend , Ġwe Ġuse Ġthe Ġfollowing Ġsetup : </s> We Ġobtain Ġrepresentations Ġof Ġtweets Ġas Ġdocument Ġembed d ings . ĠWe Ġexperiment Ġwith Ġtwo Ġtypes Ġof Ġdocument Ġembed d ings : ĠFast Text ĠB IB REF 19 Ġ( embed ding Ġsize Ġ= Ġ100 ) Ġand ĠB ERT - Base , Ġunc ased ĠB IB REF 20 Ġ( embed ding Ġsize Ġ= Ġ768 ). </s> We Ġthen Ġuse Ġthese Ġembed d ings Ġas Ġfeatures Ġto Ġtrain Ġa Ġclassification Ġmodel . ĠWe Ġexperiment Ġwith Ġtwo Ġtypes Ġof Ġclass ifiers : ĠLog istic ĠReg ression Ġ( LR ) Ġand ĠMulti ĠLinear ĠPer cept ron Ġ( ML P ) Ġof Ġsize Ġ$( 5 , Ġ5 , Ġ5 ) $. </s> The Ġcharacterization Ġscore Ġof Ġtweet Ġ$ T $ Ġis Ġgiven Ġby Ġthe Ġclass ifier 's Ġconfidence Ġthat Ġ$ T $ Ġbelongs Ġto Ġthe Ġpositive Ġclass . </s> Iter ative Ġsampling : ĠAs Ġdescribed Ġin ĠSection ĠSEC REF 6 , Ġthere Ġexists Ġan Ġextreme Ġclass Ġimbalance Ġfor Ġthis Ġbinary Ġclassification Ġtask , Ġin Ġthat Ġthe Ġnumber Ġof Ġnegative Ġexamples Ġis Ġfar Ġmore Ġthan Ġthe Ġnumber Ġof Ġpositive Ġexamples . ĠHere , Ġwe Ġexplore Ġan Ġiter ative Ġsampling Ġtechnique Ġto Ġaddress Ġthis Ġproblem . ĠWe Ġtrain Ġour Ġclass ifier Ġfor Ġmultiple Ġiterations , Ġcoupling Ġthe Ġsame Ġ$\ text rm Ġ{ train } _{ \ text rm Ġ{ pos }} $ Ġwith Ġa Ġnew Ġrandomly Ġsampled Ġ$\ text rm Ġ{ train } _{ \ text rm Ġ{ neg }} $ Ġset Ġin Ġeach Ġiteration . </s> We Ġconduct Ġthis Ġexperiment Ġfor Ġall Ġusers Ġwith Ġthe Ġbest Ġperforming Ġmodel Ġfor Ġthis Ġapproach , Ġi . e . Ġwe Ġuse ĠB ERT Ġembed d ings Ġas Ġfeatures , Ġand ĠML P Ġfor Ġclassification . ĠWe Ġtrain Ġthis Ġclass ifier Ġfor Ġ40 Ġiterations , Ġand Ġcompare Ġthe Ġmodel 's Ġperformance Ġwhen Ġwe Ġuse Ġthe Ġsame Ġset Ġof Ġnegative Ġexamples Ġvs . Ġwhen Ġwe Ġrandomly Ġsample Ġnew Ġnegative Ġexamples Ġin Ġeach Ġiteration . </s> Figure ĠFIG REF 28 Ġshows Ġthe Ġmean Ġtrain Ġand Ġtest Ġaccuracy Ġfor Ġall Ġusers Ġover Ġ40 Ġiterations . ĠAs Ġexpected , Ġthe Ġtraining Ġaccuracy Ġis Ġhigher Ġif Ġwe Ġdo Ġnot Ġsample , Ġas Ġthe Ġmodel Ġgets Ġtrained Ġon Ġthe Ġsame Ġdata Ġrepeatedly Ġin Ġeach Ġiteration . ĠHowever , Ġif Ġwe Ġperform Ġrandom Ġsampling , Ġthe Ġmodel Ġis Ġexposed Ġto Ġa Ġlarger Ġnumber Ġof Ġnegative Ġexamples , Ġwhich Ġresults Ġin Ġa Ġhigher Ġtest Ġaccuracy Ġ(+ Ġ1 . 08 %), Ġspecifically Ġfor Ġnegative Ġtest Ġexamples Ġ(+ Ġ2 . 62 %). </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠApproach Ġ5 : ĠToken Ġembed d ings Ġand Ġsequential Ġmodeling </s> In Ġthis Ġapproach , Ġwe Ġtoken ize Ġeach Ġtweet , Ġand Ġobtain Ġembed d ings Ġfor Ġeach Ġtoken . ĠWe Ġthen Ġsequ entially Ġgive Ġthese Ġembed d ings Ġas Ġinput Ġto Ġa Ġclass ifier . </s> We Ġuse Ġa Ġpret rained Ġmodel Ġ( BER T - Base , ĠUnc ased : Ġ12 - layer , Ġ768 - hidden , Ġ12 - heads , Ġ110 M Ġparameters ) Ġto Ġgenerate Ġtoken Ġembed d ings Ġof Ġsize Ġ768 , Ġand Ġpass Ġthese Ġto Ġa ĠLong ĠShort ĠTerm ĠMemory Ġ( L ST M ) ĠB IB REF 21 Ġclass ifier . ĠWe Ġuse Ġan ĠL ST M Ġlayer Ġwith Ġ768 Ġunits Ġwith Ġdrop out Ġand Ġrecurrent Ġdrop out Ġratio Ġ0 . 2 , Ġfollowed Ġby Ġa Ġdense Ġlayer Ġwith Ġs igm oid Ġactivation . ĠWe Ġtrain Ġthis Ġmodel Ġusing Ġthe ĠAdam Ġoptim izer ĠB IB REF 22 Ġand Ġbinary Ġcross - ent ropy Ġloss , Ġwith Ġaccuracy Ġas Ġthe Ġtraining Ġmetric . </s> App ro aches Ġto Ġauthors hip Ġverification Ġ: :: ĠResults Ġand ĠComparison </s> Table ĠT AB REF 24 Ġpresents Ġthe Ġuser - wise Ġtest Ġaccuracy Ġof Ġthe Ġfive Ġapproaches Ġunder Ġthe Ġspecified Ġconfigurations . ĠNote Ġthat Ġthe Ġtest Ġset Ġcontains Ġan Ġequal Ġnumber Ġof Ġpositive Ġand Ġnegative Ġexamples Ġfor Ġeach Ġauthor . </s> Other Ġbas elines Ġthat Ġwe Ġattempted Ġto Ġcompare Ġagainst Ġinclude Ġthe Ġbest Ġsubmissions Ġto Ġthe ĠPAN Ġ2013 Ġand Ġ2014 Ġauthor Ġverification Ġchallenge : Ġpan 2013 winner Ġand Ġslightly modified im post or , Ġwhich Ġare Ġvariants Ġof Ġthe ĠIm post ors ĠMethod . ĠThis Ġchallenge Ġemployed Ġsignificantly Ġlonger Ġdocuments Ġ( with Ġan Ġaverage Ġof Ġ10 39 , Ġ8 45 , Ġand Ġ4 393 Ġwords Ġper Ġdocument Ġfor Ġarticles , Ġessays Ġand Ġnovels Ġrespectively , Ġas Ġopposed Ġto Ġan Ġaverage Ġof Ġ19 Ġwords Ġper Ġtweet ) Ġand Ġsignificantly Ġfewer Ġdocuments Ġper Ġauthor Ġ( an Ġaverage Ġof Ġ3 . 2 , Ġ2 . 6 Ġand Ġ1 Ġdocument / s Ġper Ġauthor , Ġas Ġopposed Ġto Ġan Ġaverage Ġof Ġ67 38 Ġtweets Ġper Ġuser ). ĠOur Ġexperiments Ġwith Ġthe Ġauthors hip Ġverification Ġclass ifier ĠB IB REF 23 Ġshowed Ġthat Ġthe ĠIm post ors ĠMethod Ġis Ġprohib itive ly Ġexpensive Ġon Ġlarger Ġcorpor a , Ġand Ġalso Ġperformed Ġtoo Ġinaccur ately Ġon Ġshort Ġtexts Ġto Ġprovide Ġa Ġmeaningful Ġbaseline . </s> For Ġ13 Ġof Ġthe Ġ15 Ġusers Ġin Ġour Ġdataset , ĠApproach ĠSEC REF 29 Ġ( token Ġembed d ings Ġfollowed Ġby Ġsequential Ġmodeling ) Ġhas Ġthe Ġhighest Ġaccuracy . ĠThis Ġmodel Ġcorrectly Ġidentifies Ġthe Ġauthor Ġof Ġ90 . 37 % Ġof Ġall Ġtweets Ġin Ġour Ġstudy , Ġand Ġwill Ġbe Ġused Ġto Ġdefine Ġthe Ġcharacterization Ġscore Ġfor Ġour Ġsubsequent Ġstudies . </s> User Ġstudy </s> To Ġverify Ġwhether Ġhuman Ġeval u ators Ġare Ġin Ġagreement Ġwith Ġour Ġcharacterization Ġmodel , Ġwe Ġconducted Ġa Ġuser Ġstudy Ġusing ĠM Tur k ĠB IB REF 24 . </s> User Ġstudy Ġ: :: ĠSetup </s> For Ġeach Ġuser Ġin Ġour Ġdataset , Ġwe Ġbuild Ġa Ġset Ġof Ġ20 Ġtweet Ġpairs , Ġwith Ġone Ġtweet Ġeach Ġfrom Ġthe Ġ50 Ġtop - scoring Ġand Ġbottom - scoring Ġtweets Ġwritten Ġby Ġthe Ġuser . ĠWe Ġask Ġthe Ġhuman Ġeval u ator Ġto Ġchoose Ġwhich Ġtweet Ġsounds Ġmore Ġcharacteristic Ġof Ġthe Ġuser . ĠTo Ġvalidate Ġthat Ġthe ĠM Tur k Ġworker Ġknows Ġenough Ġabout Ġthe ĠTwitter Ġuser Ġto Ġpick Ġa Ġcharacteristic Ġtweet , Ġwe Ġuse Ġa Ġqualification Ġtest Ġcontaining Ġa Ġbasic Ġset Ġof Ġquestions Ġabout Ġthe ĠTwitter Ġuser . ĠWe Ġwere Ġunable Ġto Ġfind Ġequal Ġnumbers Ġof ĠTurk ers Ġfamiliar Ġwith Ġeach Ġsubject , Ġso Ġour Ġnumber Ġof Ġeval u ators Ġ$ n $ Ġdiffers Ġaccording Ġto Ġauthor . </s> User Ġstudy Ġ: :: ĠResults </s> Table ĠT AB REF 33 Ġdescribes Ġthe Ġresults Ġobtained Ġin Ġthe Ġuser Ġstudy : Ġthe Ġmean Ġand Ġstandard Ġdeviation Ġof Ġpercentage Ġof Ġanswers Ġin Ġagreement Ġwith Ġour Ġmodel , Ġthe Ġp - value , Ġand Ġthe Ġnumber Ġof ĠM Tur k Ġworkers Ġwho Ġcompleted Ġeach Ġtask . ĠWe Ġfind Ġthat Ġthe Ġaverage Ġagreement Ġof Ġhuman Ġeval u ators Ġwith Ġour Ġmodel Ġis Ġ70 . 40 % Ġover Ġall Ġ15 Ġusers Ġin Ġour Ġdataset . </s> For Ġeach Ġof Ġthe Ġ15 Ġcelebrities , Ġthe Ġhuman Ġeval u ators Ġagree Ġwith Ġour Ġmodel Ġabove Ġa Ġsignificance Ġlevel Ġof Ġ0 . 05 , Ġand Ġin Ġ13 Ġof Ġ15 Ġcases Ġabove Ġa Ġlevel Ġof Ġ$ 10 ^{ - 5 } $. ĠThis Ġmakes Ġclear Ġour Ġscores Ġare Ġmeasuring Ġwhat Ġwe Ġintend Ġto Ġbe Ġmeasuring . </s> M apping Ġwith Ġpopularity Ġ: :: ĠCor relation </s> We Ġnow Ġexplore Ġthe Ġrelationship Ġbetween Ġcharacterization Ġscore Ġand Ġtweet Ġpopularity Ġfor Ġeach Ġof Ġthe Ġusers Ġin Ġour Ġdataset . ĠTo Ġanalyze Ġthis Ġrelationship , Ġwe Ġperform Ġthe Ġfollowing Ġprocedure Ġfor Ġeach Ġauthor Ġ$ U $ : </s> Sort Ġall Ġtweets Ġwritten Ġby Ġ$ U $ Ġin Ġascending Ġorder Ġof Ġcharacterization Ġscore . </s> B ucket Ġthe Ġsorted Ġtweets Ġby Ġpercentile Ġscore Ġ( 1 Ġto Ġ100 ). </s> For Ġeach Ġbucket , Ġcalculate Ġthe Ġmean Ġnumber Ġof Ġlikes , Ġreplies , Ġand Ġret we ets . </s> Comp ute Ġthe Ġcorrelation Ġof Ġthis Ġmean Ġand Ġthe Ġpercentile Ġscore . </s> The ĠPearson Ġcorrelation Ġcoefficients Ġ( r - values ) Ġare Ġlisted Ġin ĠTable ĠT AB REF 39 . ĠThe Ġusers Ġat Ġthe Ġtop Ġ( Trump , ĠBach chan , ĠModi ) Ġall Ġdisplay Ġvery Ġstrong Ġpositive Ġcorrelation . ĠWe Ġname Ġthis Ġgroup ĠU PC Ġ( Users Ġwith ĠPositive ĠCor relation ), Ġand Ġthe Ġgroup Ġof Ġusers Ġat Ġthe Ġbottom Ġ( Grand e , ĠBieber , ĠKardashian ) Ġas ĠUNC Ġ( Users Ġwith ĠNegative ĠCor relation ). </s> M apping Ġwith Ġpopularity Ġ: :: ĠInterpret ation </s> For Ġusers Ġwith Ġpositive Ġcorrelation , Ġthe Ġhigher Ġthe Ġtweet 's Ġcharacterization Ġscore , Ġthe Ġmore Ġpopular Ġit Ġbecomes , Ġi . e . Ġthe Ġmore Ġlikes , Ġreplies , Ġand Ġret we ets Ġit Ġreceives . ĠIn Ġcontrast , Ġfor Ġusers Ġwith Ġnegative Ġcorrelation , Ġthe Ġhigher Ġthe Ġtweet Ġscore , Ġthe Ġless Ġpopular Ġit Ġbecomes . </s> Figure ĠFIG REF 41 Ġshows Ġthe Ġplot Ġof Ġlog Ġmean Ġnumber Ġof Ġlikes Ġper Ġbucket Ġvs . Ġtweet Ġscore Ġpercentile , Ġfor Ġusers Ġwith Ġthe Ġhighest Ġpositive Ġcorrelation . ĠSimilarly , ĠFigure ĠFIG REF 42 Ġshows Ġthe Ġplot Ġof Ġlog Ġmean Ġnumber Ġof Ġlikes Ġper Ġbucket Ġvs . Ġtweet Ġscore Ġpercentile , Ġfor Ġusers Ġwith Ġthe Ġhighest Ġnegative Ġcorrelation . </s> One Ġmay Ġquestion Ġwhether Ġthese Ġresults Ġare Ġdue Ġto Ġtemporal Ġeffects : Ġuser 's Ġpopularity Ġvary Ġwith Ġtime , Ġand Ġperhaps Ġthe Ġmodel 's Ġmore Ġcharacteristic Ġtweets Ġsimply Ġreflect Ġperiods Ġof Ġauthors hip . ĠFigures ĠFIG REF 41 Ġand ĠFIG REF 42 Ġdispro ve Ġthis Ġhypothesis . ĠHere Ġthe Ġcolor Ġof Ġeach Ġpoint Ġdenotes Ġthe Ġyear Ġfor Ġwhich Ġmost Ġtweets Ġare Ġpresent Ġin Ġthe Ġcorresponding Ġbucket . ĠSince Ġthe Ġdistribution Ġof Ġcolors Ġover Ġtime Ġis Ġnot Ġclustered , Ġwe Ġinfer Ġthat Ġthe Ġobserved Ġresult Ġis Ġnot Ġan Ġartifact Ġof Ġtemporal Ġeffects . ĠIn Ġboth Ġcases , Ġthere Ġis Ġa Ġstrong Ġtrend Ġin Ġtweet Ġpopularity Ġbased Ġon Ġtweet Ġscore . ĠWe Ġnote Ġthat Ġthe Ġplots Ġare Ġpresented Ġon Ġthe Ġlog Ġscale , Ġmeaning Ġthe Ġtrends Ġhere Ġare Ġexponential . </s> M apping Ġwith Ġpopularity Ġ: :: ĠQual itative ĠAnalysis </s> We Ġpresent Ġexamples Ġof Ġthe Ġmost Ġand Ġleast Ġcharacteristic Ġtweets Ġfor Ġcelebrities Ġfrom Ġthree Ġcategories , Ġalong Ġwith Ġtheir Ġcorresponding Ġcharacterization Ġscores Ġcomputed Ġusing ĠApproach ĠSEC REF 29 .