CNN 
EXTRACTIVE
The performance on discriminating between offensive (OFF) and non-offensive (NOT) posts is reported in Table TABREF18 . We can see that all systems perform significantly better than chance, with the neural models being substantially better than the SVM. The CNN outperforms the RNN model, achieving a macro-F1 score of 0.80. The CNN system achieved higher performance in this experiment compared to the BiLSTM, with a macro-F1 score of 0.69. All systems performed better at identifying target and threats (TIN) than untargeted offenses (UNT).
1902.09666
Introduction </s> Off ensive Ġcontent Ġhas Ġbecome Ġpervasive Ġin Ġsocial Ġmedia Ġand Ġa Ġreason Ġof Ġconcern Ġfor Ġgovernment Ġorganizations , Ġonline Ġcommunities , Ġand Ġsocial Ġmedia Ġplatforms . ĠOne Ġof Ġthe Ġmost Ġcommon Ġstrategies Ġto Ġtackle Ġthe Ġproblem Ġis Ġto Ġtrain Ġsystems Ġcapable Ġof Ġrecognizing Ġoffensive Ġcontent , Ġwhich Ġthen Ġcan Ġbe Ġdeleted Ġor Ġset Ġaside Ġfor Ġhuman Ġmoderation . ĠIn Ġthe Ġlast Ġfew Ġyears , Ġthere Ġhave Ġbeen Ġseveral Ġstudies Ġpublished Ġon Ġthe Ġapplication Ġof Ġcomputational Ġmethods Ġto Ġdeal Ġwith Ġthis Ġproblem . ĠMost Ġprior Ġwork Ġfocuses Ġon Ġa Ġdifferent Ġaspect Ġof Ġoffensive Ġlanguage Ġsuch Ġas Ġabusive Ġlanguage ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, Ġ( cy ber -) agg ression ĠB IB REF 2 Ġ, Ġ( cy ber -) bull ying ĠB IB REF 3 Ġ, ĠB IB REF 4 Ġ, Ġtoxic Ġcomments ĠIN LINE FORM 0 Ġ, Ġhate Ġspeech ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ, ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġ, ĠB IB REF 10 Ġ, Ġand Ġoffensive Ġlanguage ĠB IB REF 11 Ġ. ĠPrior Ġwork Ġhas Ġfocused Ġon Ġthese Ġaspects Ġof Ġoffensive Ġlanguage Ġin ĠTwitter ĠB IB REF 3 Ġ, ĠB IB REF 7 Ġ, ĠB IB REF 8 Ġ, ĠB IB REF 11 Ġ, ĠWikipedia Ġcomments , Ġand ĠFacebook Ġposts ĠB IB REF 2 Ġ. </s> Recently , ĠW ase em Ġet . Ġal . Ġ( ĠB IB REF 12 Ġ) Ġacknowledged Ġthe Ġsimilarities Ġamong Ġprior Ġwork Ġand Ġdiscussed Ġthe Ġneed Ġfor Ġa Ġtyp ology Ġthat Ġdifferent iates Ġbetween Ġwhether Ġthe Ġ( ab usive ) Ġlanguage Ġis Ġdirected Ġtowards Ġa Ġspecific Ġindividual Ġor Ġentity Ġor Ġtowards Ġa Ġgeneralized Ġgroup Ġand Ġwhether Ġthe Ġabusive Ġcontent Ġis Ġexplicit Ġor Ġimplicit . ĠW ieg and Ġet Ġal . Ġ( ĠB IB REF 11 Ġ) Ġfollowed Ġthis Ġtrend Ġas Ġwell Ġon ĠGerman Ġtweets . ĠIn Ġtheir Ġevaluation , Ġthey Ġhave Ġa Ġtask Ġto Ġdetect Ġoffensive Ġvs Ġnot Ġoffensive Ġtweets Ġand Ġa Ġsecond Ġtask Ġfor Ġdistinguishing Ġbetween Ġthe Ġoffensive Ġtweets Ġas Ġprof anity , Ġinsult , Ġor Ġabuse . ĠHowever , Ġno Ġprior Ġwork Ġhas Ġexplored Ġthe Ġtarget Ġof Ġthe Ġoffensive Ġlanguage , Ġwhich Ġis Ġimportant Ġin Ġmany Ġscenarios , Ġe . g ., Ġwhen Ġstudying Ġhate Ġspeech Ġwith Ġrespect Ġto Ġa Ġspecific Ġtarget . </s> Therefore , Ġwe Ġexpand Ġon Ġthese Ġideas Ġby Ġproposing Ġa Ġa Ġhierarchical Ġthree - level Ġannotation Ġmodel Ġthat Ġencompasses : </s> Using Ġthis Ġannotation Ġmodel , Ġwe Ġcreate Ġa Ġnew Ġlarge Ġpublicly Ġavailable Ġdataset Ġof ĠEnglish Ġtweets . ĠThe Ġkey Ġcontributions Ġof Ġthis Ġpaper Ġare Ġas Ġfollows : </s> Related ĠWork </s> Different Ġabusive Ġand Ġoffense Ġlanguage Ġidentification Ġsub - t asks Ġhave Ġbeen Ġexplored Ġin Ġthe Ġpast Ġfew Ġyears Ġincluding Ġaggression Ġidentification , Ġbullying Ġdetection , Ġhate Ġspeech , Ġtoxic Ġcomments , Ġand Ġoffensive Ġlanguage . </s> Agg ression Ġidentification : ĠThe ĠTR AC Ġshared Ġtask Ġon ĠAgg ression ĠIdentification ĠB IB REF 2 Ġprovided Ġparticipants Ġwith Ġa Ġdataset Ġcontaining Ġ15 , 000 Ġannot ated ĠFacebook Ġposts Ġand Ġcomments Ġin ĠEnglish Ġand ĠHindi Ġfor Ġtraining Ġand Ġvalidation . ĠFor Ġtesting , Ġtwo Ġdifferent Ġsets , Ġone Ġfrom ĠFacebook Ġand Ġone Ġfrom ĠTwitter Ġwere Ġprovided . ĠSystems Ġwere Ġtrained Ġto Ġdiscriminate Ġbetween Ġthree Ġclasses : Ġnon - aggressive , Ġcovert ly Ġaggressive , Ġand Ġovertly Ġaggressive . </s> Bull ying Ġdetection : ĠSeveral Ġstudies Ġhave Ġbeen Ġpublished Ġon Ġbullying Ġdetection . ĠOne Ġof Ġthem Ġis Ġthe Ġone Ġby Ġx u 2012 learning Ġwhich Ġapply Ġsentiment Ġanalysis Ġto Ġdetect Ġbullying Ġin Ġtweets . Ġx u 2012 learning Ġuse Ġtopic Ġmodels Ġto Ġto Ġidentify Ġrelevant Ġtopics Ġin Ġbullying . ĠAnother Ġrelated Ġstudy Ġis Ġthe Ġone Ġby Ġdad var 2013 impro ving Ġwhich Ġuse Ġuser - related Ġfeatures Ġsuch Ġas Ġthe Ġfrequency Ġof Ġprof anity Ġin Ġprevious Ġmessages Ġto Ġimprove Ġbullying Ġdetection . </s> H ate Ġspeech Ġidentification : ĠIt Ġis Ġperhaps Ġthe Ġmost Ġwidespread Ġabusive Ġlanguage Ġdetection Ġsub - task . ĠThere Ġhave Ġbeen Ġseveral Ġstudies Ġpublished Ġon Ġthis Ġsub - task Ġsuch Ġas Ġk w ok 2013 l ocate Ġand Ġdj ur ic 2015 hate Ġwho Ġbuild Ġa Ġbinary Ġclass ifier Ġto Ġdistinguish Ġbetween Ġ` clean ' Ġcomments Ġand Ġcomments Ġcontaining Ġhate Ġspeech Ġand Ġprof anity . ĠMore Ġrecently , ĠDavidson Ġet Ġal . Ġdavid son 2017 aut om ated Ġpresented Ġthe Ġhate Ġspeech Ġdetection Ġdataset Ġcontaining Ġover Ġ24 , 000 ĠEnglish Ġtweets Ġlabeled Ġas Ġnon Ġoffensive , Ġhate Ġspeech , Ġand Ġprof anity . </s> Off ensive Ġlanguage : ĠThe ĠGerm E val ĠB IB REF 11 Ġshared Ġtask Ġfocused Ġon ĠOffensive Ġlanguage Ġidentification Ġin ĠGerman Ġtweets . ĠA Ġdataset Ġof Ġover Ġ8 , 500 Ġannot ated Ġtweets Ġwas Ġprovided Ġfor Ġa Ġcourse - gr ained Ġbinary Ġclassification Ġtask Ġin Ġwhich Ġsystems Ġwere Ġtrained Ġto Ġdiscriminate Ġbetween Ġoffensive Ġand Ġnon - offensive Ġtweets Ġand Ġa Ġsecond Ġtask Ġwhere Ġthe Ġorganizers Ġbroke Ġdown Ġthe Ġoffensive Ġclass Ġinto Ġthree Ġclasses : Ġprof anity , Ġinsult , Ġand Ġabuse . </s> T oxic Ġcomments : ĠThe ĠToxic ĠComment ĠClassification ĠChallenge Ġwas Ġan Ġopen Ġcompetition Ġat ĠK agg le Ġwhich Ġprovided Ġparticipants Ġwith Ġcomments Ġfrom ĠWikipedia Ġlabeled Ġin Ġsix Ġclasses : Ġtoxic , Ġsevere Ġtoxic , Ġobscene , Ġthreat , Ġinsult , Ġidentity Ġhate . </s> While Ġeach Ġof Ġthese Ġsub - t asks Ġtackle Ġa Ġparticular Ġtype Ġof Ġabuse Ġor Ġoffense , Ġthey Ġshare Ġsimilar Ġproperties Ġand Ġthe Ġhierarchical Ġannotation Ġmodel Ġproposed Ġin Ġthis Ġpaper Ġaims Ġto Ġcapture Ġthis . ĠConsidering Ġthat , Ġfor Ġexample , Ġan Ġinsult Ġtargeted Ġat Ġan Ġindividual Ġis Ġcommonly Ġknown Ġas Ġcyber bull ing Ġand Ġthat Ġinsults Ġtargeted Ġat Ġa Ġgroup Ġare Ġknown Ġas Ġhate Ġspeech , Ġwe Ġpose Ġthat ĠOL ID 's Ġhierarchical Ġannotation Ġmodel Ġmakes Ġit Ġa Ġuseful Ġresource Ġfor Ġvarious Ġoffensive Ġlanguage Ġidentification Ġsub - t asks . </s> H ier arch ically ĠMod elling ĠOffensive ĠContent </s> In Ġthe ĠOL ID Ġdataset , Ġwe Ġuse Ġa Ġhierarchical Ġannotation Ġmodel Ġsplit Ġinto Ġthree Ġlevels Ġto Ġdistinguish Ġbetween Ġwhether Ġlanguage Ġis Ġoffensive Ġor Ġnot Ġ( A ), Ġand Ġtype Ġ( B ) Ġand Ġtarget Ġ( C ) Ġof Ġthe Ġoffensive Ġlanguage . ĠEach Ġlevel Ġis Ġdescribed Ġin Ġmore Ġdetail Ġin Ġthe Ġfollowing Ġsubsections Ġand Ġexamples Ġare Ġshown Ġin ĠTable ĠT AB REF 10 Ġ. </s> Level ĠA : ĠOffensive Ġlanguage ĠDetection </s> Level ĠA Ġdiscrim inates Ġbetween Ġoffensive Ġ( OFF ) Ġand Ġnon - offensive Ġ( NOT ) Ġtweets . </s> Not ĠOffensive Ġ( NOT ): ĠPosts Ġthat Ġdo Ġnot Ġcontain Ġoffense Ġor Ġprof anity ; </s> Off ensive Ġ( OFF ): ĠWe Ġlabel Ġa Ġpost Ġas Ġoffensive Ġif Ġit Ġcontains Ġany Ġform Ġof Ġnon - acceptable Ġlanguage Ġ( prof anity ) Ġor Ġa Ġtargeted Ġoffense , Ġwhich Ġcan Ġbe Ġveiled Ġor Ġdirect . ĠThis Ġcategory Ġincludes Ġinsults , Ġthreats , Ġand Ġposts Ġcontaining Ġprof ane Ġlanguage Ġor Ġswear Ġwords . </s> Level ĠB : ĠC ategor ization Ġof ĠOffensive ĠLanguage </s> Level ĠB Ġcategor izes Ġthe Ġtype Ġof Ġoffense Ġand Ġtwo Ġlabels Ġare Ġused : Ġtargeted Ġ( T IN ) Ġand Ġunt arget ed Ġ( INT ) Ġinsults Ġand Ġthreats . </s> Target ed ĠIns ult Ġ( T IN ): ĠPosts Ġwhich Ġcontain Ġan Ġinsult / threat Ġto Ġan Ġindividual , Ġgroup , Ġor Ġothers Ġ( see Ġnext Ġlayer ); </s> Unt arget ed Ġ( UN T ): ĠPosts Ġcontaining Ġnon - target ed Ġprof anity Ġand Ġswearing . ĠPosts Ġwith Ġgeneral Ġprof anity Ġare Ġnot Ġtargeted , Ġbut Ġthey Ġcontain Ġnon - acceptable Ġlanguage . </s> Level ĠC : ĠOffensive ĠLanguage ĠTarget ĠIdentification </s> Level ĠC Ġcategor izes Ġthe Ġtargets Ġof Ġinsults Ġand Ġthreats Ġas Ġindividual Ġ( IND ), Ġgroup Ġ( GR P ), Ġand Ġother Ġ( OTH ). </s> Individual Ġ( IND ): ĠPosts Ġtargeting Ġan Ġindividual . ĠIt Ġcan Ġbe Ġa Ġa Ġfamous Ġperson , Ġa Ġnamed Ġindividual Ġor Ġan Ġunnamed Ġparticipant Ġin Ġthe Ġconversation . ĠIns ults Ġand Ġthreats Ġtargeted Ġat Ġindividuals Ġare Ġoften Ġdefined Ġas Ġcyber bull ing . </s> Group Ġ( GR P ): ĠThe Ġtarget Ġof Ġthese Ġoffensive Ġposts Ġis Ġa Ġgroup Ġof Ġpeople Ġconsidered Ġas Ġa Ġunity Ġdue Ġto Ġthe Ġsame Ġethnicity , Ġgender Ġor Ġsexual Ġorientation , Ġpolitical Ġaffiliation , Ġreligious Ġbelief , Ġor Ġother Ġcommon Ġcharacteristic . ĠMany Ġof Ġthe Ġinsults Ġand Ġthreats Ġtargeted Ġat Ġa Ġgroup Ġcorrespond Ġto Ġwhat Ġis Ġcommonly Ġunderstood Ġas Ġhate Ġspeech . </s> Other Ġ( OTH ): ĠThe Ġtarget Ġof Ġthese Ġoffensive Ġposts Ġdoes Ġnot Ġbelong Ġto Ġany Ġof Ġthe Ġprevious Ġtwo Ġcategories Ġ( e . g . Ġan Ġorganization , Ġa Ġsituation , Ġan Ġevent , Ġor Ġan Ġissue ). </s> Data ĠCollection </s> The Ġdata Ġincluded Ġin ĠOL ID Ġhas Ġbeen Ġcollected Ġfrom ĠTwitter . ĠWe Ġretrieved Ġthe Ġdata Ġusing Ġthe ĠTwitter ĠAPI Ġby Ġsearching Ġfor Ġkeywords Ġand Ġconstruct ions Ġthat Ġare Ġoften Ġincluded Ġin Ġoffensive Ġmessages , Ġsuch Ġas Ġ` she Ġis ' Ġor Ġ` to : Bre it B art News '. ĠWe Ġcarried Ġout Ġa Ġfirst Ġround Ġof Ġtrial Ġannotation Ġof Ġ300 Ġinstances Ġwith Ġsix Ġexperts . ĠThe Ġgoal Ġof Ġthe Ġtrial Ġannotation Ġwas Ġto Ġ1 ) Ġevaluate Ġthe Ġproposed Ġtag set ; Ġ2 ) Ġevaluate Ġthe Ġdata Ġretrieval Ġmethod ; Ġand Ġ3 ) Ġcreate Ġa Ġgold Ġstandard Ġwith Ġinstances Ġthat Ġcould Ġbe Ġused Ġas Ġtest Ġquestions Ġin Ġthe Ġtraining Ġand Ġtest Ġsetting Ġannotation Ġwhich Ġwas Ġcarried Ġout Ġusing Ġcrowds ourcing . ĠThe Ġbreakdown Ġof Ġkeywords Ġand Ġtheir Ġoffensive Ġcontent Ġin Ġthe Ġtrial Ġdata Ġof Ġ300 Ġtweets Ġis Ġshown Ġin ĠTable ĠT AB REF 14 Ġ. ĠWe Ġincluded Ġa Ġleft Ġ(@ New York er ) Ġand Ġfar - right Ġ(@ Bre it B art News ) Ġnews Ġaccounts Ġbecause Ġthere Ġtends Ġto Ġbe Ġpolitical Ġoffense Ġin Ġthe Ġcomments . ĠOne Ġof Ġthe Ġbest Ġoffensive Ġkeywords Ġwas Ġtweets Ġthat Ġwere Ġflagged Ġas Ġnot Ġbeing Ġsafe Ġby Ġthe ĠTwitter Ġ` safe ' Ġfilter Ġ( the Ġ` -' Ġindicates Ġ` not Ġsafe '). ĠThe Ġvast Ġmajority Ġof Ġcontent Ġon ĠTwitter Ġis Ġnot Ġoffensive Ġso Ġwe Ġtried Ġdifferent Ġstrategies Ġto Ġkeep Ġa Ġreasonable Ġnumber Ġof Ġtweets Ġin Ġthe Ġoffensive Ġclass Ġamount ing Ġto Ġaround Ġ30 % Ġof Ġthe Ġdataset Ġincluding Ġexcluding Ġsome Ġkeywords Ġthat Ġwere Ġnot Ġhigh Ġin Ġoffensive Ġcontent Ġsuch Ġas Ġ` they Ġare ` Ġand Ġ` to : New York er `. ĠAlthough Ġ` he Ġis ' Ġis Ġlower Ġin Ġoffensive Ġcontent Ġwe Ġkept Ġit Ġas Ġa Ġkeyword Ġto Ġavoid Ġgender Ġbias . ĠIn Ġaddition Ġto Ġthe Ġkeywords Ġin Ġthe Ġtrial Ġset , Ġwe Ġsearched Ġfor Ġmore Ġpolitical Ġkeywords Ġwhich Ġtend Ġto Ġbe Ġhigher Ġin Ġoffensive Ġcontent , Ġand Ġsampled Ġour Ġdataset Ġsuch Ġthat Ġ50 % Ġof Ġthe Ġthe Ġtweets Ġcome Ġfrom Ġpolitical Ġkeywords Ġand Ġ50 % Ġcome Ġfrom Ġnon - political Ġkeywords . ĠIn Ġaddition Ġto Ġthe Ġkeywords Ġ` gun Ġcontrol ', Ġand Ġ` to : Bre itbart News ', Ġpolitical Ġkeywords Ġused Ġto Ġcollect Ġthese Ġtweets Ġare Ġ` MAG A ', Ġ` ant ifa ', Ġ` conservative ' Ġand Ġ` liberal '. ĠWe Ġcomputed ĠFl i ess ' ĠIN LINE FORM 0 Ġon Ġthe Ġtrial Ġset Ġfor Ġthe Ġfive Ġannot ators Ġon Ġ21 Ġof Ġthe Ġtweets . ĠIN LINE FORM 1 Ġis Ġ. 83 Ġfor ĠLayer ĠA Ġ( OFF Ġvs ĠNOT ) Ġindicating Ġhigh Ġagreement . ĠAs Ġto Ġnormal ization Ġand Ġanonym ization , Ġno Ġuser Ġmetadata Ġor ĠTwitter ĠIDs Ġhave Ġbeen Ġstored , Ġand ĠURLs Ġand ĠTwitter Ġmentions Ġhave Ġbeen Ġsubstituted Ġto Ġplace holders . ĠWe Ġfollow Ġprior Ġwork Ġin Ġrelated Ġareas Ġ( burn ap 2015 cy ber , d avid son 2017 aut om ated ) Ġand Ġannot ate Ġour Ġdata Ġusing Ġcrowds ourcing Ġusing Ġthe Ġplatform ĠFigure ĠEight . ĠWe Ġensure Ġdata Ġquality Ġby : Ġ1 ) Ġwe Ġonly Ġreceived Ġannotations Ġfrom Ġindividuals Ġwho Ġwere Ġexperienced Ġin Ġthe Ġplatform ; Ġand Ġ2 ) Ġwe Ġused Ġtest Ġquestions Ġto Ġdiscard Ġannotations Ġof Ġindividuals Ġwho Ġdid Ġnot Ġreach Ġa Ġcertain Ġthreshold . ĠEach Ġinstance Ġin Ġthe Ġdataset Ġwas Ġannot ated Ġby Ġmultiple Ġannot ators Ġand Ġinter - annot ator Ġagreement Ġhas Ġbeen Ġcalculated . ĠWe Ġfirst Ġacquired Ġtwo Ġannotations Ġfor Ġeach Ġinstance . ĠIn Ġcase Ġof Ġ100 % Ġagreement , Ġwe Ġconsidered Ġthese Ġas Ġacceptable Ġannotations , Ġand Ġin Ġcase Ġof Ġdisagreement , Ġwe Ġrequested Ġmore Ġannotations Ġuntil Ġthe Ġagreement Ġwas Ġabove Ġ66 %. ĠAfter Ġthe Ġcrowds ourcing Ġannotation , Ġwe Ġused Ġexpert Ġadjud ication Ġto Ġguarantee Ġthe Ġquality Ġof Ġthe Ġannotation . ĠThe Ġbreakdown Ġof Ġthe Ġdata Ġinto Ġtraining Ġand Ġtesting Ġfor Ġthe Ġlabels Ġfrom Ġeach Ġlevel Ġis Ġshown Ġin ĠTable ĠT AB REF 15 Ġ. </s> Exper iments Ġand ĠEvaluation </s> We Ġassess Ġour Ġdataset Ġusing Ġtraditional Ġand Ġdeep Ġlearning Ġmethods . ĠOur Ġsimplest Ġmodel Ġis Ġa Ġlinear ĠS VM Ġtrained Ġon Ġword Ġun ig rams . ĠSV Ms Ġhave Ġproduced Ġstate - of - the - art Ġresults Ġfor Ġmany Ġtext Ġclassification Ġtasks ĠB IB REF 13 Ġ. ĠWe Ġalso Ġtrain Ġa Ġbid irection al ĠLong ĠShort - Term - Memory Ġ( Bi L ST M ) Ġmodel , Ġwhich Ġwe Ġadapted Ġfrom Ġthe Ġsentiment Ġanalysis Ġsystem Ġof Ġsentiment System , ras ool i 2018 cross Ġand Ġaltered Ġto Ġpredict Ġoffensive Ġlabels Ġinstead . ĠIt Ġconsists Ġof Ġ( 1 ) Ġan Ġinput Ġembed ding Ġlayer , Ġ( 2 ) Ġa Ġbid irection al ĠL ST M Ġlayer , Ġ( 3 ) Ġan Ġaverage Ġpool ing Ġlayer Ġof Ġinput Ġfeatures . ĠThe Ġconc aten ation Ġof Ġthe ĠL ST M 's Ġand Ġaverage Ġpool Ġlayer Ġis Ġpassed Ġthrough Ġa Ġdense Ġlayer Ġand Ġthe Ġoutput Ġis Ġpassed Ġthrough Ġa Ġsoft max Ġfunction . ĠWe Ġset Ġtwo Ġinput Ġchannels Ġfor Ġthe Ġinput Ġembed ding Ġlayers : Ġpre - trained ĠFast Text Ġembed d ings ĠB IB REF 14 Ġ, Ġas Ġwell Ġas Ġupd atable Ġembed d ings Ġlearned Ġby Ġthe Ġmodel Ġduring Ġtraining . ĠFinally , Ġwe Ġalso Ġapply Ġa ĠConv olution al ĠNeural ĠNetwork Ġ( CNN ) Ġmodel Ġbased Ġon Ġthe Ġarchitecture Ġof ĠB IB REF 15 Ġ, Ġusing Ġthe Ġsame Ġmulti - channel Ġinputs Ġas Ġthe Ġabove ĠBi L ST M . </s> Our Ġmodels Ġare Ġtrained Ġon Ġthe Ġtraining Ġdata , Ġand Ġevaluated Ġby Ġpredicting Ġthe Ġlabels Ġfor Ġthe Ġheld - out Ġtest Ġset . ĠThe Ġdistribution Ġis Ġdescribed Ġin ĠTable ĠT AB REF 15 Ġ. ĠWe Ġevaluate Ġand Ġcompare Ġthe Ġmodels Ġusing Ġthe Ġmacro - aver aged ĠF 1 - score Ġas Ġthe Ġlabel Ġdistribution Ġis Ġhighly Ġim balanced . ĠPer - class ĠPrecision Ġ( P ), ĠRecall Ġ( R ), Ġand ĠF 1 - score Ġ( F 1 ), Ġalso Ġwith Ġother Ġaveraged Ġmetrics Ġare Ġalso Ġreported . ĠThe Ġmodels Ġare Ġcompared Ġagainst Ġbas elines Ġof Ġpredicting Ġall Ġlabels Ġas Ġthe Ġmajority Ġor Ġminority Ġclasses . </s> Off ensive ĠLanguage ĠDetection </s> The Ġperformance Ġon Ġdiscriminating Ġbetween Ġoffensive Ġ( OFF ) Ġand Ġnon - offensive Ġ( NOT ) Ġposts Ġis Ġreported Ġin ĠTable ĠT AB REF 18 Ġ. ĠWe Ġcan Ġsee Ġthat Ġall Ġsystems Ġperform Ġsignificantly Ġbetter Ġthan Ġchance , Ġwith Ġthe Ġneural Ġmodels Ġbeing Ġsubstantially Ġbetter Ġthan Ġthe ĠS VM . ĠThe ĠCNN Ġoutper forms Ġthe ĠR NN Ġmodel , Ġachieving Ġa Ġmacro - F 1 Ġscore Ġof Ġ0 . 80 . </s> C ategor ization Ġof ĠOffensive ĠLanguage </s> In Ġthis Ġexperiment , Ġthe Ġtwo Ġsystems Ġwere Ġtrained Ġto Ġdiscriminate Ġbetween Ġinsults Ġand Ġthreats Ġ( T IN ) Ġand Ġunt arget ed Ġ( UN T ) Ġoffenses , Ġwhich Ġgenerally Ġrefer Ġto Ġprof anity . ĠThe Ġresults Ġare Ġshown Ġin ĠTable ĠT AB REF 19 Ġ. </s> The ĠCNN Ġsystem Ġachieved Ġhigher Ġperformance Ġin Ġthis Ġexperiment Ġcompared Ġto Ġthe ĠBi L ST M , Ġwith Ġa Ġmacro - F 1 Ġscore Ġof Ġ0 . 69 . ĠAll Ġsystems Ġperformed Ġbetter Ġat Ġidentifying Ġtarget Ġand Ġthreats Ġ( T IN ) Ġthan Ġunt arget ed Ġoffenses Ġ( UN T ). </s> Off ensive ĠLanguage ĠTarget ĠIdentification </s> The Ġresults Ġof Ġthe Ġoffensive Ġtarget Ġidentification Ġexperiment Ġare Ġreported Ġin ĠTable ĠT AB REF 20 Ġ. ĠHere Ġthe Ġsystems Ġwere Ġtrained Ġto Ġdistinguish Ġbetween Ġthree Ġtargets : Ġa Ġgroup Ġ( GR P ), Ġan Ġindividual Ġ( IND ), Ġor Ġothers Ġ( OTH ). ĠAll Ġthree Ġmodels Ġachieved Ġsimilar Ġresults Ġfar Ġsurpass ing Ġthe Ġrandom Ġbas elines , Ġwith Ġa Ġslight Ġperformance Ġedge Ġfor Ġthe Ġneural Ġmodels . </s> The Ġperformance Ġof Ġall Ġsystems Ġfor Ġthe ĠO TH Ġclass Ġis Ġ0 . ĠThis Ġpoor Ġperformances Ġcan Ġbe Ġexplained Ġby Ġtwo Ġmain Ġfactors . ĠFirst , Ġunlike Ġthe Ġtwo Ġother Ġclasses , ĠO TH Ġis Ġa Ġheter ogeneous Ġcollection Ġof Ġtargets . ĠIt Ġincludes Ġoffensive Ġtweets Ġtargeted Ġat Ġorganizations , Ġsituations , Ġevents , Ġetc . Ġmaking Ġit Ġmore Ġchallenging Ġfor Ġsystems Ġto Ġlearn Ġdiscrim in ative Ġproperties Ġof Ġthis Ġclass . ĠSecond , Ġthis Ġclass Ġcontains Ġfewer Ġtraining Ġinstances Ġthan Ġthe Ġother Ġtwo . ĠThere Ġare Ġonly Ġ395 Ġinstances Ġin ĠO TH , Ġand Ġ1 , 075 Ġin ĠGR P , Ġand Ġ2 , 407 Ġin ĠIND . </s> Conclusion Ġand ĠFuture ĠWork </s> This Ġpaper Ġpresents ĠOL ID , Ġa Ġnew Ġdataset Ġwith Ġannotation Ġof Ġtype Ġand Ġtarget Ġof Ġoffensive Ġlanguage . ĠOL ID Ġis Ġthe Ġofficial Ġdataset Ġof Ġthe Ġshared Ġtask ĠSem E val Ġ2019 ĠTask Ġ6 : ĠIdent ifying Ġand ĠC ategor izing ĠOffensive ĠLanguage Ġin ĠSocial ĠMedia Ġ( Off ens E val ) ĠB IB REF 16 Ġ. ĠIn ĠOff ens E val , Ġeach Ġannotation Ġlevel Ġin ĠOL ID Ġis Ġan Ġindependent Ġsub - task . ĠThe Ġdataset Ġcontains Ġ14 , 100 Ġtweets Ġand Ġis Ġreleased Ġfreely Ġto Ġthe Ġresearch Ġcommunity . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , Ġthis Ġis Ġthe Ġfirst Ġdataset Ġto Ġcontain Ġannotation Ġof Ġtype Ġand Ġtarget Ġof Ġoffenses Ġin Ġsocial Ġmedia Ġand Ġit Ġopens Ġseveral Ġnew Ġavenues Ġfor Ġresearch Ġin Ġthis Ġarea . ĠWe Ġpresent Ġbaseline Ġexperiments Ġusing ĠSV Ms Ġand Ġneural Ġnetworks Ġto Ġidentify Ġthe Ġoffensive Ġtweets , Ġdiscriminate Ġbetween Ġinsults , Ġthreats , Ġand Ġprof anity , Ġand Ġfinally Ġto Ġidentify Ġthe Ġtarget Ġof Ġthe Ġoffensive Ġmessages . ĠThe Ġresults Ġshow Ġthat Ġthis Ġis Ġa Ġchallenging Ġtask . ĠA ĠCNN - based Ġsentence Ġclass ifier Ġachieved Ġthe Ġbest Ġresults Ġin Ġall Ġthree Ġsub - t asks . </s> In Ġfuture Ġwork , Ġwe Ġwould Ġlike Ġto Ġmake Ġa Ġcross - cor p us Ġcomparison Ġof ĠOL ID Ġand Ġdatasets Ġannot ated Ġfor Ġsimilar Ġtasks Ġsuch Ġas Ġaggression Ġidentification ĠB IB REF 2 Ġand Ġhate Ġspeech Ġdetection ĠB IB REF 8 Ġ. ĠThis Ġcomparison Ġis , Ġhowever , Ġfar Ġfrom Ġtrivial Ġas Ġthe Ġannotation Ġof ĠOL ID Ġis Ġdifferent . </s> Acknowled gments </s> The Ġresearch Ġpresented Ġin Ġthis Ġpaper Ġwas Ġpartially Ġsupported Ġby Ġan ĠER AS Ġfellowship Ġawarded Ġby Ġthe ĠUniversity Ġof ĠWolver ham pton .
Introduction Offensive content has become pervasive in social media and a reason of concern for government organizations, online communities, and social media platforms. One of the most common strategies to tackle the problem is to train systems capable of recognizing offensive content, which then can be deleted or set aside for human moderation. In the last few years, there have been several studies published on the application of computational methods to deal with this problem. Most prior work focuses on a different aspect of offensive language such as abusive language BIBREF0 , BIBREF1 , (cyber-)aggression BIBREF2 , (cyber-)bullying BIBREF3 , BIBREF4 , toxic comments INLINEFORM0 , hate speech BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , and offensive language BIBREF11 . Prior work has focused on these aspects of offensive language in Twitter BIBREF3 , BIBREF7 , BIBREF8 , BIBREF11 , Wikipedia comments, and Facebook posts BIBREF2 . Recently, Waseem et. al. ( BIBREF12 ) acknowledged the similarities among prior work and discussed the need for a typology that differentiates between whether the (abusive) language is directed towards a specific individual or entity or towards a generalized group and whether the abusive content is explicit or implicit. Wiegand et al. ( BIBREF11 ) followed this trend as well on German tweets. In their evaluation, they have a task to detect offensive vs not offensive tweets and a second task for distinguishing between the offensive tweets as profanity, insult, or abuse. However, no prior work has explored the target of the offensive language, which is important in many scenarios, e.g., when studying hate speech with respect to a specific target. Therefore, we expand on these ideas by proposing a a hierarchical three-level annotation model that encompasses: Using this annotation model, we create a new large publicly available dataset of English tweets. The key contributions of this paper are as follows: Related Work Different abusive and offense language identification sub-tasks have been explored in the past few years including aggression identification, bullying detection, hate speech, toxic comments, and offensive language. Aggression identification: The TRAC shared task on Aggression Identification BIBREF2 provided participants with a dataset containing 15,000 annotated Facebook posts and comments in English and Hindi for training and validation. For testing, two different sets, one from Facebook and one from Twitter were provided. Systems were trained to discriminate between three classes: non-aggressive, covertly aggressive, and overtly aggressive. Bullying detection: Several studies have been published on bullying detection. One of them is the one by xu2012learning which apply sentiment analysis to detect bullying in tweets. xu2012learning use topic models to to identify relevant topics in bullying. Another related study is the one by dadvar2013improving which use user-related features such as the frequency of profanity in previous messages to improve bullying detection. Hate speech identification: It is perhaps the most widespread abusive language detection sub-task. There have been several studies published on this sub-task such as kwok2013locate and djuric2015hate who build a binary classifier to distinguish between `clean' comments and comments containing hate speech and profanity. More recently, Davidson et al. davidson2017automated presented the hate speech detection dataset containing over 24,000 English tweets labeled as non offensive, hate speech, and profanity. Offensive language: The GermEval BIBREF11 shared task focused on Offensive language identification in German tweets. A dataset of over 8,500 annotated tweets was provided for a course-grained binary classification task in which systems were trained to discriminate between offensive and non-offensive tweets and a second task where the organizers broke down the offensive class into three classes: profanity, insult, and abuse. Toxic comments: The Toxic Comment Classification Challenge was an open competition at Kaggle which provided participants with comments from Wikipedia labeled in six classes: toxic, severe toxic, obscene, threat, insult, identity hate. While each of these sub-tasks tackle a particular type of abuse or offense, they share similar properties and the hierarchical annotation model proposed in this paper aims to capture this. Considering that, for example, an insult targeted at an individual is commonly known as cyberbulling and that insults targeted at a group are known as hate speech, we pose that OLID's hierarchical annotation model makes it a useful resource for various offensive language identification sub-tasks. Hierarchically Modelling Offensive Content In the OLID dataset, we use a hierarchical annotation model split into three levels to distinguish between whether language is offensive or not (A), and type (B) and target (C) of the offensive language. Each level is described in more detail in the following subsections and examples are shown in Table TABREF10 . Level A: Offensive language Detection Level A discriminates between offensive (OFF) and non-offensive (NOT) tweets. Not Offensive (NOT): Posts that do not contain offense or profanity; Offensive (OFF): We label a post as offensive if it contains any form of non-acceptable language (profanity) or a targeted offense, which can be veiled or direct. This category includes insults, threats, and posts containing profane language or swear words. Level B: Categorization of Offensive Language Level B categorizes the type of offense and two labels are used: targeted (TIN) and untargeted (INT) insults and threats. Targeted Insult (TIN): Posts which contain an insult/threat to an individual, group, or others (see next layer); Untargeted (UNT): Posts containing non-targeted profanity and swearing. Posts with general profanity are not targeted, but they contain non-acceptable language. Level C: Offensive Language Target Identification Level C categorizes the targets of insults and threats as individual (IND), group (GRP), and other (OTH). Individual (IND): Posts targeting an individual. It can be a a famous person, a named individual or an unnamed participant in the conversation. Insults and threats targeted at individuals are often defined as cyberbulling. Group (GRP): The target of these offensive posts is a group of people considered as a unity due to the same ethnicity, gender or sexual orientation, political affiliation, religious belief, or other common characteristic. Many of the insults and threats targeted at a group correspond to what is commonly understood as hate speech. Other (OTH): The target of these offensive posts does not belong to any of the previous two categories (e.g. an organization, a situation, an event, or an issue). Data Collection The data included in OLID has been collected from Twitter. We retrieved the data using the Twitter API by searching for keywords and constructions that are often included in offensive messages, such as `she is' or `to:BreitBartNews'. We carried out a first round of trial annotation of 300 instances with six experts. The goal of the trial annotation was to 1) evaluate the proposed tagset; 2) evaluate the data retrieval method; and 3) create a gold standard with instances that could be used as test questions in the training and test setting annotation which was carried out using crowdsourcing. The breakdown of keywords and their offensive content in the trial data of 300 tweets is shown in Table TABREF14 . We included a left (@NewYorker) and far-right (@BreitBartNews) news accounts because there tends to be political offense in the comments. One of the best offensive keywords was tweets that were flagged as not being safe by the Twitter `safe' filter (the `-' indicates `not safe'). The vast majority of content on Twitter is not offensive so we tried different strategies to keep a reasonable number of tweets in the offensive class amounting to around 30% of the dataset including excluding some keywords that were not high in offensive content such as `they are` and `to:NewYorker`. Although `he is' is lower in offensive content we kept it as a keyword to avoid gender bias. In addition to the keywords in the trial set, we searched for more political keywords which tend to be higher in offensive content, and sampled our dataset such that 50% of the the tweets come from political keywords and 50% come from non-political keywords. In addition to the keywords `gun control', and `to:BreitbartNews', political keywords used to collect these tweets are `MAGA', `antifa', `conservative' and `liberal'. We computed Fliess' INLINEFORM0 on the trial set for the five annotators on 21 of the tweets. INLINEFORM1 is .83 for Layer A (OFF vs NOT) indicating high agreement. As to normalization and anonymization, no user metadata or Twitter IDs have been stored, and URLs and Twitter mentions have been substituted to placeholders. We follow prior work in related areas (burnap2015cyber,davidson2017automated) and annotate our data using crowdsourcing using the platform Figure Eight. We ensure data quality by: 1) we only received annotations from individuals who were experienced in the platform; and 2) we used test questions to discard annotations of individuals who did not reach a certain threshold. Each instance in the dataset was annotated by multiple annotators and inter-annotator agreement has been calculated. We first acquired two annotations for each instance. In case of 100% agreement, we considered these as acceptable annotations, and in case of disagreement, we requested more annotations until the agreement was above 66%. After the crowdsourcing annotation, we used expert adjudication to guarantee the quality of the annotation. The breakdown of the data into training and testing for the labels from each level is shown in Table TABREF15 . Experiments and Evaluation We assess our dataset using traditional and deep learning methods. Our simplest model is a linear SVM trained on word unigrams. SVMs have produced state-of-the-art results for many text classification tasks BIBREF13 . We also train a bidirectional Long Short-Term-Memory (BiLSTM) model, which we adapted from the sentiment analysis system of sentimentSystem,rasooli2018cross and altered to predict offensive labels instead. It consists of (1) an input embedding layer, (2) a bidirectional LSTM layer, (3) an average pooling layer of input features. The concatenation of the LSTM's and average pool layer is passed through a dense layer and the output is passed through a softmax function. We set two input channels for the input embedding layers: pre-trained FastText embeddings BIBREF14 , as well as updatable embeddings learned by the model during training. Finally, we also apply a Convolutional Neural Network (CNN) model based on the architecture of BIBREF15 , using the same multi-channel inputs as the above BiLSTM. Our models are trained on the training data, and evaluated by predicting the labels for the held-out test set. The distribution is described in Table TABREF15 . We evaluate and compare the models using the macro-averaged F1-score as the label distribution is highly imbalanced. Per-class Precision (P), Recall (R), and F1-score (F1), also with other averaged metrics are also reported. The models are compared against baselines of predicting all labels as the majority or minority classes. Offensive Language Detection The performance on discriminating between offensive (OFF) and non-offensive (NOT) posts is reported in Table TABREF18 . We can see that all systems perform significantly better than chance, with the neural models being substantially better than the SVM. The CNN outperforms the RNN model, achieving a macro-F1 score of 0.80. Categorization of Offensive Language In this experiment, the two systems were trained to discriminate between insults and threats (TIN) and untargeted (UNT) offenses, which generally refer to profanity. The results are shown in Table TABREF19 . The CNN system achieved higher performance in this experiment compared to the BiLSTM, with a macro-F1 score of 0.69. All systems performed better at identifying target and threats (TIN) than untargeted offenses (UNT). Offensive Language Target Identification The results of the offensive target identification experiment are reported in Table TABREF20 . Here the systems were trained to distinguish between three targets: a group (GRP), an individual (IND), or others (OTH). All three models achieved similar results far surpassing the random baselines, with a slight performance edge for the neural models. The performance of all systems for the OTH class is 0. This poor performances can be explained by two main factors. First, unlike the two other classes, OTH is a heterogeneous collection of targets. It includes offensive tweets targeted at organizations, situations, events, etc. making it more challenging for systems to learn discriminative properties of this class. Second, this class contains fewer training instances than the other two. There are only 395 instances in OTH, and 1,075 in GRP, and 2,407 in IND. Conclusion and Future Work This paper presents OLID, a new dataset with annotation of type and target of offensive language. OLID is the official dataset of the shared task SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) BIBREF16 . In OffensEval, each annotation level in OLID is an independent sub-task. The dataset contains 14,100 tweets and is released freely to the research community. To the best of our knowledge, this is the first dataset to contain annotation of type and target of offenses in social media and it opens several new avenues for research in this area. We present baseline experiments using SVMs and neural networks to identify the offensive tweets, discriminate between insults, threats, and profanity, and finally to identify the target of the offensive messages. The results show that this is a challenging task. A CNN-based sentence classifier achieved the best results in all three sub-tasks. In future work, we would like to make a cross-corpus comparison of OLID and datasets annotated for similar tasks such as aggression identification BIBREF2 and hate speech detection BIBREF8 . This comparison is, however, far from trivial as the annotation of OLID is different. Acknowledgments The research presented in this paper was partially supported by an ERAS fellowship awarded by the University of Wolverhampton.
What is the best performing model?
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What Ġis Ġthe Ġbest Ġperforming Ġmodel ?
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<s> What Ġis Ġthe Ġbest Ġperforming Ġmodel ? </s> Introduction </s> Off ensive Ġcontent Ġhas Ġbecome Ġpervasive Ġin Ġsocial Ġmedia Ġand Ġa Ġreason Ġof Ġconcern Ġfor Ġgovernment Ġorganizations , Ġonline Ġcommunities , Ġand Ġsocial Ġmedia Ġplatforms . ĠOne Ġof Ġthe Ġmost Ġcommon Ġstrategies Ġto Ġtackle Ġthe Ġproblem Ġis Ġto Ġtrain Ġsystems Ġcapable Ġof Ġrecognizing Ġoffensive Ġcontent , Ġwhich Ġthen Ġcan Ġbe Ġdeleted Ġor Ġset Ġaside Ġfor Ġhuman Ġmoderation . ĠIn Ġthe Ġlast Ġfew Ġyears , Ġthere Ġhave Ġbeen Ġseveral Ġstudies Ġpublished Ġon Ġthe Ġapplication Ġof Ġcomputational Ġmethods Ġto Ġdeal Ġwith Ġthis Ġproblem . ĠMost Ġprior Ġwork Ġfocuses Ġon Ġa Ġdifferent Ġaspect Ġof Ġoffensive Ġlanguage Ġsuch Ġas Ġabusive Ġlanguage ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, Ġ( cy ber -) agg ression ĠB IB REF 2 Ġ, Ġ( cy ber -) bull ying ĠB IB REF 3 Ġ, ĠB IB REF 4 Ġ, Ġtoxic Ġcomments ĠIN LINE FORM 0 Ġ, Ġhate Ġspeech ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ, ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġ, ĠB IB REF 10 Ġ, Ġand Ġoffensive Ġlanguage ĠB IB REF 11 Ġ. ĠPrior Ġwork Ġhas Ġfocused Ġon Ġthese Ġaspects Ġof Ġoffensive Ġlanguage Ġin ĠTwitter ĠB IB REF 3 Ġ, ĠB IB REF 7 Ġ, ĠB IB REF 8 Ġ, ĠB IB REF 11 Ġ, ĠWikipedia Ġcomments , Ġand ĠFacebook Ġposts ĠB IB REF 2 Ġ. </s> Recently , ĠW ase em Ġet . Ġal . Ġ( ĠB IB REF 12 Ġ) Ġacknowledged Ġthe Ġsimilarities Ġamong Ġprior Ġwork Ġand Ġdiscussed Ġthe Ġneed Ġfor Ġa Ġtyp ology Ġthat Ġdifferent iates Ġbetween Ġwhether Ġthe Ġ( ab usive ) Ġlanguage Ġis Ġdirected Ġtowards Ġa Ġspecific Ġindividual Ġor Ġentity Ġor Ġtowards Ġa Ġgeneralized Ġgroup Ġand Ġwhether Ġthe Ġabusive Ġcontent Ġis Ġexplicit Ġor Ġimplicit . ĠW ieg and Ġet Ġal . Ġ( ĠB IB REF 11 Ġ) Ġfollowed Ġthis Ġtrend Ġas Ġwell Ġon ĠGerman Ġtweets . ĠIn Ġtheir Ġevaluation , Ġthey Ġhave Ġa Ġtask Ġto Ġdetect Ġoffensive Ġvs Ġnot Ġoffensive Ġtweets Ġand Ġa Ġsecond Ġtask Ġfor Ġdistinguishing Ġbetween Ġthe Ġoffensive Ġtweets Ġas Ġprof anity , Ġinsult , Ġor Ġabuse . ĠHowever , Ġno Ġprior Ġwork Ġhas Ġexplored Ġthe Ġtarget Ġof Ġthe Ġoffensive Ġlanguage , Ġwhich Ġis Ġimportant Ġin Ġmany Ġscenarios , Ġe . g ., Ġwhen Ġstudying Ġhate Ġspeech Ġwith Ġrespect Ġto Ġa Ġspecific Ġtarget . </s> Therefore , Ġwe Ġexpand Ġon Ġthese Ġideas Ġby Ġproposing Ġa Ġa Ġhierarchical Ġthree - level Ġannotation Ġmodel Ġthat Ġencompasses : </s> Using Ġthis Ġannotation Ġmodel , Ġwe Ġcreate Ġa Ġnew Ġlarge Ġpublicly Ġavailable Ġdataset Ġof ĠEnglish Ġtweets . ĠThe Ġkey Ġcontributions Ġof Ġthis Ġpaper Ġare Ġas Ġfollows : </s> Related ĠWork </s> Different Ġabusive Ġand Ġoffense Ġlanguage Ġidentification Ġsub - t asks Ġhave Ġbeen Ġexplored Ġin Ġthe Ġpast Ġfew Ġyears Ġincluding Ġaggression Ġidentification , Ġbullying Ġdetection , Ġhate Ġspeech , Ġtoxic Ġcomments , Ġand Ġoffensive Ġlanguage . </s> Agg ression Ġidentification : ĠThe ĠTR AC Ġshared Ġtask Ġon ĠAgg ression ĠIdentification ĠB IB REF 2 Ġprovided Ġparticipants Ġwith Ġa Ġdataset Ġcontaining Ġ15 , 000 Ġannot ated ĠFacebook Ġposts Ġand Ġcomments Ġin ĠEnglish Ġand ĠHindi Ġfor Ġtraining Ġand Ġvalidation . ĠFor Ġtesting , Ġtwo Ġdifferent Ġsets , Ġone Ġfrom ĠFacebook Ġand Ġone Ġfrom ĠTwitter Ġwere Ġprovided . ĠSystems Ġwere Ġtrained Ġto Ġdiscriminate Ġbetween Ġthree Ġclasses : Ġnon - aggressive , Ġcovert ly Ġaggressive , Ġand Ġovertly Ġaggressive . </s> Bull ying Ġdetection : ĠSeveral Ġstudies Ġhave Ġbeen Ġpublished Ġon Ġbullying Ġdetection . ĠOne Ġof Ġthem Ġis Ġthe Ġone Ġby Ġx u 2012 learning Ġwhich Ġapply Ġsentiment Ġanalysis Ġto Ġdetect Ġbullying Ġin Ġtweets . Ġx u 2012 learning Ġuse Ġtopic Ġmodels Ġto Ġto Ġidentify Ġrelevant Ġtopics Ġin Ġbullying . ĠAnother Ġrelated Ġstudy Ġis Ġthe Ġone Ġby Ġdad var 2013 impro ving Ġwhich Ġuse Ġuser - related Ġfeatures Ġsuch Ġas Ġthe Ġfrequency Ġof Ġprof anity Ġin Ġprevious Ġmessages Ġto Ġimprove Ġbullying Ġdetection . </s> H ate Ġspeech Ġidentification : ĠIt Ġis Ġperhaps Ġthe Ġmost Ġwidespread Ġabusive Ġlanguage Ġdetection Ġsub - task . ĠThere Ġhave Ġbeen Ġseveral Ġstudies Ġpublished Ġon Ġthis Ġsub - task Ġsuch Ġas Ġk w ok 2013 l ocate Ġand Ġdj ur ic 2015 hate Ġwho Ġbuild Ġa Ġbinary Ġclass ifier Ġto Ġdistinguish Ġbetween Ġ` clean ' Ġcomments Ġand Ġcomments Ġcontaining Ġhate Ġspeech Ġand Ġprof anity . ĠMore Ġrecently , ĠDavidson Ġet Ġal . Ġdavid son 2017 aut om ated Ġpresented Ġthe Ġhate Ġspeech Ġdetection Ġdataset Ġcontaining Ġover Ġ24 , 000 ĠEnglish Ġtweets Ġlabeled Ġas Ġnon Ġoffensive , Ġhate Ġspeech , Ġand Ġprof anity . </s> Off ensive Ġlanguage : ĠThe ĠGerm E val ĠB IB REF 11 Ġshared Ġtask Ġfocused Ġon ĠOffensive Ġlanguage Ġidentification Ġin ĠGerman Ġtweets . ĠA Ġdataset Ġof Ġover Ġ8 , 500 Ġannot ated Ġtweets Ġwas Ġprovided Ġfor Ġa Ġcourse - gr ained Ġbinary Ġclassification Ġtask Ġin Ġwhich Ġsystems Ġwere Ġtrained Ġto Ġdiscriminate Ġbetween Ġoffensive Ġand Ġnon - offensive Ġtweets Ġand Ġa Ġsecond Ġtask Ġwhere Ġthe Ġorganizers Ġbroke Ġdown Ġthe Ġoffensive Ġclass Ġinto Ġthree Ġclasses : Ġprof anity , Ġinsult , Ġand Ġabuse . </s> T oxic Ġcomments : ĠThe ĠToxic ĠComment ĠClassification ĠChallenge Ġwas Ġan Ġopen Ġcompetition Ġat ĠK agg le Ġwhich Ġprovided Ġparticipants Ġwith Ġcomments Ġfrom ĠWikipedia Ġlabeled Ġin Ġsix Ġclasses : Ġtoxic , Ġsevere Ġtoxic , Ġobscene , Ġthreat , Ġinsult , Ġidentity Ġhate . </s> While Ġeach Ġof Ġthese Ġsub - t asks Ġtackle Ġa Ġparticular Ġtype Ġof Ġabuse Ġor Ġoffense , Ġthey Ġshare Ġsimilar Ġproperties Ġand Ġthe Ġhierarchical Ġannotation Ġmodel Ġproposed Ġin Ġthis Ġpaper Ġaims Ġto Ġcapture Ġthis . ĠConsidering Ġthat , Ġfor Ġexample , Ġan Ġinsult Ġtargeted Ġat Ġan Ġindividual Ġis Ġcommonly Ġknown Ġas Ġcyber bull ing Ġand Ġthat Ġinsults Ġtargeted Ġat Ġa Ġgroup Ġare Ġknown Ġas Ġhate Ġspeech , Ġwe Ġpose Ġthat ĠOL ID 's Ġhierarchical Ġannotation Ġmodel Ġmakes Ġit Ġa Ġuseful Ġresource Ġfor Ġvarious Ġoffensive Ġlanguage Ġidentification Ġsub - t asks . </s> H ier arch ically ĠMod elling ĠOffensive ĠContent </s> In Ġthe ĠOL ID Ġdataset , Ġwe Ġuse Ġa Ġhierarchical Ġannotation Ġmodel Ġsplit Ġinto Ġthree Ġlevels Ġto Ġdistinguish Ġbetween Ġwhether Ġlanguage Ġis Ġoffensive Ġor Ġnot Ġ( A ), Ġand Ġtype Ġ( B ) Ġand Ġtarget Ġ( C ) Ġof Ġthe Ġoffensive Ġlanguage . ĠEach Ġlevel Ġis Ġdescribed Ġin Ġmore Ġdetail Ġin Ġthe Ġfollowing Ġsubsections Ġand Ġexamples Ġare Ġshown Ġin ĠTable ĠT AB REF 10 Ġ. </s> Level ĠA : ĠOffensive Ġlanguage ĠDetection </s> Level ĠA Ġdiscrim inates Ġbetween Ġoffensive Ġ( OFF ) Ġand Ġnon - offensive Ġ( NOT ) Ġtweets . </s> Not ĠOffensive Ġ( NOT ): ĠPosts Ġthat Ġdo Ġnot Ġcontain Ġoffense Ġor Ġprof anity ; </s> Off ensive Ġ( OFF ): ĠWe Ġlabel Ġa Ġpost Ġas Ġoffensive Ġif Ġit Ġcontains Ġany Ġform Ġof Ġnon - acceptable Ġlanguage Ġ( prof anity ) Ġor Ġa Ġtargeted Ġoffense , Ġwhich Ġcan Ġbe Ġveiled Ġor Ġdirect . ĠThis Ġcategory Ġincludes Ġinsults , Ġthreats , Ġand Ġposts Ġcontaining Ġprof ane Ġlanguage Ġor Ġswear Ġwords . </s> Level ĠB : ĠC ategor ization Ġof ĠOffensive ĠLanguage </s> Level ĠB Ġcategor izes Ġthe Ġtype Ġof Ġoffense Ġand Ġtwo Ġlabels Ġare Ġused : Ġtargeted Ġ( T IN ) Ġand Ġunt arget ed Ġ( INT ) Ġinsults Ġand Ġthreats . </s> Target ed ĠIns ult Ġ( T IN ): ĠPosts Ġwhich Ġcontain Ġan Ġinsult / threat Ġto Ġan Ġindividual , Ġgroup , Ġor Ġothers Ġ( see Ġnext Ġlayer ); </s> Unt arget ed Ġ( UN T ): ĠPosts Ġcontaining Ġnon - target ed Ġprof anity Ġand Ġswearing . ĠPosts Ġwith Ġgeneral Ġprof anity Ġare Ġnot Ġtargeted , Ġbut Ġthey Ġcontain Ġnon - acceptable Ġlanguage . </s> Level ĠC : ĠOffensive ĠLanguage ĠTarget ĠIdentification </s> Level ĠC Ġcategor izes Ġthe Ġtargets Ġof Ġinsults Ġand Ġthreats Ġas Ġindividual Ġ( IND ), Ġgroup Ġ( GR P ), Ġand Ġother Ġ( OTH ). </s> Individual Ġ( IND ): ĠPosts Ġtargeting Ġan Ġindividual . ĠIt Ġcan Ġbe Ġa Ġa Ġfamous Ġperson , Ġa Ġnamed Ġindividual Ġor Ġan Ġunnamed Ġparticipant Ġin Ġthe Ġconversation . ĠIns ults Ġand Ġthreats Ġtargeted Ġat Ġindividuals Ġare Ġoften Ġdefined Ġas Ġcyber bull ing . </s> Group Ġ( GR P ): ĠThe Ġtarget Ġof Ġthese Ġoffensive Ġposts Ġis Ġa Ġgroup Ġof Ġpeople Ġconsidered Ġas Ġa Ġunity Ġdue Ġto Ġthe Ġsame Ġethnicity , Ġgender Ġor Ġsexual Ġorientation , Ġpolitical Ġaffiliation , Ġreligious Ġbelief , Ġor Ġother Ġcommon Ġcharacteristic . ĠMany Ġof Ġthe Ġinsults Ġand Ġthreats Ġtargeted Ġat Ġa Ġgroup Ġcorrespond Ġto Ġwhat Ġis Ġcommonly Ġunderstood Ġas Ġhate Ġspeech . </s> Other Ġ( OTH ): ĠThe Ġtarget Ġof Ġthese Ġoffensive Ġposts Ġdoes Ġnot Ġbelong Ġto Ġany Ġof Ġthe Ġprevious Ġtwo Ġcategories Ġ( e . g . Ġan Ġorganization , Ġa Ġsituation , Ġan Ġevent , Ġor Ġan Ġissue ). </s> Data ĠCollection </s> The Ġdata Ġincluded Ġin ĠOL ID Ġhas Ġbeen Ġcollected Ġfrom ĠTwitter . ĠWe Ġretrieved Ġthe Ġdata Ġusing Ġthe ĠTwitter ĠAPI Ġby Ġsearching Ġfor Ġkeywords Ġand Ġconstruct ions Ġthat Ġare Ġoften Ġincluded Ġin Ġoffensive Ġmessages , Ġsuch Ġas Ġ` she Ġis ' Ġor Ġ` to : Bre it B art News '. ĠWe Ġcarried Ġout Ġa Ġfirst Ġround Ġof Ġtrial Ġannotation Ġof Ġ300 Ġinstances Ġwith Ġsix Ġexperts . ĠThe Ġgoal Ġof Ġthe Ġtrial Ġannotation Ġwas Ġto Ġ1 ) Ġevaluate Ġthe Ġproposed Ġtag set ; Ġ2 ) Ġevaluate Ġthe Ġdata Ġretrieval Ġmethod ; Ġand Ġ3 ) Ġcreate Ġa Ġgold Ġstandard Ġwith Ġinstances Ġthat Ġcould Ġbe Ġused Ġas Ġtest Ġquestions Ġin Ġthe Ġtraining Ġand Ġtest Ġsetting Ġannotation Ġwhich Ġwas Ġcarried Ġout Ġusing Ġcrowds ourcing . ĠThe Ġbreakdown Ġof Ġkeywords Ġand Ġtheir Ġoffensive Ġcontent Ġin Ġthe Ġtrial Ġdata Ġof Ġ300 Ġtweets Ġis Ġshown Ġin ĠTable ĠT AB REF 14 Ġ. ĠWe Ġincluded Ġa Ġleft Ġ(@ New York er ) Ġand Ġfar - right Ġ(@ Bre it B art News ) Ġnews Ġaccounts Ġbecause Ġthere Ġtends Ġto Ġbe Ġpolitical Ġoffense Ġin Ġthe Ġcomments . ĠOne Ġof Ġthe Ġbest Ġoffensive Ġkeywords Ġwas Ġtweets Ġthat Ġwere Ġflagged Ġas Ġnot Ġbeing Ġsafe Ġby Ġthe ĠTwitter Ġ` safe ' Ġfilter Ġ( the Ġ` -' Ġindicates Ġ` not Ġsafe '). ĠThe Ġvast Ġmajority Ġof Ġcontent Ġon ĠTwitter Ġis Ġnot Ġoffensive Ġso Ġwe Ġtried Ġdifferent Ġstrategies Ġto Ġkeep Ġa Ġreasonable Ġnumber Ġof Ġtweets Ġin Ġthe Ġoffensive Ġclass Ġamount ing Ġto Ġaround Ġ30 % Ġof Ġthe Ġdataset Ġincluding Ġexcluding Ġsome Ġkeywords Ġthat Ġwere Ġnot Ġhigh Ġin Ġoffensive Ġcontent Ġsuch Ġas Ġ` they Ġare ` Ġand Ġ` to : New York er `. ĠAlthough Ġ` he Ġis ' Ġis Ġlower Ġin Ġoffensive Ġcontent Ġwe Ġkept Ġit Ġas Ġa Ġkeyword Ġto Ġavoid Ġgender Ġbias . ĠIn Ġaddition Ġto Ġthe Ġkeywords Ġin Ġthe Ġtrial Ġset , Ġwe Ġsearched Ġfor Ġmore Ġpolitical Ġkeywords Ġwhich Ġtend Ġto Ġbe Ġhigher Ġin Ġoffensive Ġcontent , Ġand Ġsampled Ġour Ġdataset Ġsuch Ġthat Ġ50 % Ġof Ġthe Ġthe Ġtweets Ġcome Ġfrom Ġpolitical Ġkeywords Ġand Ġ50 % Ġcome Ġfrom Ġnon - political Ġkeywords . ĠIn Ġaddition Ġto Ġthe Ġkeywords Ġ` gun Ġcontrol ', Ġand Ġ` to : Bre itbart News ', Ġpolitical Ġkeywords Ġused Ġto Ġcollect Ġthese Ġtweets Ġare Ġ` MAG A ', Ġ` ant ifa ', Ġ` conservative ' Ġand Ġ` liberal '. ĠWe Ġcomputed ĠFl i ess ' ĠIN LINE FORM 0 Ġon Ġthe Ġtrial Ġset Ġfor Ġthe Ġfive Ġannot ators Ġon Ġ21 Ġof Ġthe Ġtweets . ĠIN LINE FORM 1 Ġis Ġ. 83 Ġfor ĠLayer ĠA Ġ( OFF Ġvs ĠNOT ) Ġindicating Ġhigh Ġagreement . ĠAs Ġto Ġnormal ization Ġand Ġanonym ization , Ġno Ġuser Ġmetadata Ġor ĠTwitter ĠIDs Ġhave Ġbeen Ġstored , Ġand ĠURLs Ġand ĠTwitter Ġmentions Ġhave Ġbeen Ġsubstituted Ġto Ġplace holders . ĠWe Ġfollow Ġprior Ġwork Ġin Ġrelated Ġareas Ġ( burn ap 2015 cy ber , d avid son 2017 aut om ated ) Ġand Ġannot ate Ġour Ġdata Ġusing Ġcrowds ourcing Ġusing Ġthe Ġplatform ĠFigure ĠEight . ĠWe Ġensure Ġdata Ġquality Ġby : Ġ1 ) Ġwe Ġonly Ġreceived Ġannotations Ġfrom Ġindividuals Ġwho Ġwere Ġexperienced Ġin Ġthe Ġplatform ; Ġand Ġ2 ) Ġwe Ġused Ġtest Ġquestions Ġto Ġdiscard Ġannotations Ġof Ġindividuals Ġwho Ġdid Ġnot Ġreach Ġa Ġcertain Ġthreshold . ĠEach Ġinstance Ġin Ġthe Ġdataset Ġwas Ġannot ated Ġby Ġmultiple Ġannot ators Ġand Ġinter - annot ator Ġagreement Ġhas Ġbeen Ġcalculated . ĠWe Ġfirst Ġacquired Ġtwo Ġannotations Ġfor Ġeach Ġinstance . ĠIn Ġcase Ġof Ġ100 % Ġagreement , Ġwe Ġconsidered Ġthese Ġas Ġacceptable Ġannotations , Ġand Ġin Ġcase Ġof Ġdisagreement , Ġwe Ġrequested Ġmore Ġannotations Ġuntil Ġthe Ġagreement Ġwas Ġabove Ġ66 %. ĠAfter Ġthe Ġcrowds ourcing Ġannotation , Ġwe Ġused Ġexpert Ġadjud ication Ġto Ġguarantee Ġthe Ġquality Ġof Ġthe Ġannotation . ĠThe Ġbreakdown Ġof Ġthe Ġdata Ġinto Ġtraining Ġand Ġtesting Ġfor Ġthe Ġlabels Ġfrom Ġeach Ġlevel Ġis Ġshown Ġin ĠTable ĠT AB REF 15 Ġ. </s> Exper iments Ġand ĠEvaluation </s> We Ġassess Ġour Ġdataset Ġusing Ġtraditional Ġand Ġdeep Ġlearning Ġmethods . ĠOur Ġsimplest Ġmodel Ġis Ġa Ġlinear ĠS VM Ġtrained Ġon Ġword Ġun ig rams . ĠSV Ms Ġhave Ġproduced Ġstate - of - the - art Ġresults Ġfor Ġmany Ġtext Ġclassification Ġtasks ĠB IB REF 13 Ġ. ĠWe Ġalso Ġtrain Ġa Ġbid irection al ĠLong ĠShort - Term - Memory Ġ( Bi L ST M ) Ġmodel , Ġwhich Ġwe Ġadapted Ġfrom Ġthe Ġsentiment Ġanalysis Ġsystem Ġof Ġsentiment System , ras ool i 2018 cross Ġand Ġaltered Ġto Ġpredict Ġoffensive Ġlabels Ġinstead . ĠIt Ġconsists Ġof Ġ( 1 ) Ġan Ġinput Ġembed ding Ġlayer , Ġ( 2 ) Ġa Ġbid irection al ĠL ST M Ġlayer , Ġ( 3 ) Ġan Ġaverage Ġpool ing Ġlayer Ġof Ġinput Ġfeatures . ĠThe Ġconc aten ation Ġof Ġthe ĠL ST M 's Ġand Ġaverage Ġpool Ġlayer Ġis Ġpassed Ġthrough Ġa Ġdense Ġlayer Ġand Ġthe Ġoutput Ġis Ġpassed Ġthrough Ġa Ġsoft max Ġfunction . ĠWe Ġset Ġtwo Ġinput Ġchannels Ġfor Ġthe Ġinput Ġembed ding Ġlayers : Ġpre - trained ĠFast Text Ġembed d ings ĠB IB REF 14 Ġ, Ġas Ġwell Ġas Ġupd atable Ġembed d ings Ġlearned Ġby Ġthe Ġmodel Ġduring Ġtraining . ĠFinally , Ġwe Ġalso Ġapply Ġa ĠConv olution al ĠNeural ĠNetwork Ġ( CNN ) Ġmodel Ġbased Ġon Ġthe Ġarchitecture Ġof ĠB IB REF 15 Ġ, Ġusing Ġthe Ġsame Ġmulti - channel Ġinputs Ġas Ġthe Ġabove ĠBi L ST M . </s> Our Ġmodels Ġare Ġtrained Ġon Ġthe Ġtraining Ġdata , Ġand Ġevaluated Ġby Ġpredicting Ġthe Ġlabels Ġfor Ġthe Ġheld - out Ġtest Ġset . ĠThe Ġdistribution Ġis Ġdescribed Ġin ĠTable ĠT AB REF 15 Ġ. ĠWe Ġevaluate Ġand Ġcompare Ġthe Ġmodels Ġusing Ġthe Ġmacro - aver aged ĠF 1 - score Ġas Ġthe Ġlabel Ġdistribution Ġis Ġhighly Ġim balanced . ĠPer - class ĠPrecision Ġ( P ), ĠRecall Ġ( R ), Ġand ĠF 1 - score Ġ( F 1 ), Ġalso Ġwith Ġother Ġaveraged Ġmetrics Ġare Ġalso Ġreported . ĠThe Ġmodels Ġare Ġcompared Ġagainst Ġbas elines Ġof Ġpredicting Ġall Ġlabels Ġas Ġthe Ġmajority Ġor Ġminority Ġclasses . </s> Off ensive ĠLanguage ĠDetection </s> The Ġperformance Ġon Ġdiscriminating Ġbetween Ġoffensive Ġ( OFF ) Ġand Ġnon - offensive Ġ( NOT ) Ġposts Ġis Ġreported Ġin ĠTable ĠT AB REF 18 Ġ. ĠWe Ġcan Ġsee Ġthat Ġall Ġsystems Ġperform Ġsignificantly Ġbetter Ġthan Ġchance , Ġwith Ġthe Ġneural Ġmodels Ġbeing Ġsubstantially Ġbetter Ġthan Ġthe ĠS VM . ĠThe ĠCNN Ġoutper forms Ġthe ĠR NN Ġmodel , Ġachieving Ġa Ġmacro - F 1 Ġscore Ġof Ġ0 . 80 . </s> C ategor ization Ġof ĠOffensive ĠLanguage </s> In Ġthis Ġexperiment , Ġthe Ġtwo Ġsystems Ġwere Ġtrained Ġto Ġdiscriminate Ġbetween Ġinsults Ġand Ġthreats Ġ( T IN ) Ġand Ġunt arget ed Ġ( UN T ) Ġoffenses , Ġwhich Ġgenerally Ġrefer Ġto Ġprof anity . ĠThe Ġresults Ġare Ġshown Ġin ĠTable ĠT AB REF 19 Ġ. </s> The ĠCNN Ġsystem Ġachieved Ġhigher Ġperformance Ġin Ġthis Ġexperiment Ġcompared Ġto Ġthe ĠBi L ST M , Ġwith Ġa Ġmacro - F 1 Ġscore Ġof Ġ0 . 69 . ĠAll Ġsystems Ġperformed Ġbetter Ġat Ġidentifying Ġtarget Ġand Ġthreats Ġ( T IN ) Ġthan Ġunt arget ed Ġoffenses Ġ( UN T ). </s> Off ensive ĠLanguage ĠTarget ĠIdentification </s> The Ġresults Ġof Ġthe Ġoffensive Ġtarget Ġidentification Ġexperiment Ġare Ġreported Ġin ĠTable ĠT AB REF 20 Ġ. ĠHere Ġthe Ġsystems Ġwere Ġtrained Ġto Ġdistinguish Ġbetween Ġthree Ġtargets : Ġa Ġgroup Ġ( GR P ), Ġan Ġindividual Ġ( IND ), Ġor Ġothers Ġ( OTH ). ĠAll Ġthree Ġmodels Ġachieved Ġsimilar Ġresults Ġfar Ġsurpass ing Ġthe Ġrandom Ġbas elines , Ġwith Ġa Ġslight Ġperformance Ġedge Ġfor Ġthe Ġneural Ġmodels . </s> The Ġperformance Ġof Ġall Ġsystems Ġfor Ġthe ĠO TH Ġclass Ġis Ġ0 . ĠThis Ġpoor Ġperformances Ġcan Ġbe Ġexplained Ġby Ġtwo Ġmain Ġfactors . ĠFirst , Ġunlike Ġthe Ġtwo Ġother Ġclasses , ĠO TH Ġis Ġa Ġheter ogeneous Ġcollection Ġof Ġtargets . ĠIt Ġincludes Ġoffensive Ġtweets Ġtargeted Ġat Ġorganizations , Ġsituations , Ġevents , Ġetc . Ġmaking Ġit Ġmore Ġchallenging Ġfor Ġsystems Ġto Ġlearn Ġdiscrim in ative Ġproperties Ġof Ġthis Ġclass . ĠSecond , Ġthis Ġclass Ġcontains Ġfewer Ġtraining Ġinstances Ġthan Ġthe Ġother Ġtwo . ĠThere Ġare Ġonly Ġ395 Ġinstances Ġin ĠO TH , Ġand Ġ1 , 075 Ġin ĠGR P , Ġand Ġ2 , 407 Ġin ĠIND . </s> Conclusion Ġand ĠFuture ĠWork </s> This Ġpaper Ġpresents ĠOL ID , Ġa Ġnew Ġdataset Ġwith Ġannotation Ġof Ġtype Ġand Ġtarget Ġof Ġoffensive Ġlanguage . ĠOL ID Ġis Ġthe Ġofficial Ġdataset Ġof Ġthe Ġshared Ġtask ĠSem E val Ġ2019 ĠTask Ġ6 : ĠIdent ifying Ġand ĠC ategor izing ĠOffensive ĠLanguage Ġin ĠSocial ĠMedia Ġ( Off ens E val ) ĠB IB REF 16 Ġ. ĠIn ĠOff ens E val , Ġeach Ġannotation Ġlevel Ġin ĠOL ID Ġis Ġan Ġindependent Ġsub - task . ĠThe Ġdataset Ġcontains Ġ14 , 100 Ġtweets Ġand Ġis Ġreleased Ġfreely Ġto Ġthe Ġresearch Ġcommunity . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , Ġthis Ġis Ġthe Ġfirst Ġdataset Ġto Ġcontain Ġannotation Ġof Ġtype Ġand Ġtarget Ġof Ġoffenses Ġin Ġsocial Ġmedia Ġand Ġit Ġopens Ġseveral Ġnew Ġavenues Ġfor Ġresearch Ġin Ġthis Ġarea . ĠWe Ġpresent Ġbaseline Ġexperiments Ġusing ĠSV Ms Ġand Ġneural Ġnetworks Ġto Ġidentify Ġthe Ġoffensive Ġtweets , Ġdiscriminate Ġbetween Ġinsults , Ġthreats , Ġand Ġprof anity , Ġand Ġfinally Ġto Ġidentify Ġthe Ġtarget Ġof Ġthe Ġoffensive Ġmessages . ĠThe Ġresults Ġshow Ġthat Ġthis Ġis Ġa Ġchallenging Ġtask . ĠA ĠCNN - based Ġsentence Ġclass ifier Ġachieved Ġthe Ġbest Ġresults Ġin Ġall Ġthree Ġsub - t asks . </s> In Ġfuture Ġwork , Ġwe Ġwould Ġlike Ġto Ġmake Ġa Ġcross - cor p us Ġcomparison Ġof ĠOL ID Ġand Ġdatasets Ġannot ated Ġfor Ġsimilar Ġtasks Ġsuch Ġas Ġaggression Ġidentification ĠB IB REF 2 Ġand Ġhate Ġspeech Ġdetection ĠB IB REF 8 Ġ. ĠThis Ġcomparison Ġis , Ġhowever , Ġfar Ġfrom Ġtrivial Ġas Ġthe Ġannotation Ġof ĠOL ID Ġis Ġdifferent . </s> Acknowled gments </s> The Ġresearch Ġpresented Ġin Ġthis Ġpaper Ġwas Ġpartially Ġsupported Ġby Ġan ĠER AS Ġfellowship Ġawarded Ġby Ġthe ĠUniversity Ġof ĠWolver ham pton .