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FLOAT SELECTED: Figure 1: NATO-Warsaw intra-bloc differences in votes and speeches.
1802.00396
Introduction </s> The Ġpolarization Ġof Ġactors ' Ġexpressed Ġpreferences Ġis Ġa Ġfundamental Ġconcern Ġfor Ġstudies Ġof Ġlegislatures , Ġcourt Ġsystems , Ġand Ġinternational Ġpolitics ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ. ĠBecause Ġpreferences Ġare Ġunob serv able , Ġscholars Ġmust Ġlook Ġfor Ġsignals Ġin Ġthe Ġempirical Ġworld . ĠRecent Ġprogress Ġhas Ġbeen Ġmade Ġin Ġparliamentary Ġand Ġcourt Ġsettings Ġthrough Ġthe Ġemployment Ġof Ġtextual Ġdata ĠB IB REF 4 Ġand Ġvotes Ġand Ġtexts Ġin Ġtandem ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ. ĠMany Ġof Ġthese Ġadvances Ġrely Ġon Ġspatial , Ġscaling , Ġand Ġitem - response Ġtype Ġmodels Ġthat Ġare Ġintuitive Ġfor Ġsettings Ġwhere Ġa Ġsmall Ġnumber Ġof Ġparties Ġor Ġideological Ġdivisions Ġinfluence Ġoutcomes . ĠThis Ġis Ġless Ġintuitive Ġfor Ġthe Ġstudy Ġof Ġstate Ġpreferences , Ġbecause Ġinternational Ġrelations Ġis Ġmarked Ġby Ġmultiple Ġdimensions Ġthat Ġspan Ġideological , Ġeconomic , Ġand Ġsecurity Ġconcerns , Ġamong Ġothers ĠB IB REF 0 Ġ. </s> This Ġpaper Ġintroduces Ġa Ġnew Ġapproach Ġto Ġestimate Ġpreference Ġpolarization Ġin Ġmult id imensional Ġsettings Ġusing Ġtexts Ġand Ġvotes . ĠFirst , Ġa Ġdistribution al Ġrepresentation Ġof Ġtextual Ġdata Ġis Ġutilized Ġto Ġretain Ġqualities Ġof Ġhuman Ġspeech Ġthat Ġare Ġotherwise Ġdiscarded Ġby Ġa Ġbag - of - words Ġapproach . ĠSecond , Ġcommunity Ġdetection Ġin Ġmultiple x Ġnetworks Ġis Ġused Ġto Ġuncover Ġpreference Ġaffinity Ġbloc s Ġacross Ġmultiple Ġlayers Ġof Ġvotes Ġand Ġspeeches . ĠJust Ġas Ġscaling Ġand Ġspatial Ġmodels Ġattempt Ġto Ġexplain Ġvariance Ġalong Ġone Ġor Ġa Ġfew Ġreduced Ġdimensions , Ġour Ġapproach Ġidentifies Ġdensely Ġconnected Ġcommunities Ġbased Ġon Ġpreference Ġsimilarity Ġthat Ġare Ġimportant Ġfor Ġexplaining Ġvariations Ġin Ġobserved Ġoutcomes . </s> We Ġillustrate Ġthe Ġutility Ġof Ġthis Ġapproach Ġwith Ġan Ġempirical Ġtest Ġof Ġa Ġcore Ġhypothesis Ġin ĠInternational ĠRelations Ġ( IR ): Ġmilitar ized Ġconflict Ġis Ġless Ġlikely Ġbetween Ġstates Ġwith Ġmore Ġsimilar Ġpreferences ĠB IB REF 7 Ġ. ĠSpecifically , Ġwe Ġextend Ġa Ġrecently Ġpublished Ġnetwork Ġmodel Ġof Ġconflict Ġonset . ĠB IB REF 8 Ġutilize Ġtemporal Ġexponential Ġrandom Ġgraph Ġmodels Ġto Ġinfer Ġthe Ġrelationship Ġbetween Ġconflict Ġonset Ġand Ġa Ġbattery Ġof Ġpredict ors , Ġone Ġof Ġwhich Ġis Ġaffinity Ġcommunities Ġlocated Ġvia Ġspectral Ġclust ering Ġon Ġa Ġgraph Ġof ĠUN Ġvotes . ĠThis Ġcovari ate Ġprovides Ġa Ġnatural Ġcomparison Ġto Ġexamine Ġwhether Ġour Ġproposed Ġclust ering Ġapproach Ġcan Ġimprove Ġour Ġability Ġto Ġmodel Ġconflict Ġonset , Ġas Ġmeasured Ġby Ġout - of - sample Ġpredictive Ġaccuracy . ĠWe Ġfind Ġthat Ġmultiple x Ġclusters Ġbased Ġon Ġcountry Ġspeeches Ġand Ġvotes ĠâĢĵ Ġwhich Ġwe Ġrefer Ġto Ġas Ġaffinity Ġbloc s ĠâĢĵ Ġoutper form Ġclusters Ġbased Ġon Ġvotes Ġor Ġspeeches Ġalone . ĠThe Ġproposed Ġframework Ġenables ĠIR Ġscholars Ġto Ġbetter Ġexplain Ġbehavioral Ġoutcomes Ġin Ġinternational Ġpolitics , Ġand Ġwill Ġbe Ġof Ġuse Ġto Ġany Ġscholar Ġinterested Ġin Ġthe Ġmeasurement Ġof Ġpreference Ġpolarization Ġin Ġmult id imensional Ġsettings . </s> Political ĠPolar ization : ĠMeasure ments Ġand ĠModels </s> P olar ization Ġin ĠIR Ġis Ġdefined Ġas ĠâĢ ľ the Ġdegree Ġto Ġwhich Ġthe Ġforeign Ġpolicies Ġof Ġnations Ġwithin Ġa Ġsingle Ġcluster Ġare Ġsimilar Ġto Ġeach Ġother , Ġand Ġthe Ġdegree Ġto Ġwhich Ġthe Ġforeign Ġpolicies Ġof Ġnations Ġin Ġdifferent Ġclusters Ġare Ġdiss imilar " ĠB IB REF 9 Ġ. ĠTherefore , Ġoperational izing Ġa Ġconcept Ġof Ġpreference Ġpolarization Ġbroadly Ġinvolves Ġtwo Ġsteps : Ġan Ġapproach Ġto Ġestimate Ġpreferences Ġfrom Ġavailable Ġdata Ġon Ġstates ' Ġobservable Ġbehavior ; Ġand Ġa Ġmethod Ġof Ġdetecting Ġdistinct Ġcommunities Ġof Ġnations , Ġsuch Ġthat Ġnations Ġbelonging Ġto Ġthe Ġsame Ġcommunity Ġshare Ġsimilar Ġpreferences , Ġand Ġnations Ġbelonging Ġto Ġdifferent Ġcommunities Ġhave Ġdiss imilar Ġpreferences . </s> UN Ġvotes Ġand Ġspeeches </s> The Ġmost Ġwidely Ġused Ġsource Ġfor Ġder iving Ġpreferences Ġin ĠIR Ġis ĠUN Ġroll Ġcall Ġdata ĠB IB REF 10 Ġ. ĠVoting Ġbehavior Ġrepresent Ġa Ġvaluable Ġsource Ġof Ġrevealed Ġpreference Ġinformation , Ġcomparable Ġacross Ġstates Ġand Ġover Ġtime . ĠHowever , ĠUN Ġroll Ġcall Ġvotes Ġtend Ġto Ġbe Ġa Ġweak Ġsignal Ġof Ġunderlying Ġpreferences Ġin Ġcases Ġwhere Ġstates Ġvote Ġfor Ġceremonial Ġpurposes , Ġare Ġconstrained Ġby Ġagenda - setting Ġpower Ġdynamics , Ġor Ġvote Ġas Ġcohorts Ġto Ġmaximize Ġtheir Ġimpact Ġwithin Ġthe ĠUN , Ġsuch Ġas Ġwith Ġregional Ġbloc s ĠB IB REF 11 Ġ. </s> Similar Ġlimitations Ġexist Ġin Ġthe Ġstudy Ġof Ġpolarization Ġin Ġnational Ġlegislatures Ġwhere Ġactors ' Ġvotes Ġseldom Ġdiver ge Ġfrom Ġparty Ġlines . ĠIn Ġresponse , Ġan Ġemerging Ġliterature Ġturns Ġto Ġactors ' Ġspeeches Ġto Ġbetter Ġcapture Ġexpressed Ġpositions Ġand Ġto Ġmeasure Ġpolarization Ġof Ġthese Ġpositions ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ. ĠThe Ġemployment Ġof Ġtext Ġdata Ġfor Ġthe Ġmeasurement Ġof Ġstate Ġpreferences Ġin Ġworld Ġpolitics Ġis Ġintuitive , Ġbecause Ġoutcomes Ġare Ġa Ġfunction Ġof Ġmultiple Ġissue Ġdimensions , Ġsuch Ġas Ġtopics Ġranging Ġfrom Ġhuman Ġrights Ġto Ġnuclear Ġproliferation Ġpolicy . ĠIn Ġparticular , Ġstates ' Ġannual Ġaddresses Ġin Ġthe ĠUN ĠGeneral ĠDebate Ġ( GD ) Ġprovide Ġa Ġvaluable Ġsource Ġof Ġdata Ġon Ġstate Ġpreferences . ĠGovernments Ġuse Ġtheir Ġannual ĠGD Ġspeeches Ġto Ġdiscuss Ġtheir Ġpositions Ġon Ġthe Ġissues Ġin Ġinternational Ġpolitics Ġthey Ġconsider Ġmost Ġimportant . ĠAs Ġstates Ġface Ġfew Ġinstitutional Ġconstraints Ġduring Ġthese Ġspeeches , Ġthey Ġcan Ġexpress Ġtheir Ġpositions Ġon Ġa Ġwider Ġrange Ġof Ġissues Ġcompared Ġto Ġvotes Ġon Ġagenda - set Ġitems . </s> An Ġexample Ġof Ġthis Ġdifference Ġbetween Ġvotes Ġand Ġspeeches Ġcan Ġbe Ġseen Ġin Ġthe Ġcase Ġof ĠGreece Ġand ĠTurkey Ġin Ġ1974 . ĠBoth Ġcountries Ġwere ĠNATO Ġmembers , Ġhowever , ĠTurkey 's Ġinvasion Ġof ĠCyprus Ġin ĠJuly Ġof Ġ1974 Ġled Ġto Ġheightened Ġtension Ġand Ġhostilities Ġbetween Ġthe Ġtwo Ġnations . ĠThis Ġincluded ĠTurkish Ġand ĠGreek Ġfighter Ġjets Ġengaging Ġin Ġa Ġdog fight Ġwhich Ġresulted Ġin Ġthe Ġdeath Ġof Ġa ĠTurkish Ġpilot . ĠYet , Ġthe Ġideal Ġpoints Ġof ĠGreece Ġand ĠTurkey Ġbased Ġon ĠUN Ġvotes Ġthat Ġyear Ġwere Ġthe Ġmost Ġsimilar Ġamong ĠNATO Ġmember Ġstates Ġ( 0 . 68 Ġand Ġ0 . 42 , Ġrespectively ). ĠWhile Ġtheir Ġvotes Ġindicate Ġthat Ġthey Ġhave Ġbroadly Ġsimilar Ġforeign Ġpolicy Ġpreferences Ġand Ġprovide Ġuseful Ġsignals Ġof Ġmembership Ġon Ġan Ġalliance Ġdimension Ġ( e . g . Ġthey Ġare Ġspat ially Ġdistant Ġfrom ĠWarsaw ĠPact Ġmembers Ġin Ġ1974 ), Ġthey Ġfail Ġto Ġreflect Ġthe Ġsignificant Ġtension Ġbetween Ġthe Ġcountries . ĠIn Ġcontrast , Ġtheir Ġspeeches Ġin Ġthe Ġ1974 ĠGeneral ĠDebate Ġclearly Ġreveal Ġthe Ġhostility Ġbetween Ġthe Ġtwo Ġnations . ĠBoth ĠGreece Ġand ĠTurkey Ġdiscussed Ġthe ĠCyprus Ġinvasion Ġat Ġlength Ġin Ġtheir Ġspeeches , Ġwith Ġeach Ġblaming Ġthe Ġcrisis Ġon Ġthe Ġother . </s> We Ġdraw Ġon Ġa Ġrecently Ġreleased Ġcorpus Ġof Ġstate Ġspeeches Ġdelivered Ġduring Ġthe Ġannual ĠUN ĠGeneral ĠDebate Ġthat Ġprovides Ġthe Ġfirst Ġdataset Ġof Ġtextual Ġoutput Ġfrom Ġstates Ġthat Ġis Ġrecorded Ġat Ġregular Ġtime - series Ġintervals Ġand Ġincludes Ġa Ġsample Ġof Ġall Ġcountries Ġthat Ġdeliver Ġspeeches ĠB IB REF 11 Ġ. ĠThere Ġare Ġlimitations Ġto Ġboth Ġvotes Ġand Ġspeeches Ġin Ġthe ĠUN Ġin Ġder iving Ġestimates Ġof Ġstates ' Ġunderlying Ġpreferences . ĠHowever , Ġit Ġis Ġnot Ġcontroversial Ġto Ġsuggest Ġthat Ġstate Ġspeeches Ġcan Ġval uably Ġcomplement Ġroll Ġcall Ġdata , Ġand Ġthe Ġuse Ġof Ġspeeches Ġand Ġvotes Ġtogether Ġcan Ġreveal Ġuseful Ġpreference Ġinformation Ġbeyond Ġthat Ġcontained Ġin Ġstates ' Ġvoting Ġbehavior Ġor ĠGD Ġspeeches Ġalone . ĠThe Ġquestion , Ġrather , Ġis Ġhow Ġbest Ġto Ġrepresent Ġthese Ġtexts Ġand Ġhow Ġbest Ġto Ġtheoretically Ġmodel Ġthese Ġdata Ġin Ġtandem . </s> Word Ġembed d ings </s> In Ġorder Ġto Ġuse Ġtexts Ġtogether Ġwith Ġvotes Ġto Ġestimate Ġpreference Ġpolarization , Ġwe Ġfirst Ġconsider Ġhow Ġto Ġbetter Ġexploit Ġthe Ġinformation Ġcontained Ġin Ġtextual Ġdata . ĠTo Ġdo Ġthis , Ġwe Ġintroduce Ġa Ġnew Ġrepresentation Ġof Ġtextual Ġdata Ġwhich Ġmore Ġadequately Ġcaptures Ġdynamics Ġof Ġhuman Ġlanguage , Ġnamely Ġun super vised Ġlearned Ġword Ġembed d ings . ĠIn Ġthe Ġbroader Ġnatural Ġlanguage Ġprocessing Ġ( N LP ) Ġliterature , Ġthere Ġhas Ġbeen Ġa Ġsurge Ġof Ġresearch Ġdevoted Ġto Ġthe Ġdevelopment Ġof Ġdistribution al Ġrepresentations Ġof Ġspeech Ġwhich Ġretain Ġsynt act ical Ġlanguage Ġqualities Ġin Ġways Ġthat Ġthe Ġbag - of - words Ġ( B OW ) Ġapproach Ġtypically Ġused Ġin Ġpolitical Ġtext Ġanalysis Ġresearch Ġis Ġnot Ġequipped Ġto Ġretain . ĠThe Ġhypothesis Ġclaims Ġthat Ġwords Ġthat Ġoccur Ġin Ġsimilar Ġcontexts Ġtend Ġto Ġhave Ġsimilar Ġmeanings ĠB IB REF 12 Ġ. ĠWhen Ġoperational ized , Ġthe Ġunique Ġintuition Ġis Ġthat Ġsimilar Ġwords Ġand Ġphrases , Ġsuch Ġas ĠâĢ ľ atomic , Ġweapons âĢ Ŀ Ġand ĠâĢ ľ nuclear , Ġwarheads âĢ Ŀ Ġare Ġfound Ġin Ġrelatively Ġprox imate Ġvector Ġspace Ġlocations . ĠAlthough Ġthe ĠB OW Ġperforms Ġsurprisingly Ġwell , Ġthis Ġexample Ġhas Ġno Ġfeatures Ġin Ġcommon , Ġand Ġa ĠB OW Ġrepresentation Ġwould Ġassign Ġlow Ġsimilarity Ġscores Ġor Ġhigh Ġdistances . </s> When Ġresults Ġare Ġprojected Ġonto Ġa Ġtwo Ġdimensional Ġspace , Ġlanguage Ġrelationships Ġsurface , Ġsuch Ġas Ġthe Ġclust ering Ġof Ġsyn onyms , Ġan ton y ms , Ġscales Ġ( e . g . Ġdemocracy Ġto Ġauthoritarian ism ), Ġhyp onym - hyper ny ms Ġ( e . g . Ġdemocracy Ġis Ġa Ġtype Ġof Ġregime ), Ġco - hyp onyms Ġ( e . g . Ġatom Ġbombs Ġand Ġballistic Ġmissiles Ġare Ġtypes Ġof Ġweapons ), Ġand Ġgroups Ġof Ġwords Ġwhich Ġtend Ġto Ġappear Ġin Ġsimilar Ġcontexts Ġlike Ġdiplomat , Ġenvoy , Ġand Ġembassy . ĠMik ol ov Ġand Ġcollaborators Ġintroduce Ġan Ġevaluation Ġscheme Ġbased Ġon Ġword Ġanalog ies Ġthat Ġexamines Ġdimensions Ġof Ġdifference Ġin Ġvector Ġspace ĠB IB REF 13 Ġ, ĠB IB REF 14 Ġ. ĠThey Ġoriginally Ġreached Ġthe Ġsurprising Ġconclusion Ġthat Ġsimple Ġvector Ġaddition Ġand Ġsubt raction Ġun co vers Ġinteresting Ġlinear Ġsubst ruct ures Ġof Ġhuman Ġlanguage , Ġfamously Ġthat ĠIN LINE FORM 0 Ġ. </s> To Ġlocate Ġvector Ġspace Ġrepresentations Ġof Ġour Ġcorpus , Ġwe Ġutilize Ġthe ĠStanford ĠN LP Ġgroup 's ĠGlobal ĠV ect ors Ġfor ĠWord ĠRepresent ation Ġ( G lo Ve ) Ġun super vised Ġlearning Ġalgorithm ĠB IB REF 15 Ġ. ĠGl o Ve Ġis Ġa Ġpopular Ġlog Ġbil in ear , Ġweighted Ġleast Ġsquares Ġmodel Ġthat Ġtrains Ġon Ġglobal Ġword - word Ġco - occ ure nce Ġcounts Ġto Ġmake Ġefficient Ġuse Ġof Ġthe Ġcorpus Ġstatistics . ĠBecause Ġit Ġfactor izes Ġa Ġword - context Ġco - occ urrence Ġmatrix , Ġit Ġis Ġcloser Ġto Ġtraditional Ġcount Ġmethods Ġlike Ġlatent Ġsemantic Ġanalysis Ġor Ġprinciple Ġcomponent Ġanalysis . ĠHere , Ġwe Ġpresent Ġthree Ġanalog ical Ġexamples Ġfrom Ġthe Ġlocated Ġembed d ings : ĠDIS PLAY FORM 0 </s> where Ġeach ĠIN LINE FORM 0 Ġdescribes Ġa Ġvector Ġspace Ġlocation Ġof Ġthe Ġgiven Ġfeature , Ġand Ġthe Ġcos ine Ġsimilarity Ġbetween Ġeach Ġvector Ġspace Ġlocation Ġis Ġadded Ġor Ġsubt racted Ġto Ġfind Ġthe Ġclosest Ġvector Ġoffsets Ġ( with Ġcos ine Ġsimilarity Ġprinted Ġunderneath ). ĠThese Ġanalog ies Ġare Ġinterpreted , Ġfor Ġexample , Ġas ĠIN LINE FORM 1 Ġis Ġto ĠIN LINE FORM 2 Ġas ĠIN LINE FORM 3 Ġis Ġto ĠIN LINE FORM 4 Ġ. ĠThese Ġexamples Ġappear Ġto Ġencode Ġrelations Ġof Ġcause - effect , Ġthreats / harm , Ġand Ġlocation / ge ography , Ġrespectively . ĠAs Ġfound Ġin Ġthe Ġwider ĠN LP Ġliterature , Ġthe Ġimplication Ġis Ġthat Ġthese Ġvector Ġspace Ġmodels Ġare Ġsurprisingly Ġeffective Ġat Ġcapturing Ġdifferent Ġlex ical Ġrelations , Ġdespite Ġthe Ġlack Ġof Ġsupervision . </s> To Ġmeasure Ġposition Ġsimilarities , Ġwe Ġapply Ġa Ġnew Ġdocument - level Ġdistance Ġmeasure Ġto Ġthe Ġembed d ings : Ġthe Ġ( rel ax ed ) ĠWord ĠM over 's ĠDistance Ġ( RW MD ) ĠB IB REF 17 Ġ. ĠRW MD Ġis Ġdescribed Ġin Ġgreater Ġdetail Ġin Ġthe ĠSupplementary ĠMaterials , Ġbut Ġin Ġshort , Ġthis Ġmeasures Ġthe Ġcumulative Ġdistance Ġrequired Ġto Ġtransform Ġone Ġstate 's Ġspeech Ġpoint Ġcloud Ġinto Ġthat Ġof Ġanother Ġstate , Ġensuring Ġthat Ġdifferences Ġdo Ġnot Ġsimply Ġreflect Ġthe Ġuse Ġof Ġdifferent Ġwords . ĠStates Ġemploy Ġvaried Ġlanguage Ġand Ġlex ical Ġpatterns Ġto Ġdescribe Ġsimilar Ġtopics . ĠFor Ġexample , Ġif Ġstate ĠA Ġsays ĠâĢ ľ nuclear Ġweapons Ġare Ġbad ," Ġand Ġstate ĠB Ġsays ĠâĢ ľ atom Ġbombs Ġare Ġterrible ," Ġthe Ġonly Ġfeature Ġin Ġcommon Ġis Ġthe Ġterm ĠâĢ ľ are ," Ġwhich Ġleads Ġto Ġnear - orth og on ality Ġin Ġtheir ĠB OW Ġvectors Ġand Ġlow Ġsimilarity Ġscores . ĠIf Ġa Ġthird Ġstate ĠC Ġsays ĠâĢ ľ atom Ġbombs Ġare Ġgood ," Ġthen ĠB Ġand ĠC Ġwould Ġexhibit Ġthe Ġhighest Ġcos ine Ġsimilarity Ġof Ġthe Ġthree , Ġdespite Ġhaving Ġthe Ġopposite Ġexpressed Ġpolicy Ġpositions . ĠWord Ġembed d ings Ġand Ġterm - document Ġmat rices Ġare Ġlocated Ġfor Ġeach Ġyear Ġin Ġthe Ġcorpus , Ġ1970 - 2014 , Ġand Ġstate Ġdy ad ĠRW MD Ġdistances Ġare Ġcalculated , Ġconverted Ġto Ġsimilarity Ġscores , Ġand Ġstored Ġin Ġan ĠIN LINE FORM 1 Ġmatrix ĠIN LINE FORM 2 Ġfor Ġeach Ġyear . </s> For Ġtexts Ġto Ġbe Ġconsidered Ġas Ġa Ġuseful Ġcomplement Ġto Ġroll Ġcall Ġdata , Ġwe Ġshould Ġsee Ġdifferences Ġin Ġthe Ġpositions Ġexpressed Ġin Ġspeeches Ġversus Ġvotes . ĠThis Ġwould Ġindicate Ġthat Ġthe Ġtwo Ġsources Ġreveal Ġdifferent Ġpreference Ġinformation Ġand Ġthat Ġusing Ġone Ġover Ġthe Ġother Ġrisks Ġoverlooking Ġavailable Ġpreference Ġsignals . ĠFurther , Ġfor Ġthe Ġproposed Ġword Ġembed d ings Ġapproach Ġto Ġbe Ġconsidered Ġuseful , Ġit Ġshould Ġprovide Ġgreater Ġinsight Ġinto Ġintra - bl oc Ġposition Ġvariation Ġbeyond Ġwhat Ġis Ġavailable Ġin Ġthe ĠB OW , Ġbecause Ġintra - bl oc Ġvariation Ġis Ġan Ġimportant Ġcomponent Ġof Ġthe Ġdefinition Ġof Ġpolarization Ġused Ġhere , Ġas Ġwell Ġas Ġin Ġmuch Ġof Ġthe Ġrecent Ġtext Ġversus Ġvotes Ġliterature . ĠThe Ġcase Ġof ĠNATO Ġand ĠWarsaw ĠPact Ġmembers Ġis Ġpresented Ġin Ġ[ fig : d iffs ] Figure Ġ1 , Ġwhich Ġplots Ġstates ' Ġideal Ġpoints Ġfrom ĠB IB REF 21 Ġ, ĠB OW Ġcos ine Ġsimilarities , Ġand ĠRW MD Ġsimilarities , Ġwith Ġthe Ġlatter Ġtwo Ġscaled Ġbetween ĠIN LINE FORM 0 Ġ. </s> Visual Ġassessment Ġof Ġintra - bl oc Ġsimilarities Ġindicates Ġthat Ġthe ĠRW MD s Ġbased Ġon Ġword Ġembed d ings Ġyield Ġhigher Ġvariations Ġin Ġexpressed Ġpositions Ġcompared Ġto Ġthe Ġcos ine Ġsimilarities Ġbetween Ġmembers ' ĠB OW Ġvectors . ĠThis Ġprovides Ġpreliminary Ġconfirmation Ġthat Ġthe Ġsuggested Ġword Ġembed d ings Ġapproach Ġcaptures Ġmore Ġinteresting Ġvariation Ġin Ġthe Ġpreferences Ġof Ġstates Ġcompared Ġto Ġthe ĠB OW . ĠFurthermore , Ġwhile Ġthe Ġideal Ġpoints Ġclearly Ġreflect Ġa Ġless ening Ġof ĠCold ĠWar Ġtensions , Ġthe ĠRW MD Ġsimilarities Ġappear Ġto Ġdetect Ġgreater Ġintra - bl oc Ġposition Ġvariations . ĠTherefore , Ġboth Ġappear Ġto Ġprovide Ġdifferent Ġand Ġpotentially Ġvaluable Ġinformation Ġfor Ġthe Ġestimation Ġof Ġstate Ġpreferences . </s> Multiple x Ġnetworks Ġand Ġcommunity Ġdetection </s> Having Ġoutlined Ġa Ġnovel Ġapproach Ġto Ġestimate Ġstates ' Ġpreference Ġsimilarity , Ġthe Ġsecond Ġstep Ġis Ġto Ġdetect Ġdistinct Ġcommunities Ġof Ġstates . ĠThe Ġnetwork Ġscience Ġliterature Ġon Ġcommunity Ġdetection Ġis Ġespecially Ġwell - su ited Ġfor Ġthis Ġtask . ĠOne Ġcommon Ġmeasure Ġis Ġmodular ity , Ġa Ġcommunity Ġdetection Ġhe uristic Ġthat Ġpartitions Ġa Ġnetwork Ġsuch Ġthat Ġthe Ġtotal Ġnumber Ġof Ġintra - community Ġedges Ġis Ġmaxim ized Ġrelative Ġto Ġa Ġbaseline Ġexpectation Ġfrom Ġa Ġnull Ġmodel Ġ( i . e . Ġa Ġrandom Ġgraph ) ĠB IB REF 22 Ġ. ĠThe Ġintuition Ġis Ġthat Ġa Ġcommunity Ġshould Ġhave Ġmore Ġor Ġstronger Ġties Ġamong Ġthe Ġactors Ġwithin Ġthe Ġcommunity Ġcompared Ġto Ġties Ġwith Ġactors Ġin Ġother Ġcommunities . ĠThis Ġcommon Ġapproach , Ġhowever , Ġcan Ġbe Ġmisleading Ġin Ġdense Ġnetworks , Ġsuch Ġas Ġvote Ġand Ġspeech Ġsimilarity . ĠIndeed , ĠB IB REF 22 Ġuse ĠUN Ġvoting Ġdata Ġto Ġillustrate Ġthe Ġchallenges Ġrelated Ġto Ġnetwork Ġclust ering Ġon Ġdata Ġwith Ġhigh Ġlevels Ġof Ġagreement Ġbetween Ġobservations . </s> To Ġovercome Ġthis Ġissue Ġwith Ġdensity Ġand Ġto Ġexploit Ġthe Ġinformation Ġfound Ġin Ġvotes Ġand Ġspeeches Ġin Ġtandem , Ġwe Ġinstead Ġturn Ġto Ġmultiple x Ġnetwork Ġcommunity Ġdetection . ĠMult il ayer Ġgraphs Ġconsist Ġof Ġmore Ġthan Ġone Ġlayer Ġand Ġpermit Ġthe Ġsearch Ġfor Ġcommunities Ġacross Ġgraph Ġlevels . ĠCommunity Ġdetection Ġmethods Ġfor Ġmult il ayer Ġgraphs , Ġthough , Ġare Ġstill Ġin Ġtheir Ġinfancy Ġand Ġmost Ġcurrent Ġapproaches Ġposit Ġthe Ġsame Ġcommunity Ġstructure Ġat Ġdifferent Ġlevels Ġof Ġthe Ġmult igraph . ĠAs Ġ[ fig : d iffs ] Figure Ġ1 Ġshows , Ġhowever , Ġvotes Ġand Ġspeeches Ġappear Ġto Ġexhibit Ġrather Ġheter ogeneous Ġstructures . </s> A Ġrecently Ġproposed Ġsolution Ġfor Ġthis Ġtask Ġis Ġthe ĠMult il ayer ĠExt raction Ġprocedure ĠB IB REF 27 Ġ. ĠThe Ġalgorithm Ġidentifies Ġdensely Ġconnected Ġvertex - l ayers Ġin Ġmult il ayer Ġnetworks Ġthrough Ġa Ġsignificance - based Ġscore Ġthat Ġquant ifies Ġthe Ġconnectivity Ġof Ġan Ġobserved Ġvertex - layer Ġset Ġby Ġcomparison Ġwith Ġa Ġmult il ayer Ġfixed Ġdegree Ġrandom Ġgraph Ġmodel . ĠFor Ġour Ġanalysis , Ġthe Ġclusters Ġfrom Ġvoting Ġdata Ġcomprise Ġone Ġlayer Ġand Ġthe Ġtext - based Ġclusters Ġcomprise Ġthe Ġsecond Ġlayer Ġof Ġthe Ġgraph Ġfor Ġeach Ġyear . ĠThe ĠSupplementary ĠMaterials Ġdescribes Ġthe Ġdata Ġmanipulation Ġsteps Ġin Ġdetail , Ġbut Ġin Ġshort , Ġwe Ġfollow ĠPaul s Ġand ĠCran mer Ġ( ĠB IB REF 8 Ġ) Ġin Ġperforming Ġ5 - ne arest Ġneighbor Ġclust ering Ġon Ġthe Ġmatrix Ġof Ġstate Ġspeech Ġsimilarities Ġto Ġfind Ġcandidates Ġfor Ġaffinity Ġcommunities Ġand Ġthen Ġassign Ġties Ġabove Ġthresholds . ĠThese Ġtext - based Ġclusters Ġand Ġtheir Ġvote - based Ġclusters Ġare Ġthen Ġused Ġas Ġtwo Ġlayers Ġof Ġa Ġmult il ayer Ġnetwork , Ġand Ġthe ĠMult il ayer ĠExt raction Ġalgorithm Ġis Ġused Ġto Ġdetect Ġaffinity Ġbloc s Ġacross Ġthe Ġtwo Ġlayers . ĠThis Ġprocess Ġis Ġillustrated Ġin Ġ[ fig : pl ots ] Figure Ġ2 , Ġwhich Ġdisplays Ġ( a ) Ġthe Ġvote Ġand Ġtext Ġsimilarity Ġmat rices , Ġ( b ) Ġthe Ġsingle Ġlayer Ġvote Ġand Ġspeech Ġclusters , Ġand Ġ( c ) Ġthe Ġmultiple x Ġaffinity Ġbloc s Ġlocated Ġacross Ġboth Ġlayers . </s> E mp ir ical ĠApplication : ĠAff inity ĠBl oc s Ġand ĠConflict ĠOn set </s> A Ġrecently Ġpublished Ġnetwork Ġconflict Ġonset Ġmodel Ġprovides Ġan Ġideal Ġtest Ġfor Ġour Ġproposed Ġmult il ayer Ġaffinity Ġbloc s . ĠB IB REF 8 Ġuse Ġspectral Ġclust ering Ġto Ġidentify Ġdensely Ġconnected Ġaffinity Ġcommunities Ġbased Ġon ĠUN Ġroll Ġcall Ġdata Ġand Ġemploy Ġthese Ġclusters Ġin Ġtemporal Ġexponential Ġrandom Ġgraph Ġmodels Ġ( TER G Ms ) Ġto Ġinfer Ġthe Ġrelationship Ġbetween Ġviolent Ġconflict Ġonset Ġand Ġaffinity Ġcommunities Ġin Ġthe ĠUN GA , Ġamong Ġother Ġcovari ates Ġof Ġinterest . ĠThe Ġoutcome Ġnetwork Ġof Ġinterest Ġis Ġconstructed Ġfrom Ġconflict Ġonset Ġdata Ġfrom Ġthe ĠCor rel ates Ġof ĠWar Ġ( C OW ) Ġproject 's ĠMil itar ized ĠInterstate ĠDisp ute Ġ( M ID ) Ġdataset Ġ( v 4 . 1 ) ĠB IB REF 30 Ġ. ĠThe Ġtime Ġperiod Ġfor Ġthe Ġmodel Ġwe Ġreplicate Ġspans Ġ1965 - 2000 , Ġwhereas Ġour Ġcorpus Ġof Ġspeeches Ġcovers Ġ1970 - 2014 . ĠWe Ġlimit Ġour Ġextension Ġto Ġthe Ġyears Ġof Ġoverlap , Ġ1970 - 2000 . </s> Our Ġinf erential Ġanalysis Ġfirst Ġrepl icates Ġtheir Ġfull Ġconflict Ġonset Ġmodel ĠB IB REF 8 Ġ, Ġwhich Ġwe Ġsuccessfully Ġdo . ĠThis Ġsame Ġmodel Ġis Ġestimated Ġover Ġthe Ġconstrained Ġtime Ġrange Ġof Ġour Ġanalysis Ġto Ġensure Ġthat Ġsignificance Ġand Ġdirection Ġof Ġthe Ġcoefficients Ġon Ġthe Ġcovari ates Ġdo Ġnot Ġsubstantially Ġdiffer . ĠWe Ġfind Ġthat Ġthe Ġsigns Ġremain Ġthe Ġsame Ġand Ġthe Ġcoefficients Ġdo Ġnot Ġchange Ġdramatically . ĠThese Ġresults Ġare Ġreported Ġin Ġ[ table : ter gm ] Table Ġ1 Ġas ĠModel Ġ1 Ġand Ġ2 , Ġrespectively . ĠWe Ġthen Ġuse Ġspeech Ġand Ġmultiple x Ġbloc s Ġas Ġsubstitutes Ġfor Ġthe Ġoriginal Ġvote Ġclusters Ġand Ġreport Ġthese Ġas ĠModel Ġ3 Ġand Ġ4 , Ġrespectively . </s> The Ġresults Ġof ĠModel Ġ3 Ġin Ġ[ table : ter gm ] Table Ġ1 Ġindicate Ġthat Ġthe Ġtext - based Ġclusters Ġexhibit Ġa Ġnoticeably Ġlarger Ġeffect Ġof Ġpreference Ġsimilarity Ġon Ġconflict Ġavoidance Ġcompared Ġto Ġthe Ġvote - based Ġclusters . ĠInterestingly , Ġthe Ġsignificance Ġof Ġjoint Ġdemocracy Ġfalls Ġoff , Ġas Ġwell Ġas Ġthe Ġeffects Ġof Ġthe Ġsecurity , Ġtrade , Ġand Ġeconomic Ġdependency Ġnetworks . ĠThis Ġimplies Ġthat Ġmembership Ġin Ġtext - based Ġaffinity Ġcommunities Ġis Ġassociated Ġwith Ġa Ġsubstantial Ġdecrease Ġin Ġthe Ġlikelihood Ġof Ġviolent Ġconflict Ġonset Ġand Ġis Ġa Ġmuch Ġlarger Ġeffect Ġcompared Ġto Ġother Ġcovari ates Ġof Ġinterest . ĠThis Ġstark Ġdifference Ġbetween Ġthe Ġvotes - Ġand Ġspeeches - cl uster Ġcoefficients Ġprovides Ġfurther Ġindication Ġof Ġunderlying Ġheterogeneity Ġin Ġthe Ġnetwork Ġgraphs . ĠBoth Ġindicate Ġthat Ġmembership Ġin Ġaffinity Ġcommunities Ġis Ġassociated Ġwith Ġa Ġdecrease Ġin Ġthe Ġlikelihood Ġof Ġconflict Ġonset Ġbut Ġappear Ġto Ġcapture Ġdifferent Ġmanifestations Ġof Ġlatent Ġpreferences . </s> The Ġmultiple x Ġmodel Ġdisplays Ġcoefficients Ġcloser Ġto ĠModels Ġ1 Ġand Ġ2 . ĠThe Ġmultiple x Ġbloc Ġindicates Ġthat Ġmembership Ġin Ġaffinity Ġcommunities Ġas Ġlocated Ġacross Ġvote Ġand Ġspeech Ġgraphs Ġis Ġassociated Ġwith Ġa Ġdecrease Ġin Ġthe Ġlikelihood Ġthat Ġa Ġgiven Ġpair Ġof Ġstates Ġwill Ġengage Ġin Ġarmed Ġconflict . ĠTo Ġincrease Ġconfidence Ġin Ġthese Ġresults , Ġhowever , Ġwe Ġfollow ĠB IB REF 8 Ġin Ġthe Ġassessment Ġof Ġout - of - sample Ġpredictive Ġaccuracy Ġby Ġtraining Ġmodels Ġon Ġfive Ġyear Ġwindows Ġand Ġthen Ġassessing Ġpredictions Ġon Ġthe Ġnext Ġyear . ĠThe Ġareas Ġunder Ġthe Ġprecision Ġrecall Ġcurves Ġare Ġthen Ġsummed Ġover Ġthe Ġentire Ġdate Ġrange . ĠThe Ġpredictive Ġcapability Ġof ĠModel Ġ3 Ġoutper forms Ġtheir Ġpaper 's Ġbaseline Ġmodel Ġwith Ġno Ġpreference Ġnetworks Ġbut Ġunder per forms Ġtheir Ġdate - adjusted Ġmodel Ġas Ġmeasured Ġby Ġarea Ġunder Ġthe Ġprecision Ġrecall Ġcurve Ġ( 0 . 08 1 Ġvs . Ġ0 . 9 59 ). ĠThe Ġmultiple x Ġmodel , Ġhowever , Ġoutper forms Ġtheir Ġbaseline Ġpaper Ġmodel Ġ( with Ġand Ġwithout Ġclusters ), Ġthe Ġdate - adjusted Ġmodel , Ġand Ġour Ġtextual Ġextension Ġmodel . ĠThe Ġmultiple x Ġmodel Ġexhibits Ġa Ġ20 . 5 % Ġincrease Ġin Ġarea Ġunder Ġthe Ġprecision Ġrecall Ġcurve Ġcompared Ġto Ġthe Ġoriginal Ġdate - adjusted Ġmodel Ġ( 1 . 156 Ġvs . Ġ0 . 9 59 ). ĠConstruct ing Ġaffinity Ġbloc s Ġbased Ġon Ġtexts Ġand Ġvotes Ġin Ġtandem Ġthus Ġleads Ġto Ġa Ġmore Ġsubstant ively Ġintuitive Ġmodel , Ġas Ġwell Ġas Ġincreased Ġpredictive Ġperformance . ĠAlthough Ġthis Ġis Ġa Ġrelatively Ġlarge Ġgain Ġin Ġpredictive Ġaccuracy , Ġthese Ġsubstant ively Ġsmall Ġquantities Ġconfirm Ġthat Ġthe Ġprediction Ġof Ġviolent Ġconflict Ġonset Ġremains Ġan Ġenduring Ġchallenge Ġfor Ġscholars Ġof ĠIR . ĠNonetheless , Ġthe Ġability Ġto Ġexploit Ġrevealed Ġpreference Ġinformation Ġin Ġspeeches Ġand Ġvotes Ġin Ġtandem Ġappears Ġto Ġpromise Ġfruitful Ġpotential Ġgains Ġin Ġterms Ġof Ġmethodological Ġcapability Ġand Ġtheoretical Ġsound ness . </s> Conclusion </s> This Ġpaper Ġintroduces Ġa Ġnovel Ġapproach Ġto Ġestimate Ġpreference Ġpolarization Ġin Ġmult id imensional Ġsettings Ġusing Ġvotes Ġand Ġtextual Ġdata , Ġbased Ġon Ġdevelopments Ġin Ġthe Ġnatural Ġlanguage Ġprocessing Ġand Ġnetwork Ġscience Ġliter atures . ĠThe Ġapproach Ġhelps Ġto Ġbetter Ġexploit Ġinformation Ġfound Ġin Ġtextual Ġdata , Ġand Ġto Ġlocate Ġdense Ġclusters Ġin Ġcomplex Ġand Ġmult il ay ered Ġnetworks Ġin Ġways Ġthat Ġwere Ġpreviously Ġnot Ġcomput ationally Ġpossible . ĠDrawing Ġon Ġa Ġnew Ġdataset Ġof Ġstate Ġspeeches Ġin Ġthe ĠUN ĠGeneral ĠDebate , Ġtogether Ġwith Ġvoting Ġdata Ġfrom Ġthe ĠUN GA , Ġthese Ġtools Ġwere Ġemployed Ġto Ġbetter Ġestimate Ġrevealed Ġstate Ġpreferences Ġin Ġinternational Ġpolitics Ġand Ġto Ġlocate Ġpreference Ġaffinity Ġbloc s Ġwhich Ġexist Ġacross Ġmultiple Ġlayers Ġof Ġspeech Ġand Ġroll Ġcall Ġnetworks . </s> It Ġis Ġperhaps Ġworth Ġnoting Ġthat Ġthese Ġsources Ġare Ġrelatively Ġweak Ġsignals Ġof Ġtrue Ġstate Ġpreferences Ġand Ġpropensity Ġto Ġengage Ġin Ġarmed Ġconflict . ĠHowever , Ġthese Ġmethods Ġsignificantly Ġimprove Ġour Ġability Ġto Ġidentify Ġmeaningful Ġpatterns Ġof Ġpreference Ġsimilarity Ġamongst Ġthe Ġnoise . ĠFurthermore , Ġthe Ġapproach Ġcan Ġassist Ġany Ġpolitical Ġstudy Ġthat Ġseeks Ġto Ġmeasure Ġposition Ġsimilarities Ġfrom Ġtextual Ġdata Ġand Ġdetect Ġdense Ġclusters Ġof Ġaffinity Ġor Ġantagon ism Ġacross Ġmultiple Ġrelational Ġdatasets . ĠThese Ġmight Ġinclude Ġsocial Ġmedia Ġactors Ġwho Ġoperate Ġacross Ġmultiple Ġplatforms , Ġas Ġwell Ġas Ġcontexts Ġlike Ġlegislatures Ġwhere Ġcomplex Ġrelations Ġexist Ġacross Ġvotes , Ġspeeches , Ġcommittee Ġmembers hips , Ġand Ġothers . ĠTherefore , Ġthe Ġapproach Ġpresented Ġin Ġthis Ġpaper Ġwill Ġbe Ġuseful Ġto Ġall Ġscholars Ġbroadly Ġseeking Ġto Ġmeasure Ġpolitical Ġpreferences Ġand Ġpolarization Ġin Ġmult id imensional Ġcontexts . </s> Supplement ary ĠMaterials </s> The Ġfollowing Ġmaterial Ġcomple ments Ġthe Ġanalysis Ġin Ġthe Ġmain Ġtext . ĠTo Ġour Ġknowledge , Ġthis Ġpaper Ġpresents Ġthe Ġfirst Ġpolitical Ġscience Ġapplication Ġof Ġvector Ġspace Ġrepresentations Ġof Ġtextual Ġdata , Ġthe ĠWord ĠM over 's ĠDistance , Ġand Ġthe ĠMult il ayer ĠExt raction Ġprocedure . ĠThe ĠSupplementary ĠMaterials Ġare Ġstructured Ġas Ġfollows . ĠFirst , Ġthe Ġdata Ġare Ġdiscussed Ġand Ġapplications Ġwhich Ġuse Ġa Ġbag - of - words Ġtreatment Ġare Ġpresented . ĠSecond , Ġthe Ġvector Ġspace Ġapproach Ġand Ġdistance Ġmeasure Ġare Ġintroduced Ġand Ġdiscussed . ĠFinally , Ġthe Ġnetwork Ġclust ering Ġapproach Ġand Ġmodel Ġevaluations Ġare Ġpresented . </s> Data Ġand ĠMeasure ments </s> We Ġdraw Ġon Ġthe Ġnewly Ġreleased ĠUN ĠGeneral ĠDebate ĠCorpus ĠB IB REF 11 Ġwhich Ġcontains Ġevery Ġcountry Ġstatement Ġin Ġthe ĠUN ĠGeneral ĠDebate Ġbetween Ġ1970 Ġand Ġ2014 . ĠThe ĠGeneral ĠDebate Ġ( GD ) Ġtakes Ġplace Ġevery ĠSeptember Ġat Ġthe Ġstart Ġof Ġeach Ġnew Ġsession Ġof Ġthe ĠUN ĠGeneral ĠAssembly Ġ( UN GA ). ĠIt Ġprovides Ġall Ġmember Ġstates Ġwith Ġthe Ġopportunity Ġto Ġaddress Ġthe ĠUN GA Ġand Ġto Ġpresent Ġtheir Ġperspective Ġon Ġkey Ġissues Ġin Ġworld Ġpolitics . ĠGovernments Ġuse Ġtheir ĠGD Ġstatements Ġto Ġput Ġon Ġthe Ġrecord Ġtheir Ġposition Ġon Ġevents Ġthat Ġhave Ġoccurred Ġduring Ġthe Ġpast Ġyear Ġand Ġon Ġlonger - term Ġunderlying Ġissues Ġin Ġworld Ġpolitics Ġrelated Ġto Ġissues Ġsuch Ġas Ġconflict , Ġterrorism , Ġdevelopment , Ġhuman Ġrights , Ġand Ġclimate Ġchange . </s> A Ġprincipal Ġdifference Ġbetween ĠGD Ġstatements Ġand ĠUN GA Ġvoting Ġis Ġthat Ġthe ĠGD Ġstatements Ġare Ġnot Ġinstitution ally Ġconnected Ġto Ġdecision - making Ġin Ġthe ĠUN . ĠAs Ġa Ġresult , Ġgovernments Ġare Ġfree Ġto Ġdiscuss Ġthe Ġissues Ġthey Ġconsider Ġto Ġbe Ġof Ġgreatest Ġimportance Ġin Ġworld Ġpolitics , Ġregardless Ġof Ġwhether Ġan Ġissue Ġis Ġon Ġthe Ġformal Ġagenda Ġof Ġthe ĠUN GA . ĠTherefore , Ġas ĠB IB REF 32 Ġnotes , Ġthe ĠGeneral ĠDebate Ġacts ĠâĢ ľ as Ġa Ġbar ometer Ġof Ġinternational Ġopinion Ġon Ġimportant Ġissues , Ġeven Ġthose Ġnot Ġon Ġthe Ġagenda Ġfor Ġthat Ġparticular Ġsession . âĢ Ŀ ĠIn Ġproviding Ġinformation Ġabout Ġstates ' Ġpreferences Ġon Ġworld Ġpolitics , Ġthe ĠGD Ġprovides Ġa Ġvaluable Ġdata Ġsource Ġfor Ġmeasuring Ġpolarization Ġin ĠInternational ĠRelations . ĠIn Ġaddition Ġto Ġbeing Ġthe Ġone Ġmajor Ġforum Ġwhere Ġstates Ġpresent Ġtheir Ġviews Ġon Ġinternational Ġpolitics Ġfree Ġfrom Ġexternal Ġconstraints , Ġthe Ġfact Ġthat Ġit Ġtakes Ġplace Ġannually Ġand Ġincludes Ġall ĠUN Ġmember Ġstates Ġenables Ġcomparison Ġover Ġtime Ġand Ġacross Ġcountries . ĠReaders Ġare Ġencouraged Ġto Ġconsult ĠB IB REF 11 Ġfor Ġa Ġcomprehensive Ġintroduction Ġto Ġthe Ġcorpus . </s> As Ġmentioned Ġin Ġthe Ġmain Ġtext , Ġwe Ġdiscuss Ġan Ġexample Ġwhere Ġdisagreement Ġis Ġobvious Ġin Ġstates ' ĠGD Ġspeeches Ġbut Ġless Ġobvious Ġin Ġtheir Ġvoting Ġbehavior . ĠConsider Ġthe Ġfollowing Ġbrief Ġexcerpts Ġfrom Ġthe ĠGD Ġspeeches Ġof ĠGreece Ġand ĠTurkey Ġin Ġ1974 . </s> G ree ce : ĠOn Ġ15 ĠJuly Ġa Ġcoup , Ġcondemned Ġby Ġall Ġof Ġus , Ġwas Ġstaged Ġto Ġoverthrow ĠArchbishop ĠMak arios , Ġthe Ġlegitimate , Ġelected ĠPresident Ġof Ġthe ĠRepublic . ĠThis Ġcoup Ġwas Ġnot Ġdirected Ġagainst Ġthe ĠTurkish ĠCy pri ot Ġcommunity Ġof Ġthe Ġisland ... ĠDuring Ġthe Ġfighting Ġwhile Ġthe Ġcoup Ġwas Ġin Ġprogress , Ġnot Ġa Ġsingle ĠTurkish ĠCy pri ot Ġwas Ġkilled Ġor Ġinjured . ĠYet Ġfive Ġdays Ġlater , Ġlarge ĠTurkish Ġinvasion Ġforces Ġwere Ġlanding Ġin ĠCyprus Ġand Ġthe ĠTurkish ĠAir ĠForce Ġwas Ġlaunching Ġindiscrim inate Ġattacks Ġagainst Ġunarmed Ġcivilians , Ġunder Ġthe Ġfl imsy Ġpretext Ġof Ġprotecting Ġthe ĠTurkish ĠCy pri ot Ġminority Ġon Ġthe Ġisland , Ġwhich , ĠI Ġrepeat , Ġhad Ġnot Ġbeen Ġharmed Ġin Ġany Ġway ... ĠTwo Ġhours Ġlater , Ġthe ĠTurkish Ġtroops Ġwere Ġon Ġthe Ġmove Ġagain , Ġs owing Ġdeath Ġand Ġdestruction , Ġkilling ĠUnited ĠNations Ġtroops , Ġbombing Ġhospitals Ġand Ġschools . ĠRepe ated Ġcease - fire Ġcalls Ġby Ġthe ĠSecurity ĠCouncil Ġwent Ġun he eded . ĠTurkey Ġeven Ġignored Ġthe Ġceasefire Ġproclaimed Ġby Ġits Ġown ĠPrime ĠMinister Ġon Ġ16 ĠAugust Ġ1974 . </s> Turkey : ĠTurkey Ġhas Ġconstantly Ġhad Ġto Ġface Ġfa its Ġaccompl is Ġof Ġincreasingly Ġserious Ġscope , Ġparticularly Ġsince Ġ1963 . ĠThe Ġmost Ġrecent Ġand Ġthe Ġmost Ġserious Ġof Ġthese Ġfa its Ġaccompl is Ġwas , Ġas Ġwe Ġall Ġknow , Ġthat Ġof Ġ15 ĠJuly Ġlast : Ġa Ġforeign ĠPower Ġundertook Ġa Ġcoup Ġd ' et at Ġwhich Ġit Ġhad Ġlong Ġbeen Ġf om enting Ġand Ġthe Ġpurpose Ġof Ġwhich Ġwas Ġto Ġannex Ġthe Ġisland ... ĠThe Ġcoup Ġd ' et at Ġof Ġ15 ĠJuly Ġwas Ġdirected Ġprecisely Ġagainst Ġthe ĠTurkish Ġcommunity Ġand Ġwas Ġdirectly Ġaimed Ġat Ġthe Ġannexation Ġof Ġthe Ġisland Ġto ĠGreece ... ĠI Ġhave Ġnot , Ġhowever , Ġfinished Ġcorrecting Ġall Ġthe Ġfalse Ġallegations Ġand Ġbaseless Ġcharges Ġmade Ġby Ġmy Ġcolleague . ĠI Ġreserve Ġthe Ġright Ġto Ġdo Ġso Ġwhen Ġwe Ġspeak Ġon Ġthis Ġmatter Ġbefore Ġthe ĠGeneral ĠAssembly . ĠMy ĠGreek Ġcolleague 's Ġspeech , Ġunfortunately , Ġshows Ġthe Ġnature Ġof Ġthe Ġatmosphere Ġin Ġwhich Ġthe Ġdebate Ġwill Ġtake Ġplace Ġon Ġthe Ġfuture Ġof Ġthe Ġtwo Ġcommunities , ĠTurkish Ġand ĠGreek , Ġin Ġthe Ġisland . </s> The Ġtwo Ġrepresentatives Ġare Ġoutlining Ġtheir Ġpositions Ġon Ġthe Ġcontroversy Ġrelated Ġto Ġthe ĠTurkish Ġinvasion Ġof ĠCyprus . ĠExp ressed Ġdisagreement Ġon Ġthis Ġtopic Ġis Ġclearly Ġpresent Ġin Ġthe Ġspeeches , Ġbut Ġas Ġmentioned Ġin Ġthe Ġmain Ġpaper , Ġthe Ġtwo Ġcountries ' Ġvoting Ġideal Ġpoints Ġfor Ġthat Ġyear Ġat Ġthe Ġmost Ġsimilar Ġamongst Ġall ĠNATO Ġmembers . ĠA Ġfurther Ġexample Ġis Ġillustrated Ġin Ġthe Ġspeeches Ġand Ġvoting Ġhabits Ġof ĠIndia Ġand ĠPakistan Ġin Ġ1999 , Ġthe Ġyear Ġthe Ġtwo Ġcountries Ġwent Ġto Ġwar Ġ( the ĠK arg il ĠWar ). ĠConsider Ġthe Ġfollowing Ġexcerpts Ġfrom Ġtheir ĠGeneral ĠDebate Ġstatements Ġthat Ġyear : </s> Pakistan : ĠThe ĠK arg il Ġcrisis Ġwas Ġa Ġmanifestation Ġof Ġthe Ġdeeper Ġm ala ise Ġspawned Ġby Ġthe Ġunresolved ĠKashmir Ġproblem Ġand ĠIndia 's Ġescalating Ġrepression Ġof Ġthe ĠKashmir i Ġpeople . ĠIndia Ġlaunched Ġa Ġmassive Ġmilitary Ġoperation Ġin ĠK arg il Ġand Ġthreatened Ġa Ġwider Ġconflict Ġby Ġmobil izing Ġits Ġarmed Ġforces Ġall Ġalong Ġthe ĠPakistan - India Ġinternational Ġborder . ĠPakistan Ġacted Ġwith Ġrestraint ... ĠIndia 's Ġrepression Ġin ĠJ ammu Ġand ĠKashmir Ġhas Ġkilled Ġthousands Ġof ĠKashmir is , Ġforced Ġhundreds Ġof Ġthousands Ġinto Ġexile , Ġled Ġto Ġthree Ġwars Ġbetween ĠPakistan Ġand ĠIndia Ġand Ġcons igned Ġthe Ġtwo Ġcountries Ġto Ġa Ġrelationship Ġof Ġendemic Ġconflict Ġand Ġmistrust . </s> India : ĠPre med itated Ġaggression Ġby Ġregular Ġforces Ġwas Ġcommitted Ġagainst ĠIndia . ĠNot Ġsimply Ġwas Ġthe ĠLah ore ĠDeclaration Ġviolated , Ġbut Ġso Ġwas Ġthe ĠSim la ĠAgreement , Ġwhich Ġhad Ġprevented Ġconflict Ġfor Ġmore Ġthan Ġa Ġquarter Ġof Ġa Ġcentury . ĠIn Ġself - def ence , Ġyet Ġwith Ġthe Ġutmost Ġrestraint , ĠIndia Ġtook Ġall Ġnecessary Ġand Ġappropriate Ġsteps Ġto Ġevict Ġthe Ġaggress or Ġforces Ġfrom Ġits Ġterritory .... ĠWe Ġhave Ġbeen Ġgreatly Ġdisappointed Ġby Ġthis Ġcomp ulsive Ġhostility Ġof ĠPakistan , Ġbecause Ġit Ġis Ġan Ġaber ration Ġin Ġour Ġregion Ġtoday , Ġwhere Ġall Ġthe Ġother ĠSouth ĠAsian ĠAssociation Ġfor ĠRegional ĠCooperation Ġ( SA ARC ) Ġcountries Ġare Ġat Ġpeace Ġwith Ġeach Ġother , Ġand Ġare Ġtrying , Ġb ilaterally Ġand Ġthrough Ġthe ĠSA ARC Ġmechanisms , Ġto Ġtackle Ġtogether Ġthe Ġgreat Ġchallenge Ġof Ġdevelopment . </s> T ensions Ġare Ġclearly Ġpresent Ġin Ġthe Ġtextual Ġdata Ġof Ġthe Ġrespective Ġcountries . ĠThat Ġsame Ġyear , Ġhowever , ĠIndia Ġand ĠPakistan Ġcast ed Ġvery Ġsimilar Ġvotes Ġin Ġthe ĠUN , Ġwith Ġideal Ġpoints Ġof Ġ- 0 . 797 Ġand Ġ- 0 . 7 39 , Ġrespectively . ĠTherefore , Ġboth Ġsources Ġof Ġdata Ġappear Ġto Ġprovide Ġuseful Ġsignals Ġof Ġdifferent Ġaspects Ġof Ġunderlying Ġstate Ġpreferences . </s> Before Ġproceeding Ġto Ġthe Ġdescription Ġof Ġour Ġword Ġembed d ings Ġapproach , Ġit Ġis Ġuseful Ġto Ġfirst Ġexplore Ġthe Ġcorpus Ġthrough Ġcommonly Ġused Ġmeasures Ġof Ġdisagreement , Ġnamely ĠWord score Ġand ĠEucl ide an Ġdistance . ĠFurther , Ġthe Ġnetwork Ġpolarization Ġmeasure Ġknown Ġas Ġmodular ity Ġis Ġalso Ġused Ġto Ġexplore Ġthe Ġlevels Ġof Ġpolarization Ġexhibited Ġin Ġroll Ġcall Ġdata Ġversus Ġspeeches . ĠFirst , Ġa Ġbag - of - words Ġ( B OW ) Ġrepresentation Ġof Ġthe Ġspeeches Ġis Ġobtained Ġthrough Ġtoken ization , Ġstemming , Ġremoval Ġof Ġstop Ġwords , Ġconversion Ġto Ġlower Ġcase , Ġand Ġthe Ġremoval Ġof Ġpunct uation , Ġsymbols , Ġand Ġnumbers . ĠWe Ġkeep Ġonly Ġthe Ġfeatures Ġwhich Ġappear Ġat Ġleast Ġ5 Ġtimes Ġin Ġ3 Ġdocuments Ġand Ġapply Ġterm Ġfrequency - in verse Ġdocument Ġfrequency Ġ( TF - ID F ) Ġweight ing Ġto Ġthe Ġmatrix . ĠThis Ġyields Ġa Ġdocument Ġterm Ġfrequency Ġmatrix Ġfor Ġeach Ġyear . </s> The Ġmost Ġfrequently Ġused Ġtext Ġscaling Ġmethod Ġin Ġpolitical Ġscience Ġis ĠWord score ĠB IB REF 33 Ġ. ĠWord Ġfrequencies Ġin Ġthe Ġdocument Ġare Ġused Ġto Ġclassify Ġthe Ġdocument Ġinto Ġone Ġof Ġtwo Ġcategories . ĠWith ĠWord score , Ġthe Ġlearning Ġis Ġsupervised Ġusing Ġtraining Ġdocuments Ġthat Ġare Ġknown Ġto Ġbelong Ġto Ġeach Ġof Ġthe Ġtwo Ġcategories Ġso Ġthat Ġthe Ġchosen Ġdimension Ġis Ġsubstant ively Ġdefined Ġby Ġthe Ġchoice Ġof Ġtraining Ġdocuments . ĠHere Ġwe Ġapply Ġthe Ġapproach Ġto ĠUN GD Ġstatements Ġto Ġcalculate Ġlevels Ġof Ġdisagreement . ĠIn ĠFigure ĠFIG REF 19 Ġ, ĠWord score Ġdetects Ġin Ġboth Ġcases Ġa Ġdecline Ġin Ġpolarization Ġtowards Ġthe Ġend Ġof Ġthe ĠCold ĠWar Ġand Ġa Ġmodest Ġincrease Ġafter Ġthe Ġend Ġof Ġthe ĠCold ĠWar . ĠThis Ġbroadly Ġalign s Ġwith Ġexpectations Ġin Ġinternational Ġrelations Ġresearch . </s> Second , Ġwe Ġcompare Ġthis ĠWord score Ġdisagreement Ġto Ġthe ĠEucl ide an Ġdistances Ġbetween Ġthe ĠUS Ġand ĠRussia Ġand Ġthe ĠUS Ġand ĠChina Ġand Ġpresent Ġthese Ġfor Ġeach Ġsession Ġover Ġtime . ĠThese Ġare Ġcalculated Ġusing : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġare ĠTF - ID F Ġvectors Ġfor Ġthe Ġindividual Ġstates . ĠThe Ġresults Ġof Ġthis Ġmeasure Ġof Ġdisagreement Ġare Ġpresented Ġin ĠFigure ĠFIG REF 20 Ġ. ĠThe Ġdistances Ġclosely Ġalign Ġwith Ġthe ĠWord score Ġresults Ġfor ĠUS - Russia Ġand ĠUS - China Ġdy ads . ĠPolar ization Ġbroadly Ġdecreases Ġtowards Ġthe Ġend Ġof Ġthe ĠCold ĠWar Ġand Ġthen Ġincreases Ġafterwards . </s> Although Ġthese Ġmeasures Ġprovide Ġuseful Ġaggregate Ġinformation , Ġthey Ġoffer Ġlittle Ġby Ġway Ġof Ġhigh - resolution Ġinformation Ġon Ġpreference Ġdivergence . ĠFurther , Ġunlike Ġat Ġthe Ġnational Ġlegislative Ġlevel , Ġpol arity Ġin ĠIR Ġis Ġmore Ġthan Ġsimple Ġdisagreement . ĠPolar ization Ġrefers Ġto ĠâĢ ľ the Ġdegree Ġto Ġwhich Ġthe Ġforeign Ġpolicies Ġof Ġnations Ġwithin Ġa Ġsingle Ġcluster Ġare Ġsimilar Ġto Ġeach Ġother , Ġand Ġthe Ġdegree Ġto Ġwhich Ġthe Ġforeign Ġpolicies Ġof Ġnations Ġin Ġdifferent Ġclusters Ġare Ġdiss imilar " ĠB IB REF 9 Ġ. ĠContinuing Ġwith Ġthe ĠB OW Ġrepresentation Ġof Ġthe Ġspeeches , Ġit Ġis Ġinstruct ive Ġto Ġexplore Ġpreference Ġpolarization Ġusing Ġa Ġmore Ġsophisticated Ġmeasure Ġwhich Ġcaptures Ġthis Ġtheoretical Ġdefinition . </s> Mod ularity Ġis Ġa Ġrecently Ġdeveloped Ġcommunity Ġdetection Ġhe uristic Ġin Ġthe Ġnetwork Ġsciences Ġwhich Ġattempts Ġto Ġpartition Ġa Ġnetwork Ġsuch Ġthat Ġthe Ġtotal Ġnumber Ġof Ġintra - community Ġedges Ġis Ġoptimized Ġrelative Ġto Ġa Ġbaseline Ġexpectation Ġfrom Ġan Ġappropriate Ġnull Ġmodel Ġ( i . e . Ġa Ġrandom Ġgraph ) ĠB IB REF 22 Ġ. ĠThis Ġalign s Ġwith Ġour Ġtheoretical Ġdefinition Ġvia Ġits Ġcomparison Ġof Ġwithin - group Ġties Ġwith Ġother Ġsub group Ġties . ĠThe Ġintuition Ġis Ġthat Ġa Ġcommunity Ġshould Ġhave Ġmore Ġor Ġstronger Ġties Ġamong Ġthe Ġactors Ġwithin Ġthe Ġcommunity Ġcompared Ġto Ġties Ġwith Ġactors Ġin Ġother Ġcommunities . ĠMod ularity Ġis Ġmeasured Ġbetween Ġ[ 0 , 1 ] Ġwhere Ġhigher Ġlevels Ġof Ġmodular ity Ġindicate Ġstronger Ġdivisions Ġin Ġa Ġgiven Ġnetwork , Ġand Ġtherefore Ġhigher Ġlevels Ġof Ġpolarization . ĠThe Ġalgorithm Ġcan Ġbe Ġexpressed Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġtotal Ġweight Ġof Ġthe Ġedges Ġin Ġthe Ġnetwork , ĠIN LINE FORM 1 Ġis Ġthe Ġweighted Ġdegree Ġof Ġthe ĠIN LINE FORM 2 Ġth Ġnode , ĠIN LINE FORM 3 Ġis Ġthe Ġcommunity Ġto Ġwhich ĠIN LINE FORM 4 Ġbelongs , Ġand ĠIN LINE FORM 5 Ġ= Ġ1 Ġif ĠIN LINE FORM 6 Ġand ĠIN LINE FORM 7 Ġbelong Ġto Ġthe Ġsame Ġcommunity , Ġand Ġ0 Ġotherwise ĠB IB REF 34 Ġ. ĠSince Ġmodular ity Ġoptimization Ġis Ġan ĠNP - complete Ġproblem , Ġwe Ġutilize Ġthe Ġgreedy Ġvariant Ġwhich Ġis Ġimplemented Ġthrough Ġthe Ġig raph Ġpackage . </s> This Ġapproach Ġhas Ġenjoyed Ġwidespread Ġnetwork Ġscience Ġemployment Ġby Ġapplied Ġmathematic ians Ġand Ġphysicists ĠB IB REF 35 Ġ, Ġand Ġhas Ġsurfaced Ġin Ġpolitical Ġscience Ġstudies Ġof Ġinternational Ġtrade ĠB IB REF 36 Ġand Ġinternational Ġlaw ĠB IB REF 37 Ġ. ĠAs Ġa Ġdirect Ġmeasure Ġof Ġpolarization , Ġthis Ġapproach Ġhas Ġbeen Ġused Ġto Ġmeasure Ġroll Ġcall Ġpolarization Ġin Ġthe ĠUS ĠCongress ĠB IB REF 23 Ġ, ĠB IB REF 24 Ġ, ĠB IB REF 25 Ġ, Ġas Ġwell Ġas Ġthe ĠUN GA ĠB IB REF 22 Ġ. ĠTo Ġbridge Ġfrom Ġroll Ġcall Ġdata Ġto Ġour Ġspeeches , Ġit Ġis Ġinstruct ive Ġto Ġreplicate Ġthe Ġfindings Ġof ĠB IB REF 22 Ġusing Ġideal Ġpoint Ġdata Ġbased Ġon Ġvotes Ġand Ġto Ġcompare Ġthis Ġto Ġa Ġsimple Ġweighted Ġgraph Ġbased Ġon Ġthe Ġcos ine Ġsimilarity Ġof Ġthe Ġspeeches Ġbetween Ġstates Ġover Ġtime . ĠCos ine Ġsimilarity Ġis Ġwell Ġestablished Ġin Ġthe Ġnatural Ġlanguage Ġprocessing Ġliterature Ġbut Ġhas Ġalso Ġreceived Ġrecent Ġattention Ġin Ġpolitical Ġscience ĠB IB REF 38 Ġ. ĠThis Ġprovides Ġa Ġmeasure Ġof Ġsimilarity Ġbetween Ġtwo Ġvectors Ġof Ġan Ġinner Ġproduct Ġspace Ġwhich Ġmeasures Ġthe Ġcos ine Ġof Ġthe Ġangle Ġbetween Ġthem Ġand Ġis Ġexpressed Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġare Ġvectors Ġof Ġattributes Ġ( i . e . Ġterm Ġfrequency Ġvectors ), Ġand ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġare Ġcomponents Ġof Ġvectors ĠIN LINE FORM 4 Ġand ĠIN LINE FORM 5 Ġ( i . e . Ġterm Ġfrequencies ), Ġrespectively . ĠIn Ġthe Ġcase Ġof Ġtext , Ġsimilarity Ġscores Ġare Ġbounded Ġto Ġpositive Ġspace Ġ[ 0 , 1 ]. ĠFinally , Ġwe Ġnormal ize Ġusing : ĠDIS PLAY FORM 0 </s> The Ġresult Ġis Ġan ĠIN LINE FORM 0 Ġadj ac ency Ġmatrix ĠIN LINE FORM 1 Ġwhere ĠIN LINE FORM 2 Ġcontains Ġthe Ġnormalized Ġtextual Ġsimilarity Ġscore Ġ( i . e . Ġweighted Ġedge ) Ġfor Ġeach Ġpair Ġof Ġstates ĠIN LINE FORM 3 Ġand ĠIN LINE FORM 4 Ġ. </s> We Ġreplicate Ġthe Ġresults Ġof ĠB IB REF 22 Ġusing Ġideal Ġpoint Ġdata , Ġwhich Ġis Ġrepresented Ġas Ġthe Ġgray Ġline . ĠThe Ġblue Ġline Ġrepresents Ġthe Ġmodular ity Ġcalculated Ġon Ġthe Ġspeeches . ĠInformation Ġon Ġthe Ġroll Ġcall Ġdata Ġcan Ġbe Ġfound Ġin ĠB IB REF 21 Ġand ĠB IB REF 10 Ġ. ĠOur Ġaim Ġhere Ġis Ġto Ġestablish Ġa Ġpreliminary Ġidea Ġof Ġhow Ġpolarization Ġchanges Ġover Ġtime Ġin Ġthese Ġtwo Ġsources Ġof Ġdata . ĠTo Ġthe Ġextent Ġthat Ġthe Ġpatterns Ġdiver ge , Ġboth Ġsources Ġof Ġdata Ġprovide Ġpotentially Ġdifferent Ġinformation Ġabout Ġunderlying Ġpreferences . </s> In Ġline Ġwith Ġthe Ġsimple Ġmeasures Ġpresented Ġabove , Ġboth Ġnetworks Ġin ĠFigure ĠFIG REF 24 Ġexhibit Ġgenerally Ġdecreasing Ġlevels Ġof Ġpolarization Ġtowards Ġthe Ġend Ġof Ġthe ĠCold ĠWar Ġand Ġthen Ġsubsequent Ġincreases Ġor Ġstagnation Ġafterwards . ĠInterestingly , Ġthe Ġspeech Ġnetwork Ġdisplays Ġslightly Ġhigher Ġlevels Ġof Ġpolarization Ġthan Ġthe Ġideal Ġpoint Ġnetwork . ĠThis Ġindicates Ġthat Ġmore Ġheterogeneity Ġexists Ġin Ġspeeches Ġthan Ġvotes , Ġand Ġalign s Ġwith ĠFigure Ġ1 Ġand ĠFigure Ġ2 a Ġin Ġthe Ġmain Ġbody Ġof Ġthe Ġpaper . </s> For ĠNATO - Wars aw Ġin ĠFigure ĠFIG REF 25 Ġ, Ġthe Ġspeech Ġnetwork Ġfollows Ġa Ġsimilar Ġtrend Ġas Ġusing Ġroll Ġcall Ġdata Ġalone : Ġpol arity Ġdecreases Ġtowards Ġthe Ġend Ġof Ġthe ĠCold ĠWar . ĠIt Ġappears , Ġhowever , Ġthat Ġthe Ġspeech Ġweighted Ġnetwork Ġpicks Ġup Ġhigher Ġvariation Ġin Ġpreferences , Ġwhich Ġindicates Ġthat Ġlooking Ġto Ġideal Ġpoint Ġdata Ġalone Ġmight Ġover - Ġor Ġunder - est imate Ġlevels Ġof Ġpreference Ġpolarization Ġat Ġcertain Ġpoints Ġin Ġtime . ĠFinally , Ġthe ĠWest - Rest Ġalignment Ġprovides Ġfurther Ġconfirmation Ġthat Ġpol arity Ġcan Ġbe Ġdetected Ġin Ġthe Ġvotes Ġand Ġspeeches : ĠWest - Rest Ġmembers Ġexhibit Ġhigher Ġlevels Ġof Ġpolarization Ġcompared Ġto Ġthe ĠAssembly - level . ĠPolar ization Ġexhibits Ġa Ġdownward Ġtrend Ġin Ġthe Ġpost - Cold ĠWar Ġera ĠâĢĵ Ġthe Ġperiod Ġin Ġwhich Ġscholars Ġnote Ġan Ġincreased Ġprominence Ġof Ġliberal , Ġinter dependent Ġglobalization . ĠThese Ġresults Ġalign Ġwith Ġthe Ġtrends Ġfound Ġby ĠB IB REF 22 Ġ. ĠThese Ġresults Ġindicate Ġthat Ġweighted Ġword Ġcounts Ġprovide Ġuseful Ġinformation , Ġbut Ġwe Ġaim Ġto Ġleverage Ġthe Ġinformation Ġin Ġthe Ġspeeches Ġin Ġa Ġmore Ġsophisticated Ġway Ġand Ġin Ġa Ġway Ġthat Ġcan Ġincrease Ġour Ġconfidence Ġthat Ġdivergence Ġis Ġnot Ġsimply Ġdue Ġto Ġdifferences Ġin Ġword Ġchoice . ĠFor Ġthis Ġtask , Ġwe Ġmove Ġbeyond Ġthe ĠB OW Ġapproach Ġand Ġuse Ġvector Ġspace Ġrepresentations . </s> At Ġpresent , Ġthe Ġdevelopment Ġof Ġvector Ġspace Ġmodels Ġfor Ġtextual Ġdata Ġis Ġone Ġof Ġthe Ġareas Ġthat Ġhas Ġattracted Ġthe Ġmost Ġresearch Ġinterest Ġin Ġnatural Ġlanguage Ġprocessing . ĠThis Ġinterest Ġis Ġmotivated Ġby Ġthe Ġdesire Ġto Ġmove Ġaway Ġfrom Ġsimple Ġcounts Ġand Ġweights Ġof Ġwords , Ġtowards Ġrepresentations Ġwhich Ġcan Ġpreserve Ġword Ġcontext Ġand Ġlinguistic Ġfeatures Ġof Ġhuman Ġspeech . ĠStudies Ġfind Ġthat Ġthese Ġapproaches Ġare Ġnot Ġonly Ġintuitive ly Ġdesirable , Ġbut Ġalso Ġincrease Ġclassification Ġaccuracy Ġin Ġmachine Ġlearning Ġtasks . </s> Vector Ġspace Ġapproaches Ġinvolve Ġthe Ġembed ding Ġof Ġdocument Ġfeatures Ġfrom Ġone Ġdimension Ġper Ġword Ġspace Ġto Ġa Ġcontinuous , Ġlower Ġdimensional Ġvector Ġspace . ĠEach Ġdocument Ġfeature Ġis Ġrepresented Ġas Ġa Ġreal - valued Ġvector Ġand Ġit Ġhas Ġbeen Ġshown Ġthat Ġthese Ġrepresentations Ġretain Ġdesirable Ġsynt act ical Ġqualities , Ġsuch Ġas Ġcontext Ġand Ġstructure Ġof Ġspeech . ĠTrad itionally , Ġdimension ality Ġreduction Ġcould Ġbe Ġobtained Ġthrough Ġlatent Ġsemantic Ġanalysis Ġ( L SA ) Ġused Ġto Ġfactor ize Ġthe Ġfeature Ġmat rices , Ġbut Ġtwo Ġrecently Ġdeveloped Ġmodels Ġhave Ġbeen Ġintroduced Ġwhich Ġrely Ġon Ġdifferent Ġlogic : Ġword 2 vec Ġfrom ĠMik ol ov Ġand Ġthe Ġresearch Ġgroup Ġat ĠGoogle , Ġand Ġthe Ġglobal Ġvectors Ġfor Ġword Ġrepresentations Ġ( G lo Ve ) Ġun super vised Ġlearning Ġalgorithm Ġfrom Ġthe ĠStanford ĠNatural ĠLanguage ĠProcessing Ġgroup . </s> We Ġuse ĠGl o Ve Ġintroduced Ġby ĠB IB REF 15 Ġbecause Ġwhile ĠL SA Ġtends Ġto Ġmaximize Ġthe Ġstatistical Ġinformation Ġused , Ġit Ġdoes Ġnot Ġperform Ġwell Ġon Ġanalogy Ġtasks . ĠWord 2 vec Ġdoes Ġbetter Ġon Ġthe Ġanalogy Ġtest Ġbut Ġdoes Ġnot Ġutilize Ġstatistics Ġof Ġthe Ġcorpus Ġbecause Ġit Ġtrains Ġon Ġlocal Ġcontext Ġwindows . ĠGl o Ve Ġwas Ġintroduced Ġto Ġhelp Ġbridge Ġthis Ġgap Ġand Ġcombine Ġboth Ġdesirable Ġqualities . ĠIt Ġis Ġa Ġlog Ġbil in ear , Ġweighted Ġleast Ġsquares Ġmodel Ġthat Ġtrains Ġon Ġglobal Ġword - word Ġco - occ ure nce Ġcounts Ġand Ġthus Ġmakes Ġefficient Ġuse Ġof Ġthe Ġstatistics . ĠB IB REF 15 Ġshow Ġthat Ġtheir Ġapproach Ġyields Ġstate - of - the - art Ġperformance Ġon Ġthe Ġword Ġanalogy Ġtask . ĠGl o Ve Ġis Ġsometimes Ġcriticized Ġfor Ġscal ability Ġissues Ġbut Ġgiven Ġthat Ġwe Ġare Ġworking Ġwith Ġa Ġfixed Ġsize Ġcorpus Ġthis Ġdoes Ġnot Ġpose Ġan Ġissue Ġfor Ġour Ġanalysis . ĠReaders Ġare Ġencouraged Ġto Ġconsult Ġthe ĠGl o Ve Ġpaper Ġfor Ġfull Ġmodel Ġdetails , Ġbut Ġwe Ġdescribe Ġour Ġapproach Ġand Ġdecisions Ġhere . ĠThe Ġmodel Ġis Ġexpressed Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġrepresents Ġparameters , ĠIN LINE FORM 1 Ġis Ġthe Ġvocabulary Ġsize , ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġare Ġcolumn Ġand Ġrow Ġword Ġvectors , ĠIN LINE FORM 4 Ġis Ġthe Ġco - occ urrence Ġmatrix Ġof Ġall Ġpairs Ġof Ġwords Ġthat Ġever Ġco - occ ur , Ġand ĠIN LINE FORM 5 Ġis Ġa Ġweight ing Ġfunction Ġwhich Ġassigns Ġlower Ġweights Ġto Ġwords Ġwhich Ġfrequently Ġco - occ ur . ĠThis Ġlatter most Ġterm Ġserves Ġas Ġa Ġcap Ġon Ġvery Ġfrequent Ġwords , Ġfor Ġexample Ġarticles Ġlike ĠâĢ ľ the " Ġwhich Ġprovide Ġlittle Ġpredictive Ġinformation . ĠThe Ġalgorithm Ġseeks Ġto Ġminimize Ġthe Ġdistance Ġbetween Ġthe Ġinner Ġproduct Ġof Ġthe Ġword Ġvectors Ġand Ġthe Ġlog Ġcount Ġof Ġthe Ġco - occ urrence Ġof Ġthe Ġtwo Ġwords . ĠCompared Ġto Ġskip - gram Ġapproaches Ġwhich Ġupdate Ġat Ġeach Ġcontext Ġwindow , Ġit Ġis Ġclear Ġfrom Ġthe Ġutilization Ġof ĠIN LINE FORM 6 Ġthat Ġthe Ġmodel Ġtrains Ġrelatively Ġquickly Ġsince Ġit Ġuses Ġthe Ġknown Ġcorpus Ġstatistic Ġof Ġword Ġco - occ ur rences Ġfor Ġthe Ġentire Ġcorpus Ġat Ġonce . </s> We Ġfirst Ġstem , Ġtoken ize , Ġand Ġconvert Ġthe Ġwords Ġto Ġlower case . ĠUnlike Ġa ĠB OW Ġapproach , Ġhowever , Ġthe Ġpunct uation Ġis Ġretained . ĠThe Ġmodel Ġis Ġtrained Ġon Ġeach Ġindividual Ġyear Ġin Ġthe Ġcorpus Ġwith Ġthe Ġvocabulary Ġpr uned Ġto Ġinclude Ġa Ġminimum Ġterm Ġcount Ġof Ġ4 Ġacross Ġdocuments Ġand Ġthe Ġterm Ġmust Ġexist Ġin Ġ25 % Ġof Ġthe Ġdocuments . ĠThese Ġrelatively Ġstringent Ġparameter Ġlevels Ġare Ġemployed Ġbecause Ġwe Ġtrain Ġon Ġindividual Ġyears Ġin Ġorder Ġto Ġavoid Ġlanguage Ġdrift Ġover Ġtime Ġand Ġto Ġensure Ġthat Ġour Ġestimated Ġembed d ings Ġcorrespond Ġto Ġthe Ġexact Ġpolicy Ġlanguage Ġused Ġin Ġa Ġgiven Ġyear . ĠWe Ġemploy Ġa Ġskip Ġgram Ġwindow Ġof Ġ4 Ġand Ġsearch Ġusing Ġa Ġword Ġvector Ġsize Ġof Ġ50 . ĠAt Ġpresent , Ġwe Ġfollow Ġthe Ġcomputer Ġscience Ġliterature Ġsuggestion Ġof Ġtuning Ġthese Ġparameters Ġuntil Ġreasonable Ġand Ġreliable Ġlinear Ġcombinations Ġof Ġlanguage Ġare Ġlocated . ĠFuture Ġwork Ġshould Ġexplore Ġin Ġgreater Ġdetail Ġhow Ġsystematic Ġtuning Ġdecisions Ġfor Ġsocial Ġscience Ġapplications Ġcan Ġbe Ġmade . </s> M ik ol ov Ġand Ġcollabor ors Ġintroduce Ġa Ġnew Ġevaluation Ġscheme Ġbased Ġon Ġword Ġanalog ies Ġthat Ġexamines Ġdimensions Ġof Ġdifference Ġin Ġvector Ġspace , Ġas Ġopposed Ġto Ġscal ar Ġdistances Ġbetween Ġvectors ĠB IB REF 13 Ġ, ĠB IB REF 14 Ġ. ĠIn Ġorder Ġto Ġvalidate Ġthe Ġquality Ġof Ġthe Ġlocated Ġembed d ings , Ġwe Ġthus Ġfollow Ġcurrent Ġstandard Ġpractice Ġand Ġassess Ġwhether Ġreasonable Ġlinear Ġcombinations Ġof Ġwords Ġcan Ġbe Ġreturned . ĠThe Ġmain Ġbody Ġof Ġthe Ġpaper Ġpresents Ġthree Ġexamples , Ġand Ġa Ġfurther Ġexample Ġis Ġpresented Ġhere : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġdescribes Ġa Ġvector Ġspace Ġlocation Ġof Ġthe Ġgiven Ġfeature Ġand Ġthe Ġcos ine Ġdistance Ġbetween Ġeach Ġvector Ġspace Ġlocation Ġis Ġadded Ġor Ġsubt racted Ġto Ġfind Ġthe Ġclosest Ġvector Ġspace Ġfeature Ġ( present ed Ġon Ġthe Ġright Ġhand - side Ġof Ġthe Ġequality Ġwith Ġthe Ġcos ine Ġsimilarity Ġprinted Ġunderneath ). ĠThe Ġexample Ġis Ġinterpreted Ġas : Ġif Ġwe Ġadd Ġthe Ġlocation Ġof ĠIN LINE FORM 1 Ġto ĠIN LINE FORM 2 Ġ, Ġwe Ġarrive Ġat Ġthe Ġvector Ġspace Ġlocation Ġof Ġthe Ġword ĠIN LINE FORM 3 Ġ. ĠThis , Ġas Ġwell Ġas Ġthe Ġexamples Ġpresented Ġin Ġthe Ġmain Ġbody , Ġprovide Ġsome Ġindication Ġthat Ġintuitive Ġand Ġreasonable Ġembed d ings Ġhave Ġbeen Ġlocated . </s> Vector Ġspace Ġrepresentations Ġare Ġparticularly Ġpromising Ġfor Ġthe Ġmeasurement Ġof Ġpolarization . ĠIn Ġcontrast Ġto Ġa ĠB OW Ġapproach , Ġwe Ġwant Ġto Ġknow Ġnot Ġonly Ġthat Ġtwo Ġactors Ġuse Ġ( dis ) similar Ġwords , Ġbut Ġwe Ġwant Ġhigh Ġresolution Ġinsights Ġinto Ġhow Ġand Ġwhen Ġthey Ġspeak Ġdifferently Ġon Ġdifferent Ġtopics . ĠFor Ġexample , Ġwe Ġwould Ġlike Ġto Ġcapture Ġthe Ġdiss imilar ity Ġof Ġstatements Ġlike Ġ" we Ġoppose Ġthe Ġproliferation Ġof Ġnuclear Ġweapons " Ġversus Ġ" the Ġproliferation Ġof Ġatom Ġbombs Ġis Ġnecessary ." ĠThe Ġwords ĠâĢ ľ necessary " Ġand ĠâĢ ľ opp ose " Ġwould Ġbe Ġcounted Ġin Ġa ĠB OW Ġmatrix , Ġbut Ġtheir Ġusage Ġin Ġthe Ġcontext Ġof Ġnuclear Ġweapons Ġwould Ġbe Ġlost . ĠFurther , Ġif Ġone Ġstate Ġsays ĠâĢ ľ nuclear Ġweapon " Ġand Ġanother Ġsays ĠâĢ ľ atomic Ġbomb ," Ġthe Ġtwo Ġphrases Ġhave Ġno Ġwords Ġin Ġcommon Ġand Ġthe Ġphrases Ġwill Ġbe Ġthought Ġto Ġbe Ġdistant Ġor Ġdiss imilar , Ġdespite Ġreferring Ġto Ġthe Ġsame Ġthing . ĠAlthough ĠB OW Ġapproaches Ġoften Ġperform Ġvery Ġwell , Ġfor Ġthe Ġmeasurement Ġof Ġpolarization Ġin ĠIR , Ġwe Ġneed Ġto Ġmake Ġsure Ġthat Ġthe Ġdifferences Ġuncovered Ġare Ġnot Ġsimply Ġdue Ġto Ġdifferent Ġword Ġusage Ġor Ġpolicy Ġtopics Ġbeing Ġdiscussed . </s> To Ġmeasure Ġexpressed Ġ( dis ) ag reement Ġin Ġthese Ġspeeches , Ġit Ġis Ġnecessary Ġto Ġderive Ġa Ġdocument - level Ġrepresentation Ġof Ġthe Ġlearned Ġembed d ings . ĠAlthough Ġwell - established Ġmeasurements Ġbased Ġon Ġcos ine Ġsimilarity , ĠEucl ide an Ġdistance , Ġor ĠPearson Ġcorrelations Ġcould Ġbe Ġapplied Ġto Ġthe Ġword Ġembed d ings , Ġwe Ġutilized Ġthe Ġrelaxed Ġvariant Ġof Ġa Ġnewly Ġintroduced Ġdocument Ġdistance Ġmeasure Ġthat Ġexploits Ġinformation Ġcontained Ġin Ġboth Ġthe Ġword Ġembed d ings Ġand Ġterm - document Ġmat rices : Ġthe Ġ( rel ax ed ) ĠWord ĠM over 's ĠDistance Ġ( RW MD ) ĠB IB REF 17 Ġ. </s> W MD Ġinnov ates Ġby Ġleveraging Ġthe Ġfinding Ġthat Ġembed ding Ġdistances Ġbetween Ġword Ġvectors Ġare Ġsem antically Ġmeaningful . ĠW MD Ġrepresents Ġtext Ġdocuments Ġas Ġa Ġweighted Ġpoint Ġcloud Ġof Ġembedded Ġwords Ġwhere Ġthe Ġdistance Ġbetween Ġtwo Ġdocuments Ġis Ġthe Ġminimum Ġcumulative Ġdistance Ġthat Ġwords Ġfrom Ġdocument ĠA Ġwould Ġneed Ġto Ġtravel Ġto Ġmatch Ġexactly Ġthe Ġpoint Ġcloud Ġof Ġdocument ĠB . ĠThis Ġhas Ġbeen Ġshown Ġto Ġyield Ġstate - of - the - art Ġclassification Ġaccuracy ĠB IB REF 39 Ġ. ĠAlthough ĠW MD Ġis Ġrelatively Ġfast Ġto Ġcompute , Ġwe Ġuse Ġthe Ġrelaxed Ġvariant Ġ( RW MD ), Ġwhich Ġresults Ġin Ġtighter Ġbounds Ġand Ġis Ġshown Ġto Ġyield Ġlower Ġtest Ġerror Ġrates . ĠIn Ġshort , Ġthis Ġrelax es Ġthe Ġoptimization Ġproblem Ġthrough Ġthe Ġremoval Ġof Ġone Ġof Ġthe Ġtwo Ġconstraints . ĠIf Ġwe Ġlet ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġbe Ġthe ĠB OW Ġrepresentations Ġof Ġtwo Ġdocuments Ġin Ġthe ĠIN LINE FORM 2 Ġdimensional Ġsimple x Ġof Ġword Ġdistributions Ġwhich Ġwe Ġobtained Ġabove , Ġwe Ġcan Ġexpress ĠRW MD Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġsparse Ġflow Ġmatrix Ġwhere ĠIN LINE FORM 1 Ġdenotes Ġhow Ġmuch Ġof Ġword ĠIN LINE FORM 2 Ġin ĠIN LINE FORM 3 Ġtravels Ġto Ġword ĠIN LINE FORM 4 Ġin ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġis Ġdefined Ġto Ġbe Ġthe Ġdistance Ġbetween Ġthe Ġtwo Ġdocuments Ġas Ġthe Ġminimum Ġweighted Ġcumulative Ġcost Ġrequired Ġto Ġmove Ġall Ġwords Ġfrom ĠIN LINE FORM 7 Ġto ĠIN LINE FORM 8 Ġ. ĠThen , Ġthe Ġoptimal Ġsolution Ġis Ġfound Ġwhen Ġeach Ġword Ġin ĠIN LINE FORM 9 Ġmoves Ġall Ġof Ġits Ġprobability Ġmass Ġto Ġthe Ġmost Ġsimilar Ġword Ġin ĠIN LINE FORM 10 Ġ. ĠThis Ġoptimal Ġmatrix ĠIN LINE FORM 11 Ġis Ġdecided Ġby : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġdistance Ġof Ġinterest Ġwhich Ġwe Ġnormal ize Ġand Ġconvert Ġto Ġa Ġsimilarity Ġscore Ġusing Ġequation Ġ4 Ġabove Ġand ĠIN LINE FORM 1 Ġ, Ġrespectively . ĠThe Ġresult Ġis Ġa Ġlist Ġof ĠIN LINE FORM 2 Ġmat rices ĠIN LINE FORM 3 Ġfor Ġeach Ġyear Ġin Ġthe Ġcorpus Ġwhere ĠIN LINE FORM 4 Ġis Ġthe Ġspeech Ġsimilarity Ġscore Ġbetween Ġstates ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġfor Ġthe Ġgiven Ġyear Ġin Ġthe Ġcorpus Ġwith Ġthe Ġdi agon als Ġof Ġthe Ġmat rices Ġset Ġto Ġ0 . </s> Network ĠAnalysis </s> Although Ġwe Ġpresent Ġa Ġnovel Ġapproach Ġfor Ġleveraging Ġposition Ġsimilarity Ġinformation Ġfound Ġin Ġpolitical Ġtext , Ġit Ġis Ġnot Ġimmediately Ġobvious Ġthat Ġthis Ġapproach Ġis Ġuseful Ġfor Ġthe Ġtask Ġof Ġpolitical Ġanalysis . ĠWe Ġposit Ġthat Ġfor Ġthis Ġapproach Ġto Ġbe Ġconsidered Ġuseful , Ġit Ġshould Ġbe Ġcapable Ġof Ġproviding Ġinformation Ġuseful Ġfor Ġthe Ġperformance Ġof Ġinference Ġon Ġobserved Ġstate Ġbehavior . ĠWe Ġchoose Ġviolent Ġconflict Ġonset Ġbecause Ġthis Ġis Ġone Ġof Ġthe Ġenduring Ġmethodological Ġchallenges Ġfaced Ġby Ġthe Ġdiscipline . ĠFor Ġthis Ġtask , Ġwe Ġaim Ġto Ġsee Ġwhether Ġor Ġnot Ġthese Ġembedded Ġspeeches Ġprovide Ġinformation Ġon Ġstate Ġpreferences Ġwhich Ġimprove Ġupon Ġcurrent Ġout - of - sample Ġpredictions Ġrelative Ġto Ġcurrent Ġmodels Ġwhich Ġemploy ĠUN Ġroll Ġcall Ġdata . ĠWe Ġchoose Ġto Ġcompare Ġour Ġapproach Ġto Ġthe Ġrecently Ġpublished Ġmodels Ġof ĠB IB REF 8 Ġ, Ġbecause Ġtheir Ġgoal Ġand Ġapplications Ġwhich Ġuse ĠUN Ġroll Ġcall Ġdata Ġclosely Ġparallel Ġour Ġresearch Ġmotivations . ĠThis Ġsection Ġprovides Ġfurther Ġinformation Ġon Ġthe Ġgraph Ġpartition ing Ġapproach , Ġthe Ġnew ĠMult il ayer ĠExt raction Ġalgorithm , Ġand Ġmodel Ġperformance Ġassessments . </s> For Ġclusters Ġbased Ġon Ġspeeches Ġalone , Ġwe Ġfollow ĠPaul s Ġand ĠCran mer 's Ġ( ĠB IB REF 8 Ġ) Ġapproach Ġthrough Ġthe Ġperformance Ġof Ġ5 - ne arest Ġneighbor Ġclust ering Ġon Ġthe Ġmatrix Ġof ĠRW MD Ġstate - state Ġsimilarities , Ġwhich Ġyields Ġcandidates Ġfor Ġmembership Ġin Ġan Ġaffinity Ġcommunity . ĠAs Ġwe Ġare Ġdealing Ġwith Ġtextual Ġdata , Ġand Ġin Ġcontrast Ġto Ġtheir Ġsign - test Ġapproach , Ġwe Ġassign Ġties Ġbetween Ġaffinity Ġcandidate Ġstate Ġpairs ĠIN LINE FORM 0 Ġbased Ġon Ġrelatively Ġstrict Ġsimilarity Ġthresholds Ġbetween Ġ0 . 50 Ġand Ġ0 . 60 . ĠThe Ġresult Ġis Ġa Ġsquare ĠIN LINE FORM 1 Ġadj ac ency Ġmatrix ĠIN LINE FORM 2 Ġof Ġun weight ed Ġties Ġfor Ġeach Ġyear Ġwhich Ġcontains Ġall Ġstates Ġwho Ġvoted Ġand Ġdelivered Ġa ĠGD Ġstatement . ĠTo Ġlocate Ġmultiple x Ġbloc s , Ġwe Ġuse Ġthe Ġvoting Ġclusters Ġfound Ġby ĠPaul s Ġand ĠCran mer Ġ( ĠB IB REF 8 Ġ) Ġas Ġone Ġlayer , Ġand Ġthe Ġspeech Ġclusters Ġjust Ġdescribed Ġas Ġa Ġsecond Ġlayer . ĠThe ĠMult il ayer ĠExt raction Ġalgorithm Ġ( described Ġbelow ) Ġis Ġthen Ġapplied Ġto Ġthese Ġtwo Ġlayers , Ġwhich Ġreturns Ġcommunity Ġmembership Ġlabels Ġin Ġvectors . ĠThese Ġare Ġtransformed Ġinto Ġadj ac ency Ġmat rices Ġof Ġun weight ed Ġties Ġfor Ġeach Ġyear . ĠThis Ġis Ġa Ġstrong Ġtest Ġfor Ġour Ġhypothesized Ġeffects Ġbecause Ġmult il ayer Ġcommunities Ġmust Ġbe Ġdetectable Ġafter Ġindividual Ġlayers Ġhave Ġalready Ġbeen Ġclustered . </s> The Ġintuition Ġof Ġemploying Ġa Ġthreshold Ġis Ġthat Ġevery Ġnode Ġis Ġconnected Ġto Ġevery Ġother Ġnode Ġin Ġthe Ġnetwork Ġthrough Ġa Ġgiven Ġtext Ġsimilarity , Ġbut Ġthis Ġcomput ationally Ġbecomes Ġinf eas ible Ġand Ġunnecessary Ġas Ġmany Ġof Ġthe Ġties Ġare Ġlow Ġvalued . ĠFurther , Ġin Ġorder Ġto Ġbe Ġcompatible Ġwith Ġthe ĠTER G Ms , Ġthis Ġbin ar ization Ġis Ġnecessary , Ġand Ġso Ġwe Ġfollow Ġthreshold ing Ġguidelines Ġoutlined Ġin Ġthe Ġnetwork Ġscience Ġliterature , Ġsee ĠB IB REF 40 Ġ, ĠB IB REF 41 Ġ, ĠB IB REF 42 Ġ, ĠB IB REF 43 Ġ. ĠBecause Ġthe Ġchoice Ġof Ġthreshold Ġis Ġcase Ġdependent Ġand Ġmust Ġtake Ġinto Ġaccount Ġthe Ġtype Ġof Ġdata Ġunder Ġscrutiny , Ġwe Ġturn Ġto Ġthe ĠN LP Ġliterature , Ġwhich Ġsuggests Ġthat Ġa Ġthreshold Ġof ĠIN LINE FORM 0 Ġis Ġconsidered Ġto Ġbe Ġa Ġrelatively Ġcommon Ġbenchmark Ġfor Ġdisc arding Ġlow - information Ġsimilarity Ġscores ĠB IB REF 44 Ġ, ĠB IB REF 45 Ġ, ĠB IB REF 46 Ġ. ĠAs Ġdifferent Ġthresholds Ġresult Ġin Ġdifferent Ġgraphs , Ġwe Ġcheck Ġand Ġconfirm Ġthe Ġrobust ness Ġof Ġour Ġresults Ġat Ġdifferent Ġlevels Ġand Ġreport Ġthese Ġresults Ġbelow . </s> Cl usters Ġbased Ġon Ġspeeches Ġor Ġvotes Ġin Ġisolation Ġprovide Ġone Ġslice Ġof Ġinformation Ġabout Ġstate Ġpreferences , Ġbut Ġwe Ġare Ġmotivated Ġto Ġlocate Ġan Ġapproach Ġwhich Ġexploits Ġboth Ġsources Ġof Ġinformation Ġin Ġtandem . ĠThis Ġis Ġbroadly Ġaligned Ġwith Ġother Ġwork Ġin Ġpolitical Ġscience Ġwhich Ġaims Ġto Ġcombine Ġvotes Ġand Ġspeeches Ġin Ġstatistical Ġmodels . ĠSpecifically , Ġwe Ġposit Ġthat Ġvaluable Ġand Ġperhaps Ġdifferent Ġinformation Ġmight Ġbe Ġgarnered Ġfrom Ġthe Ġvoting Ġbehavior Ġand Ġspeeches Ġmade Ġby Ġstates Ġand Ġwe Ġconsider Ġthese Ġas Ġtwo Ġelementary Ġlayers Ġof Ġa Ġmult il ayer Ġnetwork . ĠA Ġtwo - layer Ġnetwork Ġillustration Ġis Ġpresented Ġin ĠFigure ĠFIG REF 32 Ġ. ĠExtract ing Ġcommunities Ġbased Ġon Ġthis Ġmult il ayer Ġnetwork Ġprovides Ġone Ġstrategy Ġof Ġhol istically Ġexploiting Ġboth Ġsources Ġof Ġpreference Ġinformation Ġin Ġtandem Ġand Ġis Ġone Ġof Ġthe Ġcurrent Ġforefront s Ġof Ġnetwork Ġscience Ġresearch ĠB IB REF 26 Ġ. </s> At Ġpresent , Ġmost Ġmult il ayer Ġcommunity Ġdetection Ġhe ur istics Ġrequire Ġthe Ġcommunity Ġstructure Ġto Ġbe Ġhom ogenous Ġat Ġeach Ġlayer , Ġbut Ġas Ġdisplayed Ġabove , Ġspeech Ġand Ġvoting Ġpatterns Ġoften Ġdiver ge . ĠA Ġrecently Ġproposed Ġsolution Ġto Ġthis Ġproblem Ġis Ġthe ĠMult il ayer ĠExt raction Ġprocedure ĠB IB REF 27 Ġ. ĠThe Ġalgorithm Ġidentifies Ġdensely Ġconnected Ġvertex - l ayers Ġin Ġmult il ayer Ġnetworks Ġthrough Ġa Ġsignificance - based Ġscore Ġthat Ġquant ifies Ġthe Ġconnectivity Ġof Ġan Ġobserved Ġvertex - layer Ġset Ġby Ġcomparison Ġwith Ġa Ġmult il ayer Ġfixed Ġdegree Ġrandom Ġgraph Ġmodel . ĠFor Ġour Ġanalysis , Ġthe Ġclusters Ġfrom Ġvoting Ġdata Ġcomprise Ġthe Ġfirst Ġlayer Ġand Ġthe Ġtext - based Ġclusters Ġcomprise Ġthe Ġsecond Ġlayer Ġof Ġthe Ġgraph Ġfor Ġeach Ġyear . </s> This Ġunique Ġapproach Ġto Ġcommunity Ġdetection Ġallows Ġus Ġto Ġexploit Ġpreference Ġsimilarity Ġinformation Ġacross Ġspeeches Ġand Ġvoting Ġhabits Ġwithout Ġrequiring Ġstates Ġto Ġbe Ġsimilar Ġon Ġboth Ġdimensions . ĠForm ally , Ġour Ġvote - speech Ġlayered Ġnetwork Ġis Ġa Ġnode - aligned ĠIN LINE FORM 0 Ġund irect ed Ġmult il ayer Ġnetwork ĠIN LINE FORM 1 Ġwith Ġno Ġself - ed ges ĠIN LINE FORM 2 Ġ. ĠThe Ġmult il ayer Ġnetwork Ġis Ġnode Ġaligned Ġbecause Ġwe Ġadd Ġextr aneous Ġnodes Ġsuch Ġthat ĠIN LINE FORM 3 Ġwhere Ġtypically Ġthe ĠIN LINE FORM 4 Ġ. ĠSuch Ġan Ġapproach Ġis Ġshown Ġin Ġthe Ġliterature Ġto Ġyield Ġmat hematically Ġdesirable Ġproperties Ġfor Ġthe Ġtask Ġof Ġmult il ayer Ġcommunity Ġdetection ĠB IB REF 26 Ġand Ġis Ġalso Ġrequired Ġfor Ġthe ĠTER GM Ġestim ations . </s> The ĠMult il ayer ĠExt raction Ġprocedure Ġloc ates Ġcommunities Ġacross Ġthe Ġtwo Ġnetwork Ġlayers Ġand Ġprovides Ġa Ġresulting Ġmembership Ġidentification Ġvector Ġwhich Ġcan Ġbe Ġused Ġto Ġconstruct Ġa Ġone - mode Ġrepresentation Ġof Ġthe Ġcross - layer Ġcommunities Ġfor Ġa Ġgiven Ġyear Ġwhere Ġan Ġedge Ġbetween ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġexists Ġif Ġthe Ġextraction Ġprocedure Ġidentifies ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġas Ġbelonging Ġto Ġthe Ġsame Ġcommunity . ĠIf Ġthe Ġextraction Ġprocedure Ġfails Ġto Ġlocate Ġmult il ayer Ġcommunities , Ġwe Ġcan Ġconclude Ġthat Ġno Ġinformation Ġis Ġlost Ġby Ġusing Ġthe Ġmore Ġcommon Ġaggregation Ġprocedure Ġof Ġconsidering Ġthe Ġunion Ġof Ġedges Ġin Ġthe Ġtwo Ġlayers ĠIN LINE FORM 4 Ġand Ġconsider Ġan Ġedge Ġto Ġexist Ġbetween ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġif Ġan Ġedge Ġexists Ġin Ġeither Ġof Ġthe Ġtwo Ġlayers , Ġsee ĠB IB REF 26 Ġ. ĠThe Ġresult Ġis Ġa Ġone Ġmode Ġnetwork Ġfor Ġeach Ġyear Ġbased Ġon Ġthe Ġtwo Ġsources Ġof Ġpreference Ġinformation . ĠThese Ġmultiple x Ġbloc s Ġare Ġemployed Ġas Ġa Ġsubstitute Ġfor Ġvotes - Ġand Ġtext - based Ġclusters Ġin ĠModel Ġ4 , Ġand Ġthe Ġresults Ġare Ġreported Ġin Ġ[ table : ter gm ] Table Ġ1 Ġof Ġthe Ġmain Ġpaper . </s> For Ġthe Ġresults Ġreported Ġin Ġthe Ġpaper 's Ġmain Ġbody , Ġwe Ġfound Ġthat ĠMult il ayer ĠExt raction Ġdetected Ġheter ogenous Ġcommunity Ġstructure Ġin Ġthe Ġtwo Ġlayers Ġof Ġspeeches Ġand Ġvotes Ġfor Ġ12 Ġof Ġthe Ġyears . ĠFor Ġthe Ġother Ġ18 Ġyears , Ġno Ġmult il ayer Ġcommunities Ġwere Ġdetected Ġand Ġtherefore Ġcould Ġbe Ġaggreg ated Ġinto Ġa Ġsingle Ġnetwork Ġwithout Ġthe Ġrisk Ġof Ġignoring Ġmult il ayer Ġcommunity Ġstructure Ġin Ġthe Ġanalysis . ĠThis Ġhelps Ġto Ġexplain Ġwhy Ġsome Ġfindings Ġusing ĠUN Ġroll Ġcall Ġdata Ġalone Ġcome Ġup Ġwith Ġvarying Ġconclusions . ĠRoll Ġcall Ġdata Ġwould Ġperform Ġbetter Ġin Ġyears Ġwhere Ġcomplex Ġspeech - vote Ġdependencies Ġdid Ġnot Ġexist Ġ( i . e . Ġwhen Ġa Ġgiven Ġstate 's Ġvotes Ġand Ġspeeches Ġare Ġboth ĠâĢ ľ similar " Ġand Ġtherefore Ġconsidering Ġone Ġsource Ġof Ġinformation Ġalone Ġis Ġanaly t ically Ġsufficient ). ĠHowever , Ġin Ġyears Ġwhere Ġa Ġgiven Ġstate 's Ġvoting Ġbehavior Ġand Ġspeeches Ġdiver ge Ġ( e . g . Ġa ĠNATO Ġmember Ġvoting Ġwith Ġthe Ġbloc Ġbut Ġdelivering Ġa Ġspeech Ġwhich Ġcontains Ġposition Ġinformation Ġwhich Ġdiver ges Ġfrom Ġthe Ġrest Ġof Ġthe Ġbloc ), Ġthen Ġfavoring Ġone Ġsource Ġof Ġinformation Ġover Ġthe Ġother Ġwill Ġlikely Ġyield Ġmisleading Ġestimates Ġof Ġpreference Ġpolarization . ĠThis Ġmult il ayer Ġcommunity Ġdetection Ġapproach Ġallows Ġus Ġto Ġcapture Ġthese Ġpotentially Ġcomplex Ġstructures Ġat Ġboth Ġlevels Ġof Ġpreference Ġinformation . </s> In Ġthe Ġbody Ġof Ġthe Ġmain Ġpaper , Ġwe Ġpresent Ġthe Ġresults Ġof Ġthe Ġoriginal Ġmodel Ġand Ġdate - adjusted Ġmodels Ġof ĠB IB REF 8 Ġ, Ġas Ġwell Ġas Ġour Ġtextual Ġand Ġmultiple x Ġcluster Ġresults . ĠThe Ġreaders Ġcan Ġrefer Ġto Ġthe Ġoriginal Ġpaper Ġof ĠB IB REF 8 Ġfor Ġcomplete Ġdetails Ġon Ġvariable Ġoperational ization Ġand Ġmodel Ġspecification . ĠWe Ġfocus Ġhere Ġon Ġthe Ġperformance Ġof Ġthe Ġmodels . </s> Incre asing Ġattention Ġis Ġdevoted Ġto Ġthe Ġappropriate Ġrole Ġof Ġun super vised Ġmethods Ġin Ġpolitical Ġscience Ġresearch , Ġbecause Ġthey Ġrequire Ġhuman Ġinterpretation Ġand Ġlack Ġwell - defined Ġcriteria Ġfor Ġaccuracy Ġand Ġperformance Ġassessment . ĠBoth Ġthe Ġword Ġembed d ings Ġand Ġmult il ayer Ġcommunity Ġdetection Ġprocedure Ġare Ġun super vised Ġmethods , Ġand Ġso Ġwe Ġposit Ġthat Ġin Ġorder Ġto Ġvalidate Ġthat Ġthese Ġlocated Ġaffinity Ġbloc s Ġreally Ġdo Ġcapture Ġmeaningful Ġpreference Ġsimilarity Ġin Ġinternational Ġpolitics , Ġthey Ġshould Ġbe Ġable Ġto Ġimprove Ġupon Ġour Ġcurrent Ġability Ġto Ġpredict Ġconflict Ġonset Ġout - of - sample . ĠThis Ġwould Ġenable Ġus Ġto Ġmore Ġadequately Ġexplain Ġobserved Ġstate Ġbehavior Ġand Ġprovides Ġa Ġrigorous Ġcriteria Ġfor Ġperformance Ġassessment . ĠAs Ġreported Ġin Ġthe Ġmain Ġpaper , Ġclusters Ġbased Ġon Ġpartitions Ġof Ġour Ġspeech Ġgraph Ġand Ġour Ġspeech - vote Ġmultiple x Ġgraph Ġare Ġboth Ġstatistically Ġsignificant Ġpredict ors Ġof Ġconflict Ġonset . ĠHere , Ġwe Ġassess Ġthe Ġgoodness - of - fit Ġof Ġour Ġmodels , Ġthe Ġout - of - sample Ġpredictive Ġaccuracy Ġof Ġthe Ġmodels , Ġand Ġrobust ness Ġchecks Ġof Ġour Ġresults Ġat Ġdifferent Ġtie Ġsimilarity Ġthresholds . </s> To Ġassess Ġthe Ġin - sample Ġgoodness - of - fit Ġ( GO F ) Ġof Ġexponential Ġrandom Ġgraph Ġmodels , Ġit Ġis Ġcommon Ġto Ġsimulate Ġnetwork Ġstatistics Ġwhich Ġwere Ġnot Ġspecified Ġin Ġthe Ġmodel Ġto Ġsee Ġhow Ġwell Ġthe Ġfitted Ġmodel Ġcan Ġsimulate Ġstatistics Ġon Ġthe Ġoutcome Ġnetwork . ĠIn Ġour Ġcase , Ġthe Ġoutcome Ġnetwork Ġis Ġthe Ġconflict Ġonset Ġnetwork , Ġand Ġwe Ġperform Ġ50 Ġsimulations Ġover Ġeach Ġof Ġthe Ġ30 Ġtime Ġsteps Ġfor Ġ1 , 500 Ġtotal Ġsimulations Ġfor Ġeach Ġof Ġthe Ġfour Ġmodels . ĠThe Ġin - sample Ġareas Ġunder Ġthe ĠR OC Ġand ĠPR Ġcurves Ġfor Ġthe Ġthree Ġmodels Ġof Ġprimary Ġconcern Ġare Ġpresented Ġin ĠFigure ĠFIG REF 36 Ġand ĠFigure ĠFIG REF 37 Ġ, Ġrespectively . ĠFurthermore , Ġthe Ġdy ad - wise Ġand Ġedge - wise Ġshared Ġpartners Ġand Ġmodular ities Ġare Ġpresented Ġfor Ġall Ġfour Ġmodels Ġin ĠFigure ĠFIG REF 38 Ġand ĠFigure ĠFIG REF 39 Ġ. ĠAlthough Ġthe Ġoriginal Ġmodels Ġfrom ĠB IB REF 8 Ġand Ġour Ġtextual Ġand Ġmultiple x Ġmodels Ġall Ġexhibit Ġimpressive ĠGO Fs , Ġthe Ġmultiple x Ġmodel Ġexhibits Ġthe Ġbest Ġin - sample ĠGO F Ġas Ġmeasured Ġby Ġareas Ġunder Ġthe ĠR OC Ġand ĠPR Ġcurves . ĠThis Ġincreases Ġour Ġconfidence Ġin Ġthe Ġmodel Ġspecifications , Ġbut Ġit Ġis Ġnecessary Ġto Ġassess Ġout - of - sample Ġpredictive Ġcapability Ġsince Ġall Ġfour Ġmodels Ġfit Ġquite Ġwell . </s> To Ġassess Ġwhether Ġor Ġnot Ġthe Ġinclusion Ġof Ġtextual Ġor Ġmultiple x Ġclusters Ġimproves Ġupon Ġthe Ġexisting Ġmodels Ġwith Ġroll Ġcall - based Ġclusters Ġalone , Ġwe Ġfollow ĠB IB REF 8 Ġin Ġtraining Ġon Ġfive - year Ġwindows Ġand Ġattempt Ġto Ġpredict Ġthe Ġnext Ġyear Ġof Ġconflict Ġonset . ĠThe Ġmultiple x Ġmodel Ġexhibits Ġa Ġ20 . 5 % Ġincrease Ġin Ġarea Ġunder Ġthe Ġprecision Ġrecall Ġcurve Ġcompared Ġto Ġthe Ġoriginal Ġdate - adjusted Ġmodel Ġ( 1 . 156 Ġvs . Ġ0 . 9 59 ). ĠIn Ġcontrast , Ġthe Ġmodel Ġwith Ġtextual Ġclusters Ġalone Ġunder per forms Ġthe Ġoriginal Ġdate - adjusted Ġmodel Ġ( 0 . 08 1 Ġvs . Ġ0 . 9 59 ). ĠWe Ġplot Ġthese Ġout - of - sample Ġperformance Ġresults Ġfor Ġthe Ġmultiple x Ġmodels Ġalongside Ġother Ġtie Ġthresholds Ġas Ġchecks Ġon Ġthe Ġstability Ġof Ġthe Ġresults Ġin ĠFigure ĠFIG REF 40 Ġwhich Ġdisplays Ġthe Ġsum Ġof Ġthe Ġareas Ġunder Ġthe Ġprecision Ġrecall Ġcurves Ġat Ġvarious Ġthresholds Ġcompared Ġto Ġthe Ġbaseline Ġ( date - adjusted ) Ġmodel Ġpresented Ġin ĠB IB REF 8 Ġ. </s> We Ġfind Ġthat Ġseveral Ġof Ġthe Ġthresholds Ġprovide Ġreliable Ġand Ġstatistically Ġsignificant Ġestimates Ġof Ġconflict Ġonset . ĠIn Ġorder Ġto Ġchoose Ġthe Ġfinal Ġmodel , Ġwe Ġselect Ġthe Ġmodel Ġwith Ġthe Ġlowest Ġvariance Ġin Ġout - of - sample Ġprediction Ġcapability Ġ( i . e . Ġthe Ġarea Ġunder Ġthe Ġprecision Ġrecall Ġcurve ), Ġthat Ġis , Ġwe Ġselect Ġthe Ġmodel Ġwhich Ġyields Ġthe Ġmost Ġconsistent Ġpredictions Ġover Ġtime . ĠBecause Ġout - of - sample Ġpredictions Ġon Ġsparse Ġnetworks Ġ( such Ġas Ġthe Ġconflict Ġonset Ġnetwork ) Ġis Ġa Ġchallenging Ġtask , Ġthis Ġselection Ġapproach Ġhelps Ġto Ġeliminate Ġmodels Ġwith Ġpredictions Ġthat Ġvary Ġwidely Ġfrom Ġone Ġyear Ġto Ġthe Ġnext . ĠAs Ġpreviously Ġmentioned , Ġa Ġthreshold Ġof Ġ. 50 Ġis Ġcommon Ġin Ġthe ĠN LP Ġliterature Ġand Ġso Ġwe Ġreport Ġevery Ġthreshold Ġbetween Ġ[ 0 . 50 , 0 . 60 ] Ġat Ġ. 01 Ġincrements . ĠWe Ġselect Ġthe Ġ. 58 Ġtie Ġthreshold Ġbecause Ġthis Ġmodel Ġexhibits Ġthe Ġlowest Ġvariance Ġin Ġpredictions . </s> To Ġensure Ġthat Ġwe Ġare Ġnot Ġselecting Ġa Ġsingle Ġmodel Ġwhich Ġvastly Ġoutper forms Ġall Ġother Ġthresholds Ġ( i . e . Ġto Ġensure Ġthat Ġour Ġresults Ġare Ġrobust ), Ġwe Ġconsider Ġthe Ġout - of - sample Ġpredictive Ġaccuracy Ġfor Ġthe Ġmodels Ġin Ġwhich Ġthe Ġmultiple x Ġclusters Ġyielded Ġa Ġstatistically Ġsignificant Ġrelationship Ġwith Ġconflict Ġonset . ĠThis Ġwas Ġthe Ġcase Ġfor Ġ7 Ġout Ġof Ġthe Ġ11 Ġthresholds Ġtested . ĠThe Ġsums Ġof Ġthe Ġareas Ġunder Ġthe Ġprecision Ġrecall Ġcurve Ġfor Ġthese Ġmodels Ġare Ġplotted Ġin ĠFigure ĠFIG REF 41 Ġ. ĠThese Ġbox Ġplots Ġmake Ġclear Ġthat Ġour Ġmultiple x Ġmodels Ġdo Ġindeed Ġdisplay Ġincreased Ġout - of - sample Ġpredictive Ġcapability Ġrelative Ġto Ġthe Ġbaseline Ġmodel Ġacross Ġvarious Ġthresholds . </s> The Ġabove Ġamounts Ġto Ġa Ġtool kit Ġfor Ġthe Ġexploitation Ġof Ġmult id imensional Ġinformation Ġon Ġpositions Ġand Ġpreferences Ġin Ġpolitical Ġresearch . ĠWe Ġshow Ġhow Ġthe Ġinformation Ġin Ġtextual Ġdata Ġcan Ġbe Ġuse fully Ġexploited Ġbeyond Ġsimple Ġword Ġcounts Ġand Ġweighted Ġfrequencies . ĠWe Ġshow Ġhow Ġthis Ġinformation Ġcan Ġin Ġits Ġown Ġright Ġbe Ġuseful , Ġbut Ġalso Ġhow Ġit Ġcan Ġbe Ġexploited Ġin Ġtandem Ġwith Ġother Ġexisting Ġsources Ġof Ġpreference Ġinformation Ġlike Ġvotes Ġthrough Ġa Ġmult il ayer Ġnetwork Ġapproach . ĠFinally , Ġwe Ġshow Ġthat Ġthese Ġmeasures Ġare Ġnot Ġonly Ġsubstant ively Ġreasonable , Ġbut Ġcan Ġbe Ġused Ġto Ġextend Ġcurrent Ġstate - of - the - art Ġnetwork Ġmodels Ġwhich Ġinfer Ġthe Ġimpact Ġof Ġpreferences Ġon Ġinternational Ġconflict .
Introduction The polarization of actors' expressed preferences is a fundamental concern for studies of legislatures, court systems, and international politics BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Because preferences are unobservable, scholars must look for signals in the empirical world. Recent progress has been made in parliamentary and court settings through the employment of textual data BIBREF4 and votes and texts in tandem BIBREF5 , BIBREF6 . Many of these advances rely on spatial, scaling, and item-response type models that are intuitive for settings where a small number of parties or ideological divisions influence outcomes. This is less intuitive for the study of state preferences, because international relations is marked by multiple dimensions that span ideological, economic, and security concerns, among others BIBREF0 . This paper introduces a new approach to estimate preference polarization in multidimensional settings using texts and votes. First, a distributional representation of textual data is utilized to retain qualities of human speech that are otherwise discarded by a bag-of-words approach. Second, community detection in multiplex networks is used to uncover preference affinity blocs across multiple layers of votes and speeches. Just as scaling and spatial models attempt to explain variance along one or a few reduced dimensions, our approach identifies densely connected communities based on preference similarity that are important for explaining variations in observed outcomes. We illustrate the utility of this approach with an empirical test of a core hypothesis in International Relations (IR): militarized conflict is less likely between states with more similar preferences BIBREF7 . Specifically, we extend a recently published network model of conflict onset. BIBREF8 utilize temporal exponential random graph models to infer the relationship between conflict onset and a battery of predictors, one of which is affinity communities located via spectral clustering on a graph of UN votes. This covariate provides a natural comparison to examine whether our proposed clustering approach can improve our ability to model conflict onset, as measured by out-of-sample predictive accuracy. We find that multiplex clusters based on country speeches and votes – which we refer to as affinity blocs – outperform clusters based on votes or speeches alone. The proposed framework enables IR scholars to better explain behavioral outcomes in international politics, and will be of use to any scholar interested in the measurement of preference polarization in multidimensional settings. Political Polarization: Measurements and Models Polarization in IR is defined as “the degree to which the foreign policies of nations within a single cluster are similar to each other, and the degree to which the foreign policies of nations in different clusters are dissimilar" BIBREF9 . Therefore, operationalizing a concept of preference polarization broadly involves two steps: an approach to estimate preferences from available data on states' observable behavior; and a method of detecting distinct communities of nations, such that nations belonging to the same community share similar preferences, and nations belonging to different communities have dissimilar preferences. UN votes and speeches The most widely used source for deriving preferences in IR is UN roll call data BIBREF10 . Voting behavior represent a valuable source of revealed preference information, comparable across states and over time. However, UN roll call votes tend to be a weak signal of underlying preferences in cases where states vote for ceremonial purposes, are constrained by agenda-setting power dynamics, or vote as cohorts to maximize their impact within the UN, such as with regional blocs BIBREF11 . Similar limitations exist in the study of polarization in national legislatures where actors' votes seldom diverge from party lines. In response, an emerging literature turns to actors' speeches to better capture expressed positions and to measure polarization of these positions BIBREF5 , BIBREF6 . The employment of text data for the measurement of state preferences in world politics is intuitive, because outcomes are a function of multiple issue dimensions, such as topics ranging from human rights to nuclear proliferation policy. In particular, states' annual addresses in the UN General Debate (GD) provide a valuable source of data on state preferences. Governments use their annual GD speeches to discuss their positions on the issues in international politics they consider most important. As states face few institutional constraints during these speeches, they can express their positions on a wider range of issues compared to votes on agenda-set items. An example of this difference between votes and speeches can be seen in the case of Greece and Turkey in 1974. Both countries were NATO members, however, Turkey's invasion of Cyprus in July of 1974 led to heightened tension and hostilities between the two nations. This included Turkish and Greek fighter jets engaging in a dogfight which resulted in the death of a Turkish pilot. Yet, the ideal points of Greece and Turkey based on UN votes that year were the most similar among NATO member states (0.68 and 0.42, respectively). While their votes indicate that they have broadly similar foreign policy preferences and provide useful signals of membership on an alliance dimension (e.g. they are spatially distant from Warsaw Pact members in 1974), they fail to reflect the significant tension between the countries. In contrast, their speeches in the 1974 General Debate clearly reveal the hostility between the two nations. Both Greece and Turkey discussed the Cyprus invasion at length in their speeches, with each blaming the crisis on the other. We draw on a recently released corpus of state speeches delivered during the annual UN General Debate that provides the first dataset of textual output from states that is recorded at regular time-series intervals and includes a sample of all countries that deliver speeches BIBREF11 . There are limitations to both votes and speeches in the UN in deriving estimates of states' underlying preferences. However, it is not controversial to suggest that state speeches can valuably complement roll call data, and the use of speeches and votes together can reveal useful preference information beyond that contained in states' voting behavior or GD speeches alone. The question, rather, is how best to represent these texts and how best to theoretically model these data in tandem. Word embeddings In order to use texts together with votes to estimate preference polarization, we first consider how to better exploit the information contained in textual data. To do this, we introduce a new representation of textual data which more adequately captures dynamics of human language, namely unsupervised learned word embeddings. In the broader natural language processing (NLP) literature, there has been a surge of research devoted to the development of distributional representations of speech which retain syntactical language qualities in ways that the bag-of-words (BOW) approach typically used in political text analysis research is not equipped to retain. The hypothesis claims that words that occur in similar contexts tend to have similar meanings BIBREF12 . When operationalized, the unique intuition is that similar words and phrases, such as “atomic, weapons” and “nuclear, warheads” are found in relatively proximate vector space locations. Although the BOW performs surprisingly well, this example has no features in common, and a BOW representation would assign low similarity scores or high distances. When results are projected onto a two dimensional space, language relationships surface, such as the clustering of synonyms, antonyms, scales (e.g. democracy to authoritarianism), hyponym-hypernyms (e.g. democracy is a type of regime), co-hyponyms (e.g. atom bombs and ballistic missiles are types of weapons), and groups of words which tend to appear in similar contexts like diplomat, envoy, and embassy. Mikolov and collaborators introduce an evaluation scheme based on word analogies that examines dimensions of difference in vector space BIBREF13 , BIBREF14 . They originally reached the surprising conclusion that simple vector addition and subtraction uncovers interesting linear substructures of human language, famously that INLINEFORM0 . To locate vector space representations of our corpus, we utilize the Stanford NLP group's Global Vectors for Word Representation (GloVe) unsupervised learning algorithm BIBREF15 . GloVe is a popular log bilinear, weighted least squares model that trains on global word-word co-occurence counts to make efficient use of the corpus statistics. Because it factorizes a word-context co-occurrence matrix, it is closer to traditional count methods like latent semantic analysis or principle component analysis. Here, we present three analogical examples from the located embeddings: DISPLAYFORM0 where each INLINEFORM0 describes a vector space location of the given feature, and the cosine similarity between each vector space location is added or subtracted to find the closest vector offsets (with cosine similarity printed underneath). These analogies are interpreted, for example, as INLINEFORM1 is to INLINEFORM2 as INLINEFORM3 is to INLINEFORM4 . These examples appear to encode relations of cause-effect, threats/harm, and location/geography, respectively. As found in the wider NLP literature, the implication is that these vector space models are surprisingly effective at capturing different lexical relations, despite the lack of supervision. To measure position similarities, we apply a new document-level distance measure to the embeddings: the (relaxed) Word Mover's Distance (RWMD) BIBREF17 . RWMD is described in greater detail in the Supplementary Materials, but in short, this measures the cumulative distance required to transform one state's speech point cloud into that of another state, ensuring that differences do not simply reflect the use of different words. States employ varied language and lexical patterns to describe similar topics. For example, if state A says “nuclear weapons are bad," and state B says “atom bombs are terrible," the only feature in common is the term “are," which leads to near-orthogonality in their BOW vectors and low similarity scores. If a third state C says “atom bombs are good," then B and C would exhibit the highest cosine similarity of the three, despite having the opposite expressed policy positions. Word embeddings and term-document matrices are located for each year in the corpus, 1970-2014, and state dyad RWMD distances are calculated, converted to similarity scores, and stored in an INLINEFORM1 matrix INLINEFORM2 for each year. For texts to be considered as a useful complement to roll call data, we should see differences in the positions expressed in speeches versus votes. This would indicate that the two sources reveal different preference information and that using one over the other risks overlooking available preference signals. Further, for the proposed word embeddings approach to be considered useful, it should provide greater insight into intra-bloc position variation beyond what is available in the BOW, because intra-bloc variation is an important component of the definition of polarization used here, as well as in much of the recent text versus votes literature. The case of NATO and Warsaw Pact members is presented in [fig:diffs]Figure 1, which plots states' ideal points from BIBREF21 , BOW cosine similarities, and RWMD similarities, with the latter two scaled between INLINEFORM0 . Visual assessment of intra-bloc similarities indicates that the RWMDs based on word embeddings yield higher variations in expressed positions compared to the cosine similarities between members' BOW vectors. This provides preliminary confirmation that the suggested word embeddings approach captures more interesting variation in the preferences of states compared to the BOW. Furthermore, while the ideal points clearly reflect a lessening of Cold War tensions, the RWMD similarities appear to detect greater intra-bloc position variations. Therefore, both appear to provide different and potentially valuable information for the estimation of state preferences. Multiplex networks and community detection Having outlined a novel approach to estimate states' preference similarity, the second step is to detect distinct communities of states. The network science literature on community detection is especially well-suited for this task. One common measure is modularity, a community detection heuristic that partitions a network such that the total number of intra-community edges is maximized relative to a baseline expectation from a null model (i.e. a random graph) BIBREF22 . The intuition is that a community should have more or stronger ties among the actors within the community compared to ties with actors in other communities. This common approach, however, can be misleading in dense networks, such as vote and speech similarity. Indeed, BIBREF22 use UN voting data to illustrate the challenges related to network clustering on data with high levels of agreement between observations. To overcome this issue with density and to exploit the information found in votes and speeches in tandem, we instead turn to multiplex network community detection. Multilayer graphs consist of more than one layer and permit the search for communities across graph levels. Community detection methods for multilayer graphs, though, are still in their infancy and most current approaches posit the same community structure at different levels of the multigraph. As [fig:diffs]Figure 1 shows, however, votes and speeches appear to exhibit rather heterogeneous structures. A recently proposed solution for this task is the Multilayer Extraction procedure BIBREF27 . The algorithm identifies densely connected vertex-layers in multilayer networks through a significance-based score that quantifies the connectivity of an observed vertex-layer set by comparison with a multilayer fixed degree random graph model. For our analysis, the clusters from voting data comprise one layer and the text-based clusters comprise the second layer of the graph for each year. The Supplementary Materials describes the data manipulation steps in detail, but in short, we follow Pauls and Cranmer ( BIBREF8 ) in performing 5-nearest neighbor clustering on the matrix of state speech similarities to find candidates for affinity communities and then assign ties above thresholds. These text-based clusters and their vote-based clusters are then used as two layers of a multilayer network, and the Multilayer Extraction algorithm is used to detect affinity blocs across the two layers. This process is illustrated in [fig:plots]Figure 2, which displays (a) the vote and text similarity matrices, (b) the single layer vote and speech clusters, and (c) the multiplex affinity blocs located across both layers. Empirical Application: Affinity Blocs and Conflict Onset A recently published network conflict onset model provides an ideal test for our proposed multilayer affinity blocs. BIBREF8 use spectral clustering to identify densely connected affinity communities based on UN roll call data and employ these clusters in temporal exponential random graph models (TERGMs) to infer the relationship between violent conflict onset and affinity communities in the UNGA, among other covariates of interest. The outcome network of interest is constructed from conflict onset data from the Correlates of War (COW) project's Militarized Interstate Dispute (MID) dataset (v4.1) BIBREF30 . The time period for the model we replicate spans 1965-2000, whereas our corpus of speeches covers 1970-2014. We limit our extension to the years of overlap, 1970-2000. Our inferential analysis first replicates their full conflict onset model BIBREF8 , which we successfully do. This same model is estimated over the constrained time range of our analysis to ensure that significance and direction of the coefficients on the covariates do not substantially differ. We find that the signs remain the same and the coefficients do not change dramatically. These results are reported in [table:tergm]Table 1 as Model 1 and 2, respectively. We then use speech and multiplex blocs as substitutes for the original vote clusters and report these as Model 3 and 4, respectively. The results of Model 3 in [table:tergm]Table 1 indicate that the text-based clusters exhibit a noticeably larger effect of preference similarity on conflict avoidance compared to the vote-based clusters. Interestingly, the significance of joint democracy falls off, as well as the effects of the security, trade, and economic dependency networks. This implies that membership in text-based affinity communities is associated with a substantial decrease in the likelihood of violent conflict onset and is a much larger effect compared to other covariates of interest. This stark difference between the votes- and speeches-cluster coefficients provides further indication of underlying heterogeneity in the network graphs. Both indicate that membership in affinity communities is associated with a decrease in the likelihood of conflict onset but appear to capture different manifestations of latent preferences. The multiplex model displays coefficients closer to Models 1 and 2. The multiplex bloc indicates that membership in affinity communities as located across vote and speech graphs is associated with a decrease in the likelihood that a given pair of states will engage in armed conflict. To increase confidence in these results, however, we follow BIBREF8 in the assessment of out-of-sample predictive accuracy by training models on five year windows and then assessing predictions on the next year. The areas under the precision recall curves are then summed over the entire date range. The predictive capability of Model 3 outperforms their paper's baseline model with no preference networks but underperforms their date-adjusted model as measured by area under the precision recall curve (0.081 vs. 0.959). The multiplex model, however, outperforms their baseline paper model (with and without clusters), the date-adjusted model, and our textual extension model. The multiplex model exhibits a 20.5% increase in area under the precision recall curve compared to the original date-adjusted model (1.156 vs. 0.959). Constructing affinity blocs based on texts and votes in tandem thus leads to a more substantively intuitive model, as well as increased predictive performance. Although this is a relatively large gain in predictive accuracy, these substantively small quantities confirm that the prediction of violent conflict onset remains an enduring challenge for scholars of IR. Nonetheless, the ability to exploit revealed preference information in speeches and votes in tandem appears to promise fruitful potential gains in terms of methodological capability and theoretical soundness. Conclusion This paper introduces a novel approach to estimate preference polarization in multidimensional settings using votes and textual data, based on developments in the natural language processing and network science literatures. The approach helps to better exploit information found in textual data, and to locate dense clusters in complex and multilayered networks in ways that were previously not computationally possible. Drawing on a new dataset of state speeches in the UN General Debate, together with voting data from the UNGA, these tools were employed to better estimate revealed state preferences in international politics and to locate preference affinity blocs which exist across multiple layers of speech and roll call networks. It is perhaps worth noting that these sources are relatively weak signals of true state preferences and propensity to engage in armed conflict. However, these methods significantly improve our ability to identify meaningful patterns of preference similarity amongst the noise. Furthermore, the approach can assist any political study that seeks to measure position similarities from textual data and detect dense clusters of affinity or antagonism across multiple relational datasets. These might include social media actors who operate across multiple platforms, as well as contexts like legislatures where complex relations exist across votes, speeches, committee memberships, and others. Therefore, the approach presented in this paper will be useful to all scholars broadly seeking to measure political preferences and polarization in multidimensional contexts. Supplementary Materials The following material complements the analysis in the main text. To our knowledge, this paper presents the first political science application of vector space representations of textual data, the Word Mover's Distance, and the Multilayer Extraction procedure. The Supplementary Materials are structured as follows. First, the data are discussed and applications which use a bag-of-words treatment are presented. Second, the vector space approach and distance measure are introduced and discussed. Finally, the network clustering approach and model evaluations are presented. Data and Measurements We draw on the newly released UN General Debate Corpus BIBREF11 which contains every country statement in the UN General Debate between 1970 and 2014. The General Debate (GD) takes place every September at the start of each new session of the UN General Assembly (UNGA). It provides all member states with the opportunity to address the UNGA and to present their perspective on key issues in world politics. Governments use their GD statements to put on the record their position on events that have occurred during the past year and on longer-term underlying issues in world politics related to issues such as conflict, terrorism, development, human rights, and climate change. A principal difference between GD statements and UNGA voting is that the GD statements are not institutionally connected to decision-making in the UN. As a result, governments are free to discuss the issues they consider to be of greatest importance in world politics, regardless of whether an issue is on the formal agenda of the UNGA. Therefore, as BIBREF32 notes, the General Debate acts “as a barometer of international opinion on important issues, even those not on the agenda for that particular session.” In providing information about states' preferences on world politics, the GD provides a valuable data source for measuring polarization in International Relations. In addition to being the one major forum where states present their views on international politics free from external constraints, the fact that it takes place annually and includes all UN member states enables comparison over time and across countries. Readers are encouraged to consult BIBREF11 for a comprehensive introduction to the corpus. As mentioned in the main text, we discuss an example where disagreement is obvious in states' GD speeches but less obvious in their voting behavior. Consider the following brief excerpts from the GD speeches of Greece and Turkey in 1974. Greece: On 15 July a coup, condemned by all of us, was staged to overthrow Archbishop Makarios, the legitimate, elected President of the Republic. This coup was not directed against the Turkish Cypriot community of the island... During the fighting while the coup was in progress, not a single Turkish Cypriot was killed or injured. Yet five days later, large Turkish invasion forces were landing in Cyprus and the Turkish Air Force was launching indiscriminate attacks against unarmed civilians, under the flimsy pretext of protecting the Turkish Cypriot minority on the island, which, I repeat, had not been harmed in any way... Two hours later, the Turkish troops were on the move again, sowing death and destruction, killing United Nations troops, bombing hospitals and schools. Repeated cease-fire calls by the Security Council went unheeded. Turkey even ignored the ceasefire proclaimed by its own Prime Minister on 16 August 1974. Turkey: Turkey has constantly had to face faits accomplis of increasingly serious scope, particularly since 1963. The most recent and the most serious of these faits accomplis was, as we all know, that of 15 July last: a foreign Power undertook a coup d'etat which it had long been fomenting and the purpose of which was to annex the island... The coup d'etat of 15 July was directed precisely against the Turkish community and was directly aimed at the annexation of the island to Greece... I have not, however, finished correcting all the false allegations and baseless charges made by my colleague. I reserve the right to do so when we speak on this matter before the General Assembly. My Greek colleague's speech, unfortunately, shows the nature of the atmosphere in which the debate will take place on the future of the two communities, Turkish and Greek, in the island. The two representatives are outlining their positions on the controversy related to the Turkish invasion of Cyprus. Expressed disagreement on this topic is clearly present in the speeches, but as mentioned in the main paper, the two countries' voting ideal points for that year at the most similar amongst all NATO members. A further example is illustrated in the speeches and voting habits of India and Pakistan in 1999, the year the two countries went to war (the Kargil War). Consider the following excerpts from their General Debate statements that year: Pakistan: The Kargil crisis was a manifestation of the deeper malaise spawned by the unresolved Kashmir problem and India's escalating repression of the Kashmiri people. India launched a massive military operation in Kargil and threatened a wider conflict by mobilizing its armed forces all along the Pakistan-India international border. Pakistan acted with restraint... India's repression in Jammu and Kashmir has killed thousands of Kashmiris, forced hundreds of thousands into exile, led to three wars between Pakistan and India and consigned the two countries to a relationship of endemic conflict and mistrust. India: Premeditated aggression by regular forces was committed against India. Not simply was the Lahore Declaration violated, but so was the Simla Agreement, which had prevented conflict for more than a quarter of a century. In self-defence, yet with the utmost restraint, India took all necessary and appropriate steps to evict the aggressor forces from its territory.... We have been greatly disappointed by this compulsive hostility of Pakistan, because it is an aberration in our region today, where all the other South Asian Association for Regional Cooperation (SAARC) countries are at peace with each other, and are trying, bilaterally and through the SAARC mechanisms, to tackle together the great challenge of development. Tensions are clearly present in the textual data of the respective countries. That same year, however, India and Pakistan casted very similar votes in the UN, with ideal points of -0.797 and -0.739, respectively. Therefore, both sources of data appear to provide useful signals of different aspects of underlying state preferences. Before proceeding to the description of our word embeddings approach, it is useful to first explore the corpus through commonly used measures of disagreement, namely Wordscore and Euclidean distance. Further, the network polarization measure known as modularity is also used to explore the levels of polarization exhibited in roll call data versus speeches. First, a bag-of-words (BOW) representation of the speeches is obtained through tokenization, stemming, removal of stop words, conversion to lower case, and the removal of punctuation, symbols, and numbers. We keep only the features which appear at least 5 times in 3 documents and apply term frequency-inverse document frequency (TF-IDF) weighting to the matrix. This yields a document term frequency matrix for each year. The most frequently used text scaling method in political science is Wordscore BIBREF33 . Word frequencies in the document are used to classify the document into one of two categories. With Wordscore, the learning is supervised using training documents that are known to belong to each of the two categories so that the chosen dimension is substantively defined by the choice of training documents. Here we apply the approach to UNGD statements to calculate levels of disagreement. In Figure FIGREF19 , Wordscore detects in both cases a decline in polarization towards the end of the Cold War and a modest increase after the end of the Cold War. This broadly aligns with expectations in international relations research. Second, we compare this Wordscore disagreement to the Euclidean distances between the US and Russia and the US and China and present these for each session over time. These are calculated using: DISPLAYFORM0 where INLINEFORM0 and INLINEFORM1 are TF-IDF vectors for the individual states. The results of this measure of disagreement are presented in Figure FIGREF20 . The distances closely align with the Wordscore results for US-Russia and US-China dyads. Polarization broadly decreases towards the end of the Cold War and then increases afterwards. Although these measures provide useful aggregate information, they offer little by way of high-resolution information on preference divergence. Further, unlike at the national legislative level, polarity in IR is more than simple disagreement. Polarization refers to “the degree to which the foreign policies of nations within a single cluster are similar to each other, and the degree to which the foreign policies of nations in different clusters are dissimilar" BIBREF9 . Continuing with the BOW representation of the speeches, it is instructive to explore preference polarization using a more sophisticated measure which captures this theoretical definition. Modularity is a recently developed community detection heuristic in the network sciences which attempts to partition a network such that the total number of intra-community edges is optimized relative to a baseline expectation from an appropriate null model (i.e. a random graph) BIBREF22 . This aligns with our theoretical definition via its comparison of within-group ties with other subgroup ties. The intuition is that a community should have more or stronger ties among the actors within the community compared to ties with actors in other communities. Modularity is measured between [0,1] where higher levels of modularity indicate stronger divisions in a given network, and therefore higher levels of polarization. The algorithm can be expressed as: DISPLAYFORM0 where INLINEFORM0 is the total weight of the edges in the network, INLINEFORM1 is the weighted degree of the INLINEFORM2 th node, INLINEFORM3 is the community to which INLINEFORM4 belongs, and INLINEFORM5 = 1 if INLINEFORM6 and INLINEFORM7 belong to the same community, and 0 otherwise BIBREF34 . Since modularity optimization is an NP-complete problem, we utilize the greedy variant which is implemented through the igraph package. This approach has enjoyed widespread network science employment by applied mathematicians and physicists BIBREF35 , and has surfaced in political science studies of international trade BIBREF36 and international law BIBREF37 . As a direct measure of polarization, this approach has been used to measure roll call polarization in the US Congress BIBREF23 , BIBREF24 , BIBREF25 , as well as the UNGA BIBREF22 . To bridge from roll call data to our speeches, it is instructive to replicate the findings of BIBREF22 using ideal point data based on votes and to compare this to a simple weighted graph based on the cosine similarity of the speeches between states over time. Cosine similarity is well established in the natural language processing literature but has also received recent attention in political science BIBREF38 . This provides a measure of similarity between two vectors of an inner product space which measures the cosine of the angle between them and is expressed as: DISPLAYFORM0 where INLINEFORM0 and INLINEFORM1 are vectors of attributes (i.e. term frequency vectors), and INLINEFORM2 and INLINEFORM3 are components of vectors INLINEFORM4 and INLINEFORM5 (i.e. term frequencies), respectively. In the case of text, similarity scores are bounded to positive space [0,1]. Finally, we normalize using: DISPLAYFORM0 The result is an INLINEFORM0 adjacency matrix INLINEFORM1 where INLINEFORM2 contains the normalized textual similarity score (i.e. weighted edge) for each pair of states INLINEFORM3 and INLINEFORM4 . We replicate the results of BIBREF22 using ideal point data, which is represented as the gray line. The blue line represents the modularity calculated on the speeches. Information on the roll call data can be found in BIBREF21 and BIBREF10 . Our aim here is to establish a preliminary idea of how polarization changes over time in these two sources of data. To the extent that the patterns diverge, both sources of data provide potentially different information about underlying preferences. In line with the simple measures presented above, both networks in Figure FIGREF24 exhibit generally decreasing levels of polarization towards the end of the Cold War and then subsequent increases or stagnation afterwards. Interestingly, the speech network displays slightly higher levels of polarization than the ideal point network. This indicates that more heterogeneity exists in speeches than votes, and aligns with Figure 1 and Figure 2a in the main body of the paper. For NATO-Warsaw in Figure FIGREF25 , the speech network follows a similar trend as using roll call data alone: polarity decreases towards the end of the Cold War. It appears, however, that the speech weighted network picks up higher variation in preferences, which indicates that looking to ideal point data alone might over- or under-estimate levels of preference polarization at certain points in time. Finally, the West-Rest alignment provides further confirmation that polarity can be detected in the votes and speeches: West-Rest members exhibit higher levels of polarization compared to the Assembly-level. Polarization exhibits a downward trend in the post-Cold War era – the period in which scholars note an increased prominence of liberal, interdependent globalization. These results align with the trends found by BIBREF22 . These results indicate that weighted word counts provide useful information, but we aim to leverage the information in the speeches in a more sophisticated way and in a way that can increase our confidence that divergence is not simply due to differences in word choice. For this task, we move beyond the BOW approach and use vector space representations. At present, the development of vector space models for textual data is one of the areas that has attracted the most research interest in natural language processing. This interest is motivated by the desire to move away from simple counts and weights of words, towards representations which can preserve word context and linguistic features of human speech. Studies find that these approaches are not only intuitively desirable, but also increase classification accuracy in machine learning tasks. Vector space approaches involve the embedding of document features from one dimension per word space to a continuous, lower dimensional vector space. Each document feature is represented as a real-valued vector and it has been shown that these representations retain desirable syntactical qualities, such as context and structure of speech. Traditionally, dimensionality reduction could be obtained through latent semantic analysis (LSA) used to factorize the feature matrices, but two recently developed models have been introduced which rely on different logic: word2vec from Mikolov and the research group at Google, and the global vectors for word representations (GloVe) unsupervised learning algorithm from the Stanford Natural Language Processing group. We use GloVe introduced by BIBREF15 because while LSA tends to maximize the statistical information used, it does not perform well on analogy tasks. Word2vec does better on the analogy test but does not utilize statistics of the corpus because it trains on local context windows. GloVe was introduced to help bridge this gap and combine both desirable qualities. It is a log bilinear, weighted least squares model that trains on global word-word co-occurence counts and thus makes efficient use of the statistics. BIBREF15 show that their approach yields state-of-the-art performance on the word analogy task. GloVe is sometimes criticized for scalability issues but given that we are working with a fixed size corpus this does not pose an issue for our analysis. Readers are encouraged to consult the GloVe paper for full model details, but we describe our approach and decisions here. The model is expressed as: DISPLAYFORM0 where INLINEFORM0 represents parameters, INLINEFORM1 is the vocabulary size, INLINEFORM2 and INLINEFORM3 are column and row word vectors, INLINEFORM4 is the co-occurrence matrix of all pairs of words that ever co-occur, and INLINEFORM5 is a weighting function which assigns lower weights to words which frequently co-occur. This lattermost term serves as a cap on very frequent words, for example articles like “the" which provide little predictive information. The algorithm seeks to minimize the distance between the inner product of the word vectors and the log count of the co-occurrence of the two words. Compared to skip-gram approaches which update at each context window, it is clear from the utilization of INLINEFORM6 that the model trains relatively quickly since it uses the known corpus statistic of word co-occurrences for the entire corpus at once. We first stem, tokenize, and convert the words to lowercase. Unlike a BOW approach, however, the punctuation is retained. The model is trained on each individual year in the corpus with the vocabulary pruned to include a minimum term count of 4 across documents and the term must exist in 25% of the documents. These relatively stringent parameter levels are employed because we train on individual years in order to avoid language drift over time and to ensure that our estimated embeddings correspond to the exact policy language used in a given year. We employ a skip gram window of 4 and search using a word vector size of 50. At present, we follow the computer science literature suggestion of tuning these parameters until reasonable and reliable linear combinations of language are located. Future work should explore in greater detail how systematic tuning decisions for social science applications can be made. Mikolov and collaborors introduce a new evaluation scheme based on word analogies that examines dimensions of difference in vector space, as opposed to scalar distances between vectors BIBREF13 , BIBREF14 . In order to validate the quality of the located embeddings, we thus follow current standard practice and assess whether reasonable linear combinations of words can be returned. The main body of the paper presents three examples, and a further example is presented here: DISPLAYFORM0 where INLINEFORM0 describes a vector space location of the given feature and the cosine distance between each vector space location is added or subtracted to find the closest vector space feature (presented on the right hand-side of the equality with the cosine similarity printed underneath). The example is interpreted as: if we add the location of INLINEFORM1 to INLINEFORM2 , we arrive at the vector space location of the word INLINEFORM3 . This, as well as the examples presented in the main body, provide some indication that intuitive and reasonable embeddings have been located. Vector space representations are particularly promising for the measurement of polarization. In contrast to a BOW approach, we want to know not only that two actors use (dis)similar words, but we want high resolution insights into how and when they speak differently on different topics. For example, we would like to capture the dissimilarity of statements like "we oppose the proliferation of nuclear weapons" versus "the proliferation of atom bombs is necessary." The words “necessary" and “oppose" would be counted in a BOW matrix, but their usage in the context of nuclear weapons would be lost. Further, if one state says “nuclear weapon" and another says “atomic bomb," the two phrases have no words in common and the phrases will be thought to be distant or dissimilar, despite referring to the same thing. Although BOW approaches often perform very well, for the measurement of polarization in IR, we need to make sure that the differences uncovered are not simply due to different word usage or policy topics being discussed. To measure expressed (dis)agreement in these speeches, it is necessary to derive a document-level representation of the learned embeddings. Although well-established measurements based on cosine similarity, Euclidean distance, or Pearson correlations could be applied to the word embeddings, we utilized the relaxed variant of a newly introduced document distance measure that exploits information contained in both the word embeddings and term-document matrices: the (relaxed) Word Mover's Distance (RWMD) BIBREF17 . WMD innovates by leveraging the finding that embedding distances between word vectors are semantically meaningful. WMD represents text documents as a weighted point cloud of embedded words where the distance between two documents is the minimum cumulative distance that words from document A would need to travel to match exactly the point cloud of document B. This has been shown to yield state-of-the-art classification accuracy BIBREF39 . Although WMD is relatively fast to compute, we use the relaxed variant (RWMD), which results in tighter bounds and is shown to yield lower test error rates. In short, this relaxes the optimization problem through the removal of one of the two constraints. If we let INLINEFORM0 and INLINEFORM1 be the BOW representations of two documents in the INLINEFORM2 dimensional simplex of word distributions which we obtained above, we can express RWMD as: DISPLAYFORM0 where INLINEFORM0 is a sparse flow matrix where INLINEFORM1 denotes how much of word INLINEFORM2 in INLINEFORM3 travels to word INLINEFORM4 in INLINEFORM5 and INLINEFORM6 is defined to be the distance between the two documents as the minimum weighted cumulative cost required to move all words from INLINEFORM7 to INLINEFORM8 . Then, the optimal solution is found when each word in INLINEFORM9 moves all of its probability mass to the most similar word in INLINEFORM10 . This optimal matrix INLINEFORM11 is decided by: DISPLAYFORM0 where INLINEFORM0 is the distance of interest which we normalize and convert to a similarity score using equation 4 above and INLINEFORM1 , respectively. The result is a list of INLINEFORM2 matrices INLINEFORM3 for each year in the corpus where INLINEFORM4 is the speech similarity score between states INLINEFORM5 and INLINEFORM6 for the given year in the corpus with the diagonals of the matrices set to 0. Network Analysis Although we present a novel approach for leveraging position similarity information found in political text, it is not immediately obvious that this approach is useful for the task of political analysis. We posit that for this approach to be considered useful, it should be capable of providing information useful for the performance of inference on observed state behavior. We choose violent conflict onset because this is one of the enduring methodological challenges faced by the discipline. For this task, we aim to see whether or not these embedded speeches provide information on state preferences which improve upon current out-of-sample predictions relative to current models which employ UN roll call data. We choose to compare our approach to the recently published models of BIBREF8 , because their goal and applications which use UN roll call data closely parallel our research motivations. This section provides further information on the graph partitioning approach, the new Multilayer Extraction algorithm, and model performance assessments. For clusters based on speeches alone, we follow Pauls and Cranmer's ( BIBREF8 ) approach through the performance of 5-nearest neighbor clustering on the matrix of RWMD state-state similarities, which yields candidates for membership in an affinity community. As we are dealing with textual data, and in contrast to their sign-test approach, we assign ties between affinity candidate state pairs INLINEFORM0 based on relatively strict similarity thresholds between 0.50 and 0.60. The result is a square INLINEFORM1 adjacency matrix INLINEFORM2 of unweighted ties for each year which contains all states who voted and delivered a GD statement. To locate multiplex blocs, we use the voting clusters found by Pauls and Cranmer ( BIBREF8 ) as one layer, and the speech clusters just described as a second layer. The Multilayer Extraction algorithm (described below) is then applied to these two layers, which returns community membership labels in vectors. These are transformed into adjacency matrices of unweighted ties for each year. This is a strong test for our hypothesized effects because multilayer communities must be detectable after individual layers have already been clustered. The intuition of employing a threshold is that every node is connected to every other node in the network through a given text similarity, but this computationally becomes infeasible and unnecessary as many of the ties are low valued. Further, in order to be compatible with the TERGMs, this binarization is necessary, and so we follow thresholding guidelines outlined in the network science literature, see BIBREF40 , BIBREF41 , BIBREF42 , BIBREF43 . Because the choice of threshold is case dependent and must take into account the type of data under scrutiny, we turn to the NLP literature, which suggests that a threshold of INLINEFORM0 is considered to be a relatively common benchmark for discarding low-information similarity scores BIBREF44 , BIBREF45 , BIBREF46 . As different thresholds result in different graphs, we check and confirm the robustness of our results at different levels and report these results below. Clusters based on speeches or votes in isolation provide one slice of information about state preferences, but we are motivated to locate an approach which exploits both sources of information in tandem. This is broadly aligned with other work in political science which aims to combine votes and speeches in statistical models. Specifically, we posit that valuable and perhaps different information might be garnered from the voting behavior and speeches made by states and we consider these as two elementary layers of a multilayer network. A two-layer network illustration is presented in Figure FIGREF32 . Extracting communities based on this multilayer network provides one strategy of holistically exploiting both sources of preference information in tandem and is one of the current forefronts of network science research BIBREF26 . At present, most multilayer community detection heuristics require the community structure to be homogenous at each layer, but as displayed above, speech and voting patterns often diverge. A recently proposed solution to this problem is the Multilayer Extraction procedure BIBREF27 . The algorithm identifies densely connected vertex-layers in multilayer networks through a significance-based score that quantifies the connectivity of an observed vertex-layer set by comparison with a multilayer fixed degree random graph model. For our analysis, the clusters from voting data comprise the first layer and the text-based clusters comprise the second layer of the graph for each year. This unique approach to community detection allows us to exploit preference similarity information across speeches and voting habits without requiring states to be similar on both dimensions. Formally, our vote-speech layered network is a node-aligned INLINEFORM0 undirected multilayer network INLINEFORM1 with no self-edges INLINEFORM2 . The multilayer network is node aligned because we add extraneous nodes such that INLINEFORM3 where typically the INLINEFORM4 . Such an approach is shown in the literature to yield mathematically desirable properties for the task of multilayer community detection BIBREF26 and is also required for the TERGM estimations. The Multilayer Extraction procedure locates communities across the two network layers and provides a resulting membership identification vector which can be used to construct a one-mode representation of the cross-layer communities for a given year where an edge between INLINEFORM0 and INLINEFORM1 exists if the extraction procedure identifies INLINEFORM2 and INLINEFORM3 as belonging to the same community. If the extraction procedure fails to locate multilayer communities, we can conclude that no information is lost by using the more common aggregation procedure of considering the union of edges in the two layers INLINEFORM4 and consider an edge to exist between INLINEFORM5 and INLINEFORM6 if an edge exists in either of the two layers, see BIBREF26 . The result is a one mode network for each year based on the two sources of preference information. These multiplex blocs are employed as a substitute for votes- and text-based clusters in Model 4, and the results are reported in [table:tergm]Table 1 of the main paper. For the results reported in the paper's main body, we found that Multilayer Extraction detected heterogenous community structure in the two layers of speeches and votes for 12 of the years. For the other 18 years, no multilayer communities were detected and therefore could be aggregated into a single network without the risk of ignoring multilayer community structure in the analysis. This helps to explain why some findings using UN roll call data alone come up with varying conclusions. Roll call data would perform better in years where complex speech-vote dependencies did not exist (i.e. when a given state's votes and speeches are both “similar" and therefore considering one source of information alone is analytically sufficient). However, in years where a given state's voting behavior and speeches diverge (e.g. a NATO member voting with the bloc but delivering a speech which contains position information which diverges from the rest of the bloc), then favoring one source of information over the other will likely yield misleading estimates of preference polarization. This multilayer community detection approach allows us to capture these potentially complex structures at both levels of preference information. In the body of the main paper, we present the results of the original model and date-adjusted models of BIBREF8 , as well as our textual and multiplex cluster results. The readers can refer to the original paper of BIBREF8 for complete details on variable operationalization and model specification. We focus here on the performance of the models. Increasing attention is devoted to the appropriate role of unsupervised methods in political science research, because they require human interpretation and lack well-defined criteria for accuracy and performance assessment. Both the word embeddings and multilayer community detection procedure are unsupervised methods, and so we posit that in order to validate that these located affinity blocs really do capture meaningful preference similarity in international politics, they should be able to improve upon our current ability to predict conflict onset out-of-sample. This would enable us to more adequately explain observed state behavior and provides a rigorous criteria for performance assessment. As reported in the main paper, clusters based on partitions of our speech graph and our speech-vote multiplex graph are both statistically significant predictors of conflict onset. Here, we assess the goodness-of-fit of our models, the out-of-sample predictive accuracy of the models, and robustness checks of our results at different tie similarity thresholds. To assess the in-sample goodness-of-fit (GOF) of exponential random graph models, it is common to simulate network statistics which were not specified in the model to see how well the fitted model can simulate statistics on the outcome network. In our case, the outcome network is the conflict onset network, and we perform 50 simulations over each of the 30 time steps for 1,500 total simulations for each of the four models. The in-sample areas under the ROC and PR curves for the three models of primary concern are presented in Figure FIGREF36 and Figure FIGREF37 , respectively. Furthermore, the dyad-wise and edge-wise shared partners and modularities are presented for all four models in Figure FIGREF38 and Figure FIGREF39 . Although the original models from BIBREF8 and our textual and multiplex models all exhibit impressive GOFs, the multiplex model exhibits the best in-sample GOF as measured by areas under the ROC and PR curves. This increases our confidence in the model specifications, but it is necessary to assess out-of-sample predictive capability since all four models fit quite well. To assess whether or not the inclusion of textual or multiplex clusters improves upon the existing models with roll call-based clusters alone, we follow BIBREF8 in training on five-year windows and attempt to predict the next year of conflict onset. The multiplex model exhibits a 20.5% increase in area under the precision recall curve compared to the original date-adjusted model (1.156 vs. 0.959). In contrast, the model with textual clusters alone underperforms the original date-adjusted model (0.081 vs. 0.959). We plot these out-of-sample performance results for the multiplex models alongside other tie thresholds as checks on the stability of the results in Figure FIGREF40 which displays the sum of the areas under the precision recall curves at various thresholds compared to the baseline (date-adjusted) model presented in BIBREF8 . We find that several of the thresholds provide reliable and statistically significant estimates of conflict onset. In order to choose the final model, we select the model with the lowest variance in out-of-sample prediction capability (i.e. the area under the precision recall curve), that is, we select the model which yields the most consistent predictions over time. Because out-of-sample predictions on sparse networks (such as the conflict onset network) is a challenging task, this selection approach helps to eliminate models with predictions that vary widely from one year to the next. As previously mentioned, a threshold of .50 is common in the NLP literature and so we report every threshold between [0.50,0.60] at .01 increments. We select the .58 tie threshold because this model exhibits the lowest variance in predictions. To ensure that we are not selecting a single model which vastly outperforms all other thresholds (i.e. to ensure that our results are robust), we consider the out-of-sample predictive accuracy for the models in which the multiplex clusters yielded a statistically significant relationship with conflict onset. This was the case for 7 out of the 11 thresholds tested. The sums of the areas under the precision recall curve for these models are plotted in Figure FIGREF41 . These box plots make clear that our multiplex models do indeed display increased out-of-sample predictive capability relative to the baseline model across various thresholds. The above amounts to a toolkit for the exploitation of multidimensional information on positions and preferences in political research. We show how the information in textual data can be usefully exploited beyond simple word counts and weighted frequencies. We show how this information can in its own right be useful, but also how it can be exploited in tandem with other existing sources of preference information like votes through a multilayer network approach. Finally, we show that these measures are not only substantively reasonable, but can be used to extend current state-of-the-art network models which infer the impact of preferences on international conflict.
Do they use number of votes as an indicator of preference?
06be47e2f50b902b05ebf1ff1c66051925f5c247
Do Ġthey Ġuse Ġnumber Ġof Ġvotes Ġas Ġan Ġindicator Ġof Ġpreference ?
No
<s> Do Ġthey Ġuse Ġnumber Ġof Ġvotes Ġas Ġan Ġindicator Ġof Ġpreference ? </s> Introduction </s> The Ġpolarization Ġof Ġactors ' Ġexpressed Ġpreferences Ġis Ġa Ġfundamental Ġconcern Ġfor Ġstudies Ġof Ġlegislatures , Ġcourt Ġsystems , Ġand Ġinternational Ġpolitics ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ. ĠBecause Ġpreferences Ġare Ġunob serv able , Ġscholars Ġmust Ġlook Ġfor Ġsignals Ġin Ġthe Ġempirical Ġworld . ĠRecent Ġprogress Ġhas Ġbeen Ġmade Ġin Ġparliamentary Ġand Ġcourt Ġsettings Ġthrough Ġthe Ġemployment Ġof Ġtextual Ġdata ĠB IB REF 4 Ġand Ġvotes Ġand Ġtexts Ġin Ġtandem ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ. ĠMany Ġof Ġthese Ġadvances Ġrely Ġon Ġspatial , Ġscaling , Ġand Ġitem - response Ġtype Ġmodels Ġthat Ġare Ġintuitive Ġfor Ġsettings Ġwhere Ġa Ġsmall Ġnumber Ġof Ġparties Ġor Ġideological Ġdivisions Ġinfluence Ġoutcomes . ĠThis Ġis Ġless Ġintuitive Ġfor Ġthe Ġstudy Ġof Ġstate Ġpreferences , Ġbecause Ġinternational Ġrelations Ġis Ġmarked Ġby Ġmultiple Ġdimensions Ġthat Ġspan Ġideological , Ġeconomic , Ġand Ġsecurity Ġconcerns , Ġamong Ġothers ĠB IB REF 0 Ġ. </s> This Ġpaper Ġintroduces Ġa Ġnew Ġapproach Ġto Ġestimate Ġpreference Ġpolarization Ġin Ġmult id imensional Ġsettings Ġusing Ġtexts Ġand Ġvotes . ĠFirst , Ġa Ġdistribution al Ġrepresentation Ġof Ġtextual Ġdata Ġis Ġutilized Ġto Ġretain Ġqualities Ġof Ġhuman Ġspeech Ġthat Ġare Ġotherwise Ġdiscarded Ġby Ġa Ġbag - of - words Ġapproach . ĠSecond , Ġcommunity Ġdetection Ġin Ġmultiple x Ġnetworks Ġis Ġused Ġto Ġuncover Ġpreference Ġaffinity Ġbloc s Ġacross Ġmultiple Ġlayers Ġof Ġvotes Ġand Ġspeeches . ĠJust Ġas Ġscaling Ġand Ġspatial Ġmodels Ġattempt Ġto Ġexplain Ġvariance Ġalong Ġone Ġor Ġa Ġfew Ġreduced Ġdimensions , Ġour Ġapproach Ġidentifies Ġdensely Ġconnected Ġcommunities Ġbased Ġon Ġpreference Ġsimilarity Ġthat Ġare Ġimportant Ġfor Ġexplaining Ġvariations Ġin Ġobserved Ġoutcomes . </s> We Ġillustrate Ġthe Ġutility Ġof Ġthis Ġapproach Ġwith Ġan Ġempirical Ġtest Ġof Ġa Ġcore Ġhypothesis Ġin ĠInternational ĠRelations Ġ( IR ): Ġmilitar ized Ġconflict Ġis Ġless Ġlikely Ġbetween Ġstates Ġwith Ġmore Ġsimilar Ġpreferences ĠB IB REF 7 Ġ. ĠSpecifically , Ġwe Ġextend Ġa Ġrecently Ġpublished Ġnetwork Ġmodel Ġof Ġconflict Ġonset . ĠB IB REF 8 Ġutilize Ġtemporal Ġexponential Ġrandom Ġgraph Ġmodels Ġto Ġinfer Ġthe Ġrelationship Ġbetween Ġconflict Ġonset Ġand Ġa Ġbattery Ġof Ġpredict ors , Ġone Ġof Ġwhich Ġis Ġaffinity Ġcommunities Ġlocated Ġvia Ġspectral Ġclust ering Ġon Ġa Ġgraph Ġof ĠUN Ġvotes . ĠThis Ġcovari ate Ġprovides Ġa Ġnatural Ġcomparison Ġto Ġexamine Ġwhether Ġour Ġproposed Ġclust ering Ġapproach Ġcan Ġimprove Ġour Ġability Ġto Ġmodel Ġconflict Ġonset , Ġas Ġmeasured Ġby Ġout - of - sample Ġpredictive Ġaccuracy . ĠWe Ġfind Ġthat Ġmultiple x Ġclusters Ġbased Ġon Ġcountry Ġspeeches Ġand Ġvotes ĠâĢĵ Ġwhich Ġwe Ġrefer Ġto Ġas Ġaffinity Ġbloc s ĠâĢĵ Ġoutper form Ġclusters Ġbased Ġon Ġvotes Ġor Ġspeeches Ġalone . ĠThe Ġproposed Ġframework Ġenables ĠIR Ġscholars Ġto Ġbetter Ġexplain Ġbehavioral Ġoutcomes Ġin Ġinternational Ġpolitics , Ġand Ġwill Ġbe Ġof Ġuse Ġto Ġany Ġscholar Ġinterested Ġin Ġthe Ġmeasurement Ġof Ġpreference Ġpolarization Ġin Ġmult id imensional Ġsettings . </s> Political ĠPolar ization : ĠMeasure ments Ġand ĠModels </s> P olar ization Ġin ĠIR Ġis Ġdefined Ġas ĠâĢ ľ the Ġdegree Ġto Ġwhich Ġthe Ġforeign Ġpolicies Ġof Ġnations Ġwithin Ġa Ġsingle Ġcluster Ġare Ġsimilar Ġto Ġeach Ġother , Ġand Ġthe Ġdegree Ġto Ġwhich Ġthe Ġforeign Ġpolicies Ġof Ġnations Ġin Ġdifferent Ġclusters Ġare Ġdiss imilar " ĠB IB REF 9 Ġ. ĠTherefore , Ġoperational izing Ġa Ġconcept Ġof Ġpreference Ġpolarization Ġbroadly Ġinvolves Ġtwo Ġsteps : Ġan Ġapproach Ġto Ġestimate Ġpreferences Ġfrom Ġavailable Ġdata Ġon Ġstates ' Ġobservable Ġbehavior ; Ġand Ġa Ġmethod Ġof Ġdetecting Ġdistinct Ġcommunities Ġof Ġnations , Ġsuch Ġthat Ġnations Ġbelonging Ġto Ġthe Ġsame Ġcommunity Ġshare Ġsimilar Ġpreferences , Ġand Ġnations Ġbelonging Ġto Ġdifferent Ġcommunities Ġhave Ġdiss imilar Ġpreferences . </s> UN Ġvotes Ġand Ġspeeches </s> The Ġmost Ġwidely Ġused Ġsource Ġfor Ġder iving Ġpreferences Ġin ĠIR Ġis ĠUN Ġroll Ġcall Ġdata ĠB IB REF 10 Ġ. ĠVoting Ġbehavior Ġrepresent Ġa Ġvaluable Ġsource Ġof Ġrevealed Ġpreference Ġinformation , Ġcomparable Ġacross Ġstates Ġand Ġover Ġtime . ĠHowever , ĠUN Ġroll Ġcall Ġvotes Ġtend Ġto Ġbe Ġa Ġweak Ġsignal Ġof Ġunderlying Ġpreferences Ġin Ġcases Ġwhere Ġstates Ġvote Ġfor Ġceremonial Ġpurposes , Ġare Ġconstrained Ġby Ġagenda - setting Ġpower Ġdynamics , Ġor Ġvote Ġas Ġcohorts Ġto Ġmaximize Ġtheir Ġimpact Ġwithin Ġthe ĠUN , Ġsuch Ġas Ġwith Ġregional Ġbloc s ĠB IB REF 11 Ġ. </s> Similar Ġlimitations Ġexist Ġin Ġthe Ġstudy Ġof Ġpolarization Ġin Ġnational Ġlegislatures Ġwhere Ġactors ' Ġvotes Ġseldom Ġdiver ge Ġfrom Ġparty Ġlines . ĠIn Ġresponse , Ġan Ġemerging Ġliterature Ġturns Ġto Ġactors ' Ġspeeches Ġto Ġbetter Ġcapture Ġexpressed Ġpositions Ġand Ġto Ġmeasure Ġpolarization Ġof Ġthese Ġpositions ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ. ĠThe Ġemployment Ġof Ġtext Ġdata Ġfor Ġthe Ġmeasurement Ġof Ġstate Ġpreferences Ġin Ġworld Ġpolitics Ġis Ġintuitive , Ġbecause Ġoutcomes Ġare Ġa Ġfunction Ġof Ġmultiple Ġissue Ġdimensions , Ġsuch Ġas Ġtopics Ġranging Ġfrom Ġhuman Ġrights Ġto Ġnuclear Ġproliferation Ġpolicy . ĠIn Ġparticular , Ġstates ' Ġannual Ġaddresses Ġin Ġthe ĠUN ĠGeneral ĠDebate Ġ( GD ) Ġprovide Ġa Ġvaluable Ġsource Ġof Ġdata Ġon Ġstate Ġpreferences . ĠGovernments Ġuse Ġtheir Ġannual ĠGD Ġspeeches Ġto Ġdiscuss Ġtheir Ġpositions Ġon Ġthe Ġissues Ġin Ġinternational Ġpolitics Ġthey Ġconsider Ġmost Ġimportant . ĠAs Ġstates Ġface Ġfew Ġinstitutional Ġconstraints Ġduring Ġthese Ġspeeches , Ġthey Ġcan Ġexpress Ġtheir Ġpositions Ġon Ġa Ġwider Ġrange Ġof Ġissues Ġcompared Ġto Ġvotes Ġon Ġagenda - set Ġitems . </s> An Ġexample Ġof Ġthis Ġdifference Ġbetween Ġvotes Ġand Ġspeeches Ġcan Ġbe Ġseen Ġin Ġthe Ġcase Ġof ĠGreece Ġand ĠTurkey Ġin Ġ1974 . ĠBoth Ġcountries Ġwere ĠNATO Ġmembers , Ġhowever , ĠTurkey 's Ġinvasion Ġof ĠCyprus Ġin ĠJuly Ġof Ġ1974 Ġled Ġto Ġheightened Ġtension Ġand Ġhostilities Ġbetween Ġthe Ġtwo Ġnations . ĠThis Ġincluded ĠTurkish Ġand ĠGreek Ġfighter Ġjets Ġengaging Ġin Ġa Ġdog fight Ġwhich Ġresulted Ġin Ġthe Ġdeath Ġof Ġa ĠTurkish Ġpilot . ĠYet , Ġthe Ġideal Ġpoints Ġof ĠGreece Ġand ĠTurkey Ġbased Ġon ĠUN Ġvotes Ġthat Ġyear Ġwere Ġthe Ġmost Ġsimilar Ġamong ĠNATO Ġmember Ġstates Ġ( 0 . 68 Ġand Ġ0 . 42 , Ġrespectively ). ĠWhile Ġtheir Ġvotes Ġindicate Ġthat Ġthey Ġhave Ġbroadly Ġsimilar Ġforeign Ġpolicy Ġpreferences Ġand Ġprovide Ġuseful Ġsignals Ġof Ġmembership Ġon Ġan Ġalliance Ġdimension Ġ( e . g . Ġthey Ġare Ġspat ially Ġdistant Ġfrom ĠWarsaw ĠPact Ġmembers Ġin Ġ1974 ), Ġthey Ġfail Ġto Ġreflect Ġthe Ġsignificant Ġtension Ġbetween Ġthe Ġcountries . ĠIn Ġcontrast , Ġtheir Ġspeeches Ġin Ġthe Ġ1974 ĠGeneral ĠDebate Ġclearly Ġreveal Ġthe Ġhostility Ġbetween Ġthe Ġtwo Ġnations . ĠBoth ĠGreece Ġand ĠTurkey Ġdiscussed Ġthe ĠCyprus Ġinvasion Ġat Ġlength Ġin Ġtheir Ġspeeches , Ġwith Ġeach Ġblaming Ġthe Ġcrisis Ġon Ġthe Ġother . </s> We Ġdraw Ġon Ġa Ġrecently Ġreleased Ġcorpus Ġof Ġstate Ġspeeches Ġdelivered Ġduring Ġthe Ġannual ĠUN ĠGeneral ĠDebate Ġthat Ġprovides Ġthe Ġfirst Ġdataset Ġof Ġtextual Ġoutput Ġfrom Ġstates Ġthat Ġis Ġrecorded Ġat Ġregular Ġtime - series Ġintervals Ġand Ġincludes Ġa Ġsample Ġof Ġall Ġcountries Ġthat Ġdeliver Ġspeeches ĠB IB REF 11 Ġ. ĠThere Ġare Ġlimitations Ġto Ġboth Ġvotes Ġand Ġspeeches Ġin Ġthe ĠUN Ġin Ġder iving Ġestimates Ġof Ġstates ' Ġunderlying Ġpreferences . ĠHowever , Ġit Ġis Ġnot Ġcontroversial Ġto Ġsuggest Ġthat Ġstate Ġspeeches Ġcan Ġval uably Ġcomplement Ġroll Ġcall Ġdata , Ġand Ġthe Ġuse Ġof Ġspeeches Ġand Ġvotes Ġtogether Ġcan Ġreveal Ġuseful Ġpreference Ġinformation Ġbeyond Ġthat Ġcontained Ġin Ġstates ' Ġvoting Ġbehavior Ġor ĠGD Ġspeeches Ġalone . ĠThe Ġquestion , Ġrather , Ġis Ġhow Ġbest Ġto Ġrepresent Ġthese Ġtexts Ġand Ġhow Ġbest Ġto Ġtheoretically Ġmodel Ġthese Ġdata Ġin Ġtandem . </s> Word Ġembed d ings </s> In Ġorder Ġto Ġuse Ġtexts Ġtogether Ġwith Ġvotes Ġto Ġestimate Ġpreference Ġpolarization , Ġwe Ġfirst Ġconsider Ġhow Ġto Ġbetter Ġexploit Ġthe Ġinformation Ġcontained Ġin Ġtextual Ġdata . ĠTo Ġdo Ġthis , Ġwe Ġintroduce Ġa Ġnew Ġrepresentation Ġof Ġtextual Ġdata Ġwhich Ġmore Ġadequately Ġcaptures Ġdynamics Ġof Ġhuman Ġlanguage , Ġnamely Ġun super vised Ġlearned Ġword Ġembed d ings . ĠIn Ġthe Ġbroader Ġnatural Ġlanguage Ġprocessing Ġ( N LP ) Ġliterature , Ġthere Ġhas Ġbeen Ġa Ġsurge Ġof Ġresearch Ġdevoted Ġto Ġthe Ġdevelopment Ġof Ġdistribution al Ġrepresentations Ġof Ġspeech Ġwhich Ġretain Ġsynt act ical Ġlanguage Ġqualities Ġin Ġways Ġthat Ġthe Ġbag - of - words Ġ( B OW ) Ġapproach Ġtypically Ġused Ġin Ġpolitical Ġtext Ġanalysis Ġresearch Ġis Ġnot Ġequipped Ġto Ġretain . ĠThe Ġhypothesis Ġclaims Ġthat Ġwords Ġthat Ġoccur Ġin Ġsimilar Ġcontexts Ġtend Ġto Ġhave Ġsimilar Ġmeanings ĠB IB REF 12 Ġ. ĠWhen Ġoperational ized , Ġthe Ġunique Ġintuition Ġis Ġthat Ġsimilar Ġwords Ġand Ġphrases , Ġsuch Ġas ĠâĢ ľ atomic , Ġweapons âĢ Ŀ Ġand ĠâĢ ľ nuclear , Ġwarheads âĢ Ŀ Ġare Ġfound Ġin Ġrelatively Ġprox imate Ġvector Ġspace Ġlocations . ĠAlthough Ġthe ĠB OW Ġperforms Ġsurprisingly Ġwell , Ġthis Ġexample Ġhas Ġno Ġfeatures Ġin Ġcommon , Ġand Ġa ĠB OW Ġrepresentation Ġwould Ġassign Ġlow Ġsimilarity Ġscores Ġor Ġhigh Ġdistances . </s> When Ġresults Ġare Ġprojected Ġonto Ġa Ġtwo Ġdimensional Ġspace , Ġlanguage Ġrelationships Ġsurface , Ġsuch Ġas Ġthe Ġclust ering Ġof Ġsyn onyms , Ġan ton y ms , Ġscales Ġ( e . g . Ġdemocracy Ġto Ġauthoritarian ism ), Ġhyp onym - hyper ny ms Ġ( e . g . Ġdemocracy Ġis Ġa Ġtype Ġof Ġregime ), Ġco - hyp onyms Ġ( e . g . Ġatom Ġbombs Ġand Ġballistic Ġmissiles Ġare Ġtypes Ġof Ġweapons ), Ġand Ġgroups Ġof Ġwords Ġwhich Ġtend Ġto Ġappear Ġin Ġsimilar Ġcontexts Ġlike Ġdiplomat , Ġenvoy , Ġand Ġembassy . ĠMik ol ov Ġand Ġcollaborators Ġintroduce Ġan Ġevaluation Ġscheme Ġbased Ġon Ġword Ġanalog ies Ġthat Ġexamines Ġdimensions Ġof Ġdifference Ġin Ġvector Ġspace ĠB IB REF 13 Ġ, ĠB IB REF 14 Ġ. ĠThey Ġoriginally Ġreached Ġthe Ġsurprising Ġconclusion Ġthat Ġsimple Ġvector Ġaddition Ġand Ġsubt raction Ġun co vers Ġinteresting Ġlinear Ġsubst ruct ures Ġof Ġhuman Ġlanguage , Ġfamously Ġthat ĠIN LINE FORM 0 Ġ. </s> To Ġlocate Ġvector Ġspace Ġrepresentations Ġof Ġour Ġcorpus , Ġwe Ġutilize Ġthe ĠStanford ĠN LP Ġgroup 's ĠGlobal ĠV ect ors Ġfor ĠWord ĠRepresent ation Ġ( G lo Ve ) Ġun super vised Ġlearning Ġalgorithm ĠB IB REF 15 Ġ. ĠGl o Ve Ġis Ġa Ġpopular Ġlog Ġbil in ear , Ġweighted Ġleast Ġsquares Ġmodel Ġthat Ġtrains Ġon Ġglobal Ġword - word Ġco - occ ure nce Ġcounts Ġto Ġmake Ġefficient Ġuse Ġof Ġthe Ġcorpus Ġstatistics . ĠBecause Ġit Ġfactor izes Ġa Ġword - context Ġco - occ urrence Ġmatrix , Ġit Ġis Ġcloser Ġto Ġtraditional Ġcount Ġmethods Ġlike Ġlatent Ġsemantic Ġanalysis Ġor Ġprinciple Ġcomponent Ġanalysis . ĠHere , Ġwe Ġpresent Ġthree Ġanalog ical Ġexamples Ġfrom Ġthe Ġlocated Ġembed d ings : ĠDIS PLAY FORM 0 </s> where Ġeach ĠIN LINE FORM 0 Ġdescribes Ġa Ġvector Ġspace Ġlocation Ġof Ġthe Ġgiven Ġfeature , Ġand Ġthe Ġcos ine Ġsimilarity Ġbetween Ġeach Ġvector Ġspace Ġlocation Ġis Ġadded Ġor Ġsubt racted Ġto Ġfind Ġthe Ġclosest Ġvector Ġoffsets Ġ( with Ġcos ine Ġsimilarity Ġprinted Ġunderneath ). ĠThese Ġanalog ies Ġare Ġinterpreted , Ġfor Ġexample , Ġas ĠIN LINE FORM 1 Ġis Ġto ĠIN LINE FORM 2 Ġas ĠIN LINE FORM 3 Ġis Ġto ĠIN LINE FORM 4 Ġ. ĠThese Ġexamples Ġappear Ġto Ġencode Ġrelations Ġof Ġcause - effect , Ġthreats / harm , Ġand Ġlocation / ge ography , Ġrespectively . ĠAs Ġfound Ġin Ġthe Ġwider ĠN LP Ġliterature , Ġthe Ġimplication Ġis Ġthat Ġthese Ġvector Ġspace Ġmodels Ġare Ġsurprisingly Ġeffective Ġat Ġcapturing Ġdifferent Ġlex ical Ġrelations , Ġdespite Ġthe Ġlack Ġof Ġsupervision . </s> To Ġmeasure Ġposition Ġsimilarities , Ġwe Ġapply Ġa Ġnew Ġdocument - level Ġdistance Ġmeasure Ġto Ġthe Ġembed d ings : Ġthe Ġ( rel ax ed ) ĠWord ĠM over 's ĠDistance Ġ( RW MD ) ĠB IB REF 17 Ġ. ĠRW MD Ġis Ġdescribed Ġin Ġgreater Ġdetail Ġin Ġthe ĠSupplementary ĠMaterials , Ġbut Ġin Ġshort , Ġthis Ġmeasures Ġthe Ġcumulative Ġdistance Ġrequired Ġto Ġtransform Ġone Ġstate 's Ġspeech Ġpoint Ġcloud Ġinto Ġthat Ġof Ġanother Ġstate , Ġensuring Ġthat Ġdifferences Ġdo Ġnot Ġsimply Ġreflect Ġthe Ġuse Ġof Ġdifferent Ġwords . ĠStates Ġemploy Ġvaried Ġlanguage Ġand Ġlex ical Ġpatterns Ġto Ġdescribe Ġsimilar Ġtopics . ĠFor Ġexample , Ġif Ġstate ĠA Ġsays ĠâĢ ľ nuclear Ġweapons Ġare Ġbad ," Ġand Ġstate ĠB Ġsays ĠâĢ ľ atom Ġbombs Ġare Ġterrible ," Ġthe Ġonly Ġfeature Ġin Ġcommon Ġis Ġthe Ġterm ĠâĢ ľ are ," Ġwhich Ġleads Ġto Ġnear - orth og on ality Ġin Ġtheir ĠB OW Ġvectors Ġand Ġlow Ġsimilarity Ġscores . ĠIf Ġa Ġthird Ġstate ĠC Ġsays ĠâĢ ľ atom Ġbombs Ġare Ġgood ," Ġthen ĠB Ġand ĠC Ġwould Ġexhibit Ġthe Ġhighest Ġcos ine Ġsimilarity Ġof Ġthe Ġthree , Ġdespite Ġhaving Ġthe Ġopposite Ġexpressed Ġpolicy Ġpositions . ĠWord Ġembed d ings Ġand Ġterm - document Ġmat rices Ġare Ġlocated Ġfor Ġeach Ġyear Ġin Ġthe Ġcorpus , Ġ1970 - 2014 , Ġand Ġstate Ġdy ad ĠRW MD Ġdistances Ġare Ġcalculated , Ġconverted Ġto Ġsimilarity Ġscores , Ġand Ġstored Ġin Ġan ĠIN LINE FORM 1 Ġmatrix ĠIN LINE FORM 2 Ġfor Ġeach Ġyear . </s> For Ġtexts Ġto Ġbe Ġconsidered Ġas Ġa Ġuseful Ġcomplement Ġto Ġroll Ġcall Ġdata , Ġwe Ġshould Ġsee Ġdifferences Ġin Ġthe Ġpositions Ġexpressed Ġin Ġspeeches Ġversus Ġvotes . ĠThis Ġwould Ġindicate Ġthat Ġthe Ġtwo Ġsources Ġreveal Ġdifferent Ġpreference Ġinformation Ġand Ġthat Ġusing Ġone Ġover Ġthe Ġother Ġrisks Ġoverlooking Ġavailable Ġpreference Ġsignals . ĠFurther , Ġfor Ġthe Ġproposed Ġword Ġembed d ings Ġapproach Ġto Ġbe Ġconsidered Ġuseful , Ġit Ġshould Ġprovide Ġgreater Ġinsight Ġinto Ġintra - bl oc Ġposition Ġvariation Ġbeyond Ġwhat Ġis Ġavailable Ġin Ġthe ĠB OW , Ġbecause Ġintra - bl oc Ġvariation Ġis Ġan Ġimportant Ġcomponent Ġof Ġthe Ġdefinition Ġof Ġpolarization Ġused Ġhere , Ġas Ġwell Ġas Ġin Ġmuch Ġof Ġthe Ġrecent Ġtext Ġversus Ġvotes Ġliterature . ĠThe Ġcase Ġof ĠNATO Ġand ĠWarsaw ĠPact Ġmembers Ġis Ġpresented Ġin Ġ[ fig : d iffs ] Figure Ġ1 , Ġwhich Ġplots Ġstates ' Ġideal Ġpoints Ġfrom ĠB IB REF 21 Ġ, ĠB OW Ġcos ine Ġsimilarities , Ġand ĠRW MD Ġsimilarities , Ġwith Ġthe Ġlatter Ġtwo Ġscaled Ġbetween ĠIN LINE FORM 0 Ġ. </s> Visual Ġassessment Ġof Ġintra - bl oc Ġsimilarities Ġindicates Ġthat Ġthe ĠRW MD s Ġbased Ġon Ġword Ġembed d ings Ġyield Ġhigher Ġvariations Ġin Ġexpressed Ġpositions Ġcompared Ġto Ġthe Ġcos ine Ġsimilarities Ġbetween Ġmembers ' ĠB OW Ġvectors . ĠThis Ġprovides Ġpreliminary Ġconfirmation Ġthat Ġthe Ġsuggested Ġword Ġembed d ings Ġapproach Ġcaptures Ġmore Ġinteresting Ġvariation Ġin Ġthe Ġpreferences Ġof Ġstates Ġcompared Ġto Ġthe ĠB OW . ĠFurthermore , Ġwhile Ġthe Ġideal Ġpoints Ġclearly Ġreflect Ġa Ġless ening Ġof ĠCold ĠWar Ġtensions , Ġthe ĠRW MD Ġsimilarities Ġappear Ġto Ġdetect Ġgreater Ġintra - bl oc Ġposition Ġvariations . ĠTherefore , Ġboth Ġappear Ġto Ġprovide Ġdifferent Ġand Ġpotentially Ġvaluable Ġinformation Ġfor Ġthe Ġestimation Ġof Ġstate Ġpreferences . </s> Multiple x Ġnetworks Ġand Ġcommunity Ġdetection </s> Having Ġoutlined Ġa Ġnovel Ġapproach Ġto Ġestimate Ġstates ' Ġpreference Ġsimilarity , Ġthe Ġsecond Ġstep Ġis Ġto Ġdetect Ġdistinct Ġcommunities Ġof Ġstates . ĠThe Ġnetwork Ġscience Ġliterature Ġon Ġcommunity Ġdetection Ġis Ġespecially Ġwell - su ited Ġfor Ġthis Ġtask . ĠOne Ġcommon Ġmeasure Ġis Ġmodular ity , Ġa Ġcommunity Ġdetection Ġhe uristic Ġthat Ġpartitions Ġa Ġnetwork Ġsuch Ġthat Ġthe Ġtotal Ġnumber Ġof Ġintra - community Ġedges Ġis Ġmaxim ized Ġrelative Ġto Ġa Ġbaseline Ġexpectation Ġfrom Ġa Ġnull Ġmodel Ġ( i . e . Ġa Ġrandom Ġgraph ) ĠB IB REF 22 Ġ. ĠThe Ġintuition Ġis Ġthat Ġa Ġcommunity Ġshould Ġhave Ġmore Ġor Ġstronger Ġties Ġamong Ġthe Ġactors Ġwithin Ġthe Ġcommunity Ġcompared Ġto Ġties Ġwith Ġactors Ġin Ġother Ġcommunities . ĠThis Ġcommon Ġapproach , Ġhowever , Ġcan Ġbe Ġmisleading Ġin Ġdense Ġnetworks , Ġsuch Ġas Ġvote Ġand Ġspeech Ġsimilarity . ĠIndeed , ĠB IB REF 22 Ġuse ĠUN Ġvoting Ġdata Ġto Ġillustrate Ġthe Ġchallenges Ġrelated Ġto Ġnetwork Ġclust ering Ġon Ġdata Ġwith Ġhigh Ġlevels Ġof Ġagreement Ġbetween Ġobservations . </s> To Ġovercome Ġthis Ġissue Ġwith Ġdensity Ġand Ġto Ġexploit Ġthe Ġinformation Ġfound Ġin Ġvotes Ġand Ġspeeches Ġin Ġtandem , Ġwe Ġinstead Ġturn Ġto Ġmultiple x Ġnetwork Ġcommunity Ġdetection . ĠMult il ayer Ġgraphs Ġconsist Ġof Ġmore Ġthan Ġone Ġlayer Ġand Ġpermit Ġthe Ġsearch Ġfor Ġcommunities Ġacross Ġgraph Ġlevels . ĠCommunity Ġdetection Ġmethods Ġfor Ġmult il ayer Ġgraphs , Ġthough , Ġare Ġstill Ġin Ġtheir Ġinfancy Ġand Ġmost Ġcurrent Ġapproaches Ġposit Ġthe Ġsame Ġcommunity Ġstructure Ġat Ġdifferent Ġlevels Ġof Ġthe Ġmult igraph . ĠAs Ġ[ fig : d iffs ] Figure Ġ1 Ġshows , Ġhowever , Ġvotes Ġand Ġspeeches Ġappear Ġto Ġexhibit Ġrather Ġheter ogeneous Ġstructures . </s> A Ġrecently Ġproposed Ġsolution Ġfor Ġthis Ġtask Ġis Ġthe ĠMult il ayer ĠExt raction Ġprocedure ĠB IB REF 27 Ġ. ĠThe Ġalgorithm Ġidentifies Ġdensely Ġconnected Ġvertex - l ayers Ġin Ġmult il ayer Ġnetworks Ġthrough Ġa Ġsignificance - based Ġscore Ġthat Ġquant ifies Ġthe Ġconnectivity Ġof Ġan Ġobserved Ġvertex - layer Ġset Ġby Ġcomparison Ġwith Ġa Ġmult il ayer Ġfixed Ġdegree Ġrandom Ġgraph Ġmodel . ĠFor Ġour Ġanalysis , Ġthe Ġclusters Ġfrom Ġvoting Ġdata Ġcomprise Ġone Ġlayer Ġand Ġthe Ġtext - based Ġclusters Ġcomprise Ġthe Ġsecond Ġlayer Ġof Ġthe Ġgraph Ġfor Ġeach Ġyear . ĠThe ĠSupplementary ĠMaterials Ġdescribes Ġthe Ġdata Ġmanipulation Ġsteps Ġin Ġdetail , Ġbut Ġin Ġshort , Ġwe Ġfollow ĠPaul s Ġand ĠCran mer Ġ( ĠB IB REF 8 Ġ) Ġin Ġperforming Ġ5 - ne arest Ġneighbor Ġclust ering Ġon Ġthe Ġmatrix Ġof Ġstate Ġspeech Ġsimilarities Ġto Ġfind Ġcandidates Ġfor Ġaffinity Ġcommunities Ġand Ġthen Ġassign Ġties Ġabove Ġthresholds . ĠThese Ġtext - based Ġclusters Ġand Ġtheir Ġvote - based Ġclusters Ġare Ġthen Ġused Ġas Ġtwo Ġlayers Ġof Ġa Ġmult il ayer Ġnetwork , Ġand Ġthe ĠMult il ayer ĠExt raction Ġalgorithm Ġis Ġused Ġto Ġdetect Ġaffinity Ġbloc s Ġacross Ġthe Ġtwo Ġlayers . ĠThis Ġprocess Ġis Ġillustrated Ġin Ġ[ fig : pl ots ] Figure Ġ2 , Ġwhich Ġdisplays Ġ( a ) Ġthe Ġvote Ġand Ġtext Ġsimilarity Ġmat rices , Ġ( b ) Ġthe Ġsingle Ġlayer Ġvote Ġand Ġspeech Ġclusters , Ġand Ġ( c ) Ġthe Ġmultiple x Ġaffinity Ġbloc s Ġlocated Ġacross Ġboth Ġlayers . </s> E mp ir ical ĠApplication : ĠAff inity ĠBl oc s Ġand ĠConflict ĠOn set </s> A Ġrecently Ġpublished Ġnetwork Ġconflict Ġonset Ġmodel Ġprovides Ġan Ġideal Ġtest Ġfor Ġour Ġproposed Ġmult il ayer Ġaffinity Ġbloc s . ĠB IB REF 8 Ġuse Ġspectral Ġclust ering Ġto Ġidentify Ġdensely Ġconnected Ġaffinity Ġcommunities Ġbased Ġon ĠUN Ġroll Ġcall Ġdata Ġand Ġemploy Ġthese Ġclusters Ġin Ġtemporal Ġexponential Ġrandom Ġgraph Ġmodels Ġ( TER G Ms ) Ġto Ġinfer Ġthe Ġrelationship Ġbetween Ġviolent Ġconflict Ġonset Ġand Ġaffinity Ġcommunities Ġin Ġthe ĠUN GA , Ġamong Ġother Ġcovari ates Ġof Ġinterest . ĠThe Ġoutcome Ġnetwork Ġof Ġinterest Ġis Ġconstructed Ġfrom Ġconflict Ġonset Ġdata Ġfrom Ġthe ĠCor rel ates Ġof ĠWar Ġ( C OW ) Ġproject 's ĠMil itar ized ĠInterstate ĠDisp ute Ġ( M ID ) Ġdataset Ġ( v 4 . 1 ) ĠB IB REF 30 Ġ. ĠThe Ġtime Ġperiod Ġfor Ġthe Ġmodel Ġwe Ġreplicate Ġspans Ġ1965 - 2000 , Ġwhereas Ġour Ġcorpus Ġof Ġspeeches Ġcovers Ġ1970 - 2014 . ĠWe Ġlimit Ġour Ġextension Ġto Ġthe Ġyears Ġof Ġoverlap , Ġ1970 - 2000 . </s> Our Ġinf erential Ġanalysis Ġfirst Ġrepl icates Ġtheir Ġfull Ġconflict Ġonset Ġmodel ĠB IB REF 8 Ġ, Ġwhich Ġwe Ġsuccessfully Ġdo . ĠThis Ġsame Ġmodel Ġis Ġestimated Ġover Ġthe Ġconstrained Ġtime Ġrange Ġof Ġour Ġanalysis Ġto Ġensure Ġthat Ġsignificance Ġand Ġdirection Ġof Ġthe Ġcoefficients Ġon Ġthe Ġcovari ates Ġdo Ġnot Ġsubstantially Ġdiffer . ĠWe Ġfind Ġthat Ġthe Ġsigns Ġremain Ġthe Ġsame Ġand Ġthe Ġcoefficients Ġdo Ġnot Ġchange Ġdramatically . ĠThese Ġresults Ġare Ġreported Ġin Ġ[ table : ter gm ] Table Ġ1 Ġas ĠModel Ġ1 Ġand Ġ2 , Ġrespectively . ĠWe Ġthen Ġuse Ġspeech Ġand Ġmultiple x Ġbloc s Ġas Ġsubstitutes Ġfor Ġthe Ġoriginal Ġvote Ġclusters Ġand Ġreport Ġthese Ġas ĠModel Ġ3 Ġand Ġ4 , Ġrespectively . </s> The Ġresults Ġof ĠModel Ġ3 Ġin Ġ[ table : ter gm ] Table Ġ1 Ġindicate Ġthat Ġthe Ġtext - based Ġclusters Ġexhibit Ġa Ġnoticeably Ġlarger Ġeffect Ġof Ġpreference Ġsimilarity Ġon Ġconflict Ġavoidance Ġcompared Ġto Ġthe Ġvote - based Ġclusters . ĠInterestingly , Ġthe Ġsignificance Ġof Ġjoint Ġdemocracy Ġfalls Ġoff , Ġas Ġwell Ġas Ġthe Ġeffects Ġof Ġthe Ġsecurity , Ġtrade , Ġand Ġeconomic Ġdependency Ġnetworks . ĠThis Ġimplies Ġthat Ġmembership Ġin Ġtext - based Ġaffinity Ġcommunities Ġis Ġassociated Ġwith Ġa Ġsubstantial Ġdecrease Ġin Ġthe Ġlikelihood Ġof Ġviolent Ġconflict Ġonset Ġand Ġis Ġa Ġmuch Ġlarger Ġeffect Ġcompared Ġto Ġother Ġcovari ates Ġof Ġinterest . ĠThis Ġstark Ġdifference Ġbetween Ġthe Ġvotes - Ġand Ġspeeches - cl uster Ġcoefficients Ġprovides Ġfurther Ġindication Ġof Ġunderlying Ġheterogeneity Ġin Ġthe Ġnetwork Ġgraphs . ĠBoth Ġindicate Ġthat Ġmembership Ġin Ġaffinity Ġcommunities Ġis Ġassociated Ġwith Ġa Ġdecrease Ġin Ġthe Ġlikelihood Ġof Ġconflict Ġonset Ġbut Ġappear Ġto Ġcapture Ġdifferent Ġmanifestations Ġof Ġlatent Ġpreferences . </s> The Ġmultiple x Ġmodel Ġdisplays Ġcoefficients Ġcloser Ġto ĠModels Ġ1 Ġand Ġ2 . ĠThe Ġmultiple x Ġbloc Ġindicates Ġthat Ġmembership Ġin Ġaffinity Ġcommunities Ġas Ġlocated Ġacross Ġvote Ġand Ġspeech Ġgraphs Ġis Ġassociated Ġwith Ġa Ġdecrease Ġin Ġthe Ġlikelihood Ġthat Ġa Ġgiven Ġpair Ġof Ġstates Ġwill Ġengage Ġin Ġarmed Ġconflict . ĠTo Ġincrease Ġconfidence Ġin Ġthese Ġresults , Ġhowever , Ġwe Ġfollow ĠB IB REF 8 Ġin Ġthe Ġassessment Ġof Ġout - of - sample Ġpredictive Ġaccuracy Ġby Ġtraining Ġmodels Ġon Ġfive Ġyear Ġwindows Ġand Ġthen Ġassessing Ġpredictions Ġon Ġthe Ġnext Ġyear . ĠThe Ġareas Ġunder Ġthe Ġprecision Ġrecall Ġcurves Ġare Ġthen Ġsummed Ġover Ġthe Ġentire Ġdate Ġrange . ĠThe Ġpredictive Ġcapability Ġof ĠModel Ġ3 Ġoutper forms Ġtheir Ġpaper 's Ġbaseline Ġmodel Ġwith Ġno Ġpreference Ġnetworks Ġbut Ġunder per forms Ġtheir Ġdate - adjusted Ġmodel Ġas Ġmeasured Ġby Ġarea Ġunder Ġthe Ġprecision Ġrecall Ġcurve Ġ( 0 . 08 1 Ġvs . Ġ0 . 9 59 ). ĠThe Ġmultiple x Ġmodel , Ġhowever , Ġoutper forms Ġtheir Ġbaseline Ġpaper Ġmodel Ġ( with Ġand Ġwithout Ġclusters ), Ġthe Ġdate - adjusted Ġmodel , Ġand Ġour Ġtextual Ġextension Ġmodel . ĠThe Ġmultiple x Ġmodel Ġexhibits Ġa Ġ20 . 5 % Ġincrease Ġin Ġarea Ġunder Ġthe Ġprecision Ġrecall Ġcurve Ġcompared Ġto Ġthe Ġoriginal Ġdate - adjusted Ġmodel Ġ( 1 . 156 Ġvs . Ġ0 . 9 59 ). ĠConstruct ing Ġaffinity Ġbloc s Ġbased Ġon Ġtexts Ġand Ġvotes Ġin Ġtandem Ġthus Ġleads Ġto Ġa Ġmore Ġsubstant ively Ġintuitive Ġmodel , Ġas Ġwell Ġas Ġincreased Ġpredictive Ġperformance . ĠAlthough Ġthis Ġis Ġa Ġrelatively Ġlarge Ġgain Ġin Ġpredictive Ġaccuracy , Ġthese Ġsubstant ively Ġsmall Ġquantities Ġconfirm Ġthat Ġthe Ġprediction Ġof Ġviolent Ġconflict Ġonset Ġremains Ġan Ġenduring Ġchallenge Ġfor Ġscholars Ġof ĠIR . ĠNonetheless , Ġthe Ġability Ġto Ġexploit Ġrevealed Ġpreference Ġinformation Ġin Ġspeeches Ġand Ġvotes Ġin Ġtandem Ġappears Ġto Ġpromise Ġfruitful Ġpotential Ġgains Ġin Ġterms Ġof Ġmethodological Ġcapability Ġand Ġtheoretical Ġsound ness . </s> Conclusion </s> This Ġpaper Ġintroduces Ġa Ġnovel Ġapproach Ġto Ġestimate Ġpreference Ġpolarization Ġin Ġmult id imensional Ġsettings Ġusing Ġvotes Ġand Ġtextual Ġdata , Ġbased Ġon Ġdevelopments Ġin Ġthe Ġnatural Ġlanguage Ġprocessing Ġand Ġnetwork Ġscience Ġliter atures . ĠThe Ġapproach Ġhelps Ġto Ġbetter Ġexploit Ġinformation Ġfound Ġin Ġtextual Ġdata , Ġand Ġto Ġlocate Ġdense Ġclusters Ġin Ġcomplex Ġand Ġmult il ay ered Ġnetworks Ġin Ġways Ġthat Ġwere Ġpreviously Ġnot Ġcomput ationally Ġpossible . ĠDrawing Ġon Ġa Ġnew Ġdataset Ġof Ġstate Ġspeeches Ġin Ġthe ĠUN ĠGeneral ĠDebate , Ġtogether Ġwith Ġvoting Ġdata Ġfrom Ġthe ĠUN GA , Ġthese Ġtools Ġwere Ġemployed Ġto Ġbetter Ġestimate Ġrevealed Ġstate Ġpreferences Ġin Ġinternational Ġpolitics Ġand Ġto Ġlocate Ġpreference Ġaffinity Ġbloc s Ġwhich Ġexist Ġacross Ġmultiple Ġlayers Ġof Ġspeech Ġand Ġroll Ġcall Ġnetworks . </s> It Ġis Ġperhaps Ġworth Ġnoting Ġthat Ġthese Ġsources Ġare Ġrelatively Ġweak Ġsignals Ġof Ġtrue Ġstate Ġpreferences Ġand Ġpropensity Ġto Ġengage Ġin Ġarmed Ġconflict . ĠHowever , Ġthese Ġmethods Ġsignificantly Ġimprove Ġour Ġability Ġto Ġidentify Ġmeaningful Ġpatterns Ġof Ġpreference Ġsimilarity Ġamongst Ġthe Ġnoise . ĠFurthermore , Ġthe Ġapproach Ġcan Ġassist Ġany Ġpolitical Ġstudy Ġthat Ġseeks Ġto Ġmeasure Ġposition Ġsimilarities Ġfrom Ġtextual Ġdata Ġand Ġdetect Ġdense Ġclusters Ġof Ġaffinity Ġor Ġantagon ism Ġacross Ġmultiple Ġrelational Ġdatasets . ĠThese Ġmight Ġinclude Ġsocial Ġmedia Ġactors Ġwho Ġoperate Ġacross Ġmultiple Ġplatforms , Ġas Ġwell Ġas Ġcontexts Ġlike Ġlegislatures Ġwhere Ġcomplex Ġrelations Ġexist Ġacross Ġvotes , Ġspeeches , Ġcommittee Ġmembers hips , Ġand Ġothers . ĠTherefore , Ġthe Ġapproach Ġpresented Ġin Ġthis Ġpaper Ġwill Ġbe Ġuseful Ġto Ġall Ġscholars Ġbroadly Ġseeking Ġto Ġmeasure Ġpolitical Ġpreferences Ġand Ġpolarization Ġin Ġmult id imensional Ġcontexts . </s> Supplement ary ĠMaterials </s> The Ġfollowing Ġmaterial Ġcomple ments Ġthe Ġanalysis Ġin Ġthe Ġmain Ġtext . ĠTo Ġour Ġknowledge , Ġthis Ġpaper Ġpresents Ġthe Ġfirst Ġpolitical Ġscience Ġapplication Ġof Ġvector Ġspace Ġrepresentations Ġof Ġtextual Ġdata , Ġthe ĠWord ĠM over 's ĠDistance , Ġand Ġthe ĠMult il ayer ĠExt raction Ġprocedure . ĠThe ĠSupplementary ĠMaterials Ġare Ġstructured Ġas Ġfollows . ĠFirst , Ġthe Ġdata Ġare Ġdiscussed Ġand Ġapplications Ġwhich Ġuse Ġa Ġbag - of - words Ġtreatment Ġare Ġpresented . ĠSecond , Ġthe Ġvector Ġspace Ġapproach Ġand Ġdistance Ġmeasure Ġare Ġintroduced Ġand Ġdiscussed . ĠFinally , Ġthe Ġnetwork Ġclust ering Ġapproach Ġand Ġmodel Ġevaluations Ġare Ġpresented . </s> Data Ġand ĠMeasure ments </s> We Ġdraw Ġon Ġthe Ġnewly Ġreleased ĠUN ĠGeneral ĠDebate ĠCorpus ĠB IB REF 11 Ġwhich Ġcontains Ġevery Ġcountry Ġstatement Ġin Ġthe ĠUN ĠGeneral ĠDebate Ġbetween Ġ1970 Ġand Ġ2014 . ĠThe ĠGeneral ĠDebate Ġ( GD ) Ġtakes Ġplace Ġevery ĠSeptember Ġat Ġthe Ġstart Ġof Ġeach Ġnew Ġsession Ġof Ġthe ĠUN ĠGeneral ĠAssembly Ġ( UN GA ). ĠIt Ġprovides Ġall Ġmember Ġstates Ġwith Ġthe Ġopportunity Ġto Ġaddress Ġthe ĠUN GA Ġand Ġto Ġpresent Ġtheir Ġperspective Ġon Ġkey Ġissues Ġin Ġworld Ġpolitics . ĠGovernments Ġuse Ġtheir ĠGD Ġstatements Ġto Ġput Ġon Ġthe Ġrecord Ġtheir Ġposition Ġon Ġevents Ġthat Ġhave Ġoccurred Ġduring Ġthe Ġpast Ġyear Ġand Ġon Ġlonger - term Ġunderlying Ġissues Ġin Ġworld Ġpolitics Ġrelated Ġto Ġissues Ġsuch Ġas Ġconflict , Ġterrorism , Ġdevelopment , Ġhuman Ġrights , Ġand Ġclimate Ġchange . </s> A Ġprincipal Ġdifference Ġbetween ĠGD Ġstatements Ġand ĠUN GA Ġvoting Ġis Ġthat Ġthe ĠGD Ġstatements Ġare Ġnot Ġinstitution ally Ġconnected Ġto Ġdecision - making Ġin Ġthe ĠUN . ĠAs Ġa Ġresult , Ġgovernments Ġare Ġfree Ġto Ġdiscuss Ġthe Ġissues Ġthey Ġconsider Ġto Ġbe Ġof Ġgreatest Ġimportance Ġin Ġworld Ġpolitics , Ġregardless Ġof Ġwhether Ġan Ġissue Ġis Ġon Ġthe Ġformal Ġagenda Ġof Ġthe ĠUN GA . ĠTherefore , Ġas ĠB IB REF 32 Ġnotes , Ġthe ĠGeneral ĠDebate Ġacts ĠâĢ ľ as Ġa Ġbar ometer Ġof Ġinternational Ġopinion Ġon Ġimportant Ġissues , Ġeven Ġthose Ġnot Ġon Ġthe Ġagenda Ġfor Ġthat Ġparticular Ġsession . âĢ Ŀ ĠIn Ġproviding Ġinformation Ġabout Ġstates ' Ġpreferences Ġon Ġworld Ġpolitics , Ġthe ĠGD Ġprovides Ġa Ġvaluable Ġdata Ġsource Ġfor Ġmeasuring Ġpolarization Ġin ĠInternational ĠRelations . ĠIn Ġaddition Ġto Ġbeing Ġthe Ġone Ġmajor Ġforum Ġwhere Ġstates Ġpresent Ġtheir Ġviews Ġon Ġinternational Ġpolitics Ġfree Ġfrom Ġexternal Ġconstraints , Ġthe Ġfact Ġthat Ġit Ġtakes Ġplace Ġannually Ġand Ġincludes Ġall ĠUN Ġmember Ġstates Ġenables Ġcomparison Ġover Ġtime Ġand Ġacross Ġcountries . ĠReaders Ġare Ġencouraged Ġto Ġconsult ĠB IB REF 11 Ġfor Ġa Ġcomprehensive Ġintroduction Ġto Ġthe Ġcorpus . </s> As Ġmentioned Ġin Ġthe Ġmain Ġtext , Ġwe Ġdiscuss Ġan Ġexample Ġwhere Ġdisagreement Ġis Ġobvious Ġin Ġstates ' ĠGD Ġspeeches Ġbut Ġless Ġobvious Ġin Ġtheir Ġvoting Ġbehavior . ĠConsider Ġthe Ġfollowing Ġbrief Ġexcerpts Ġfrom Ġthe ĠGD Ġspeeches Ġof ĠGreece Ġand ĠTurkey Ġin Ġ1974 . </s> G ree ce : ĠOn Ġ15 ĠJuly Ġa Ġcoup , Ġcondemned Ġby Ġall Ġof Ġus , Ġwas Ġstaged Ġto Ġoverthrow ĠArchbishop ĠMak arios , Ġthe Ġlegitimate , Ġelected ĠPresident Ġof Ġthe ĠRepublic . ĠThis Ġcoup Ġwas Ġnot Ġdirected Ġagainst Ġthe ĠTurkish ĠCy pri ot Ġcommunity Ġof Ġthe Ġisland ... ĠDuring Ġthe Ġfighting Ġwhile Ġthe Ġcoup Ġwas Ġin Ġprogress , Ġnot Ġa Ġsingle ĠTurkish ĠCy pri ot Ġwas Ġkilled Ġor Ġinjured . ĠYet Ġfive Ġdays Ġlater , Ġlarge ĠTurkish Ġinvasion Ġforces Ġwere Ġlanding Ġin ĠCyprus Ġand Ġthe ĠTurkish ĠAir ĠForce Ġwas Ġlaunching Ġindiscrim inate Ġattacks Ġagainst Ġunarmed Ġcivilians , Ġunder Ġthe Ġfl imsy Ġpretext Ġof Ġprotecting Ġthe ĠTurkish ĠCy pri ot Ġminority Ġon Ġthe Ġisland , Ġwhich , ĠI Ġrepeat , Ġhad Ġnot Ġbeen Ġharmed Ġin Ġany Ġway ... ĠTwo Ġhours Ġlater , Ġthe ĠTurkish Ġtroops Ġwere Ġon Ġthe Ġmove Ġagain , Ġs owing Ġdeath Ġand Ġdestruction , Ġkilling ĠUnited ĠNations Ġtroops , Ġbombing Ġhospitals Ġand Ġschools . ĠRepe ated Ġcease - fire Ġcalls Ġby Ġthe ĠSecurity ĠCouncil Ġwent Ġun he eded . ĠTurkey Ġeven Ġignored Ġthe Ġceasefire Ġproclaimed Ġby Ġits Ġown ĠPrime ĠMinister Ġon Ġ16 ĠAugust Ġ1974 . </s> Turkey : ĠTurkey Ġhas Ġconstantly Ġhad Ġto Ġface Ġfa its Ġaccompl is Ġof Ġincreasingly Ġserious Ġscope , Ġparticularly Ġsince Ġ1963 . ĠThe Ġmost Ġrecent Ġand Ġthe Ġmost Ġserious Ġof Ġthese Ġfa its Ġaccompl is Ġwas , Ġas Ġwe Ġall Ġknow , Ġthat Ġof Ġ15 ĠJuly Ġlast : Ġa Ġforeign ĠPower Ġundertook Ġa Ġcoup Ġd ' et at Ġwhich Ġit Ġhad Ġlong Ġbeen Ġf om enting Ġand Ġthe Ġpurpose Ġof Ġwhich Ġwas Ġto Ġannex Ġthe Ġisland ... ĠThe Ġcoup Ġd ' et at Ġof Ġ15 ĠJuly Ġwas Ġdirected Ġprecisely Ġagainst Ġthe ĠTurkish Ġcommunity Ġand Ġwas Ġdirectly Ġaimed Ġat Ġthe Ġannexation Ġof Ġthe Ġisland Ġto ĠGreece ... ĠI Ġhave Ġnot , Ġhowever , Ġfinished Ġcorrecting Ġall Ġthe Ġfalse Ġallegations Ġand Ġbaseless Ġcharges Ġmade Ġby Ġmy Ġcolleague . ĠI Ġreserve Ġthe Ġright Ġto Ġdo Ġso Ġwhen Ġwe Ġspeak Ġon Ġthis Ġmatter Ġbefore Ġthe ĠGeneral ĠAssembly . ĠMy ĠGreek Ġcolleague 's Ġspeech , Ġunfortunately , Ġshows Ġthe Ġnature Ġof Ġthe Ġatmosphere Ġin Ġwhich Ġthe Ġdebate Ġwill Ġtake Ġplace Ġon Ġthe Ġfuture Ġof Ġthe Ġtwo Ġcommunities , ĠTurkish Ġand ĠGreek , Ġin Ġthe Ġisland . </s> The Ġtwo Ġrepresentatives Ġare Ġoutlining Ġtheir Ġpositions Ġon Ġthe Ġcontroversy Ġrelated Ġto Ġthe ĠTurkish Ġinvasion Ġof ĠCyprus . ĠExp ressed Ġdisagreement Ġon Ġthis Ġtopic Ġis Ġclearly Ġpresent Ġin Ġthe Ġspeeches , Ġbut Ġas Ġmentioned Ġin Ġthe Ġmain Ġpaper , Ġthe Ġtwo Ġcountries ' Ġvoting Ġideal Ġpoints Ġfor Ġthat Ġyear Ġat Ġthe Ġmost Ġsimilar Ġamongst Ġall ĠNATO Ġmembers . ĠA Ġfurther Ġexample Ġis Ġillustrated Ġin Ġthe Ġspeeches Ġand Ġvoting Ġhabits Ġof ĠIndia Ġand ĠPakistan Ġin Ġ1999 , Ġthe Ġyear Ġthe Ġtwo Ġcountries Ġwent Ġto Ġwar Ġ( the ĠK arg il ĠWar ). ĠConsider Ġthe Ġfollowing Ġexcerpts Ġfrom Ġtheir ĠGeneral ĠDebate Ġstatements Ġthat Ġyear : </s> Pakistan : ĠThe ĠK arg il Ġcrisis Ġwas Ġa Ġmanifestation Ġof Ġthe Ġdeeper Ġm ala ise Ġspawned Ġby Ġthe Ġunresolved ĠKashmir Ġproblem Ġand ĠIndia 's Ġescalating Ġrepression Ġof Ġthe ĠKashmir i Ġpeople . ĠIndia Ġlaunched Ġa Ġmassive Ġmilitary Ġoperation Ġin ĠK arg il Ġand Ġthreatened Ġa Ġwider Ġconflict Ġby Ġmobil izing Ġits Ġarmed Ġforces Ġall Ġalong Ġthe ĠPakistan - India Ġinternational Ġborder . ĠPakistan Ġacted Ġwith Ġrestraint ... ĠIndia 's Ġrepression Ġin ĠJ ammu Ġand ĠKashmir Ġhas Ġkilled Ġthousands Ġof ĠKashmir is , Ġforced Ġhundreds Ġof Ġthousands Ġinto Ġexile , Ġled Ġto Ġthree Ġwars Ġbetween ĠPakistan Ġand ĠIndia Ġand Ġcons igned Ġthe Ġtwo Ġcountries Ġto Ġa Ġrelationship Ġof Ġendemic Ġconflict Ġand Ġmistrust . </s> India : ĠPre med itated Ġaggression Ġby Ġregular Ġforces Ġwas Ġcommitted Ġagainst ĠIndia . ĠNot Ġsimply Ġwas Ġthe ĠLah ore ĠDeclaration Ġviolated , Ġbut Ġso Ġwas Ġthe ĠSim la ĠAgreement , Ġwhich Ġhad Ġprevented Ġconflict Ġfor Ġmore Ġthan Ġa Ġquarter Ġof Ġa Ġcentury . ĠIn Ġself - def ence , Ġyet Ġwith Ġthe Ġutmost Ġrestraint , ĠIndia Ġtook Ġall Ġnecessary Ġand Ġappropriate Ġsteps Ġto Ġevict Ġthe Ġaggress or Ġforces Ġfrom Ġits Ġterritory .... ĠWe Ġhave Ġbeen Ġgreatly Ġdisappointed Ġby Ġthis Ġcomp ulsive Ġhostility Ġof ĠPakistan , Ġbecause Ġit Ġis Ġan Ġaber ration Ġin Ġour Ġregion Ġtoday , Ġwhere Ġall Ġthe Ġother ĠSouth ĠAsian ĠAssociation Ġfor ĠRegional ĠCooperation Ġ( SA ARC ) Ġcountries Ġare Ġat Ġpeace Ġwith Ġeach Ġother , Ġand Ġare Ġtrying , Ġb ilaterally Ġand Ġthrough Ġthe ĠSA ARC Ġmechanisms , Ġto Ġtackle Ġtogether Ġthe Ġgreat Ġchallenge Ġof Ġdevelopment . </s> T ensions Ġare Ġclearly Ġpresent Ġin Ġthe Ġtextual Ġdata Ġof Ġthe Ġrespective Ġcountries . ĠThat Ġsame Ġyear , Ġhowever , ĠIndia Ġand ĠPakistan Ġcast ed Ġvery Ġsimilar Ġvotes Ġin Ġthe ĠUN , Ġwith Ġideal Ġpoints Ġof Ġ- 0 . 797 Ġand Ġ- 0 . 7 39 , Ġrespectively . ĠTherefore , Ġboth Ġsources Ġof Ġdata Ġappear Ġto Ġprovide Ġuseful Ġsignals Ġof Ġdifferent Ġaspects Ġof Ġunderlying Ġstate Ġpreferences . </s> Before Ġproceeding Ġto Ġthe Ġdescription Ġof Ġour Ġword Ġembed d ings Ġapproach , Ġit Ġis Ġuseful Ġto Ġfirst Ġexplore Ġthe Ġcorpus Ġthrough Ġcommonly Ġused Ġmeasures Ġof Ġdisagreement , Ġnamely ĠWord score Ġand ĠEucl ide an Ġdistance . ĠFurther , Ġthe Ġnetwork Ġpolarization Ġmeasure Ġknown Ġas Ġmodular ity Ġis Ġalso Ġused Ġto Ġexplore Ġthe Ġlevels Ġof Ġpolarization Ġexhibited Ġin Ġroll Ġcall Ġdata Ġversus Ġspeeches . ĠFirst , Ġa Ġbag - of - words Ġ( B OW ) Ġrepresentation Ġof Ġthe Ġspeeches Ġis Ġobtained Ġthrough Ġtoken ization , Ġstemming , Ġremoval Ġof Ġstop Ġwords , Ġconversion Ġto Ġlower Ġcase , Ġand Ġthe Ġremoval Ġof Ġpunct uation , Ġsymbols , Ġand Ġnumbers . ĠWe Ġkeep Ġonly Ġthe Ġfeatures Ġwhich Ġappear Ġat Ġleast Ġ5 Ġtimes Ġin Ġ3 Ġdocuments Ġand Ġapply Ġterm Ġfrequency - in verse Ġdocument Ġfrequency Ġ( TF - ID F ) Ġweight ing Ġto Ġthe Ġmatrix . ĠThis Ġyields Ġa Ġdocument Ġterm Ġfrequency Ġmatrix Ġfor Ġeach Ġyear . </s> The Ġmost Ġfrequently Ġused Ġtext Ġscaling Ġmethod Ġin Ġpolitical Ġscience Ġis ĠWord score ĠB IB REF 33 Ġ. ĠWord Ġfrequencies Ġin Ġthe Ġdocument Ġare Ġused Ġto Ġclassify Ġthe Ġdocument Ġinto Ġone Ġof Ġtwo Ġcategories . ĠWith ĠWord score , Ġthe Ġlearning Ġis Ġsupervised Ġusing Ġtraining Ġdocuments Ġthat Ġare Ġknown Ġto Ġbelong Ġto Ġeach Ġof Ġthe Ġtwo Ġcategories Ġso Ġthat Ġthe Ġchosen Ġdimension Ġis Ġsubstant ively Ġdefined Ġby Ġthe Ġchoice Ġof Ġtraining Ġdocuments . ĠHere Ġwe Ġapply Ġthe Ġapproach Ġto ĠUN GD Ġstatements Ġto Ġcalculate Ġlevels Ġof Ġdisagreement . ĠIn ĠFigure ĠFIG REF 19 Ġ, ĠWord score Ġdetects Ġin Ġboth Ġcases Ġa Ġdecline Ġin Ġpolarization Ġtowards Ġthe Ġend Ġof Ġthe ĠCold ĠWar Ġand Ġa Ġmodest Ġincrease Ġafter Ġthe Ġend Ġof Ġthe ĠCold ĠWar . ĠThis Ġbroadly Ġalign s Ġwith Ġexpectations Ġin Ġinternational Ġrelations Ġresearch . </s> Second , Ġwe Ġcompare Ġthis ĠWord score Ġdisagreement Ġto Ġthe ĠEucl ide an Ġdistances Ġbetween Ġthe ĠUS Ġand ĠRussia Ġand Ġthe ĠUS Ġand ĠChina Ġand Ġpresent Ġthese Ġfor Ġeach Ġsession Ġover Ġtime . ĠThese Ġare Ġcalculated Ġusing : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġare ĠTF - ID F Ġvectors Ġfor Ġthe Ġindividual Ġstates . ĠThe Ġresults Ġof Ġthis Ġmeasure Ġof Ġdisagreement Ġare Ġpresented Ġin ĠFigure ĠFIG REF 20 Ġ. ĠThe Ġdistances Ġclosely Ġalign Ġwith Ġthe ĠWord score Ġresults Ġfor ĠUS - Russia Ġand ĠUS - China Ġdy ads . ĠPolar ization Ġbroadly Ġdecreases Ġtowards Ġthe Ġend Ġof Ġthe ĠCold ĠWar Ġand Ġthen Ġincreases Ġafterwards . </s> Although Ġthese Ġmeasures Ġprovide Ġuseful Ġaggregate Ġinformation , Ġthey Ġoffer Ġlittle Ġby Ġway Ġof Ġhigh - resolution Ġinformation Ġon Ġpreference Ġdivergence . ĠFurther , Ġunlike Ġat Ġthe Ġnational Ġlegislative Ġlevel , Ġpol arity Ġin ĠIR Ġis Ġmore Ġthan Ġsimple Ġdisagreement . ĠPolar ization Ġrefers Ġto ĠâĢ ľ the Ġdegree Ġto Ġwhich Ġthe Ġforeign Ġpolicies Ġof Ġnations Ġwithin Ġa Ġsingle Ġcluster Ġare Ġsimilar Ġto Ġeach Ġother , Ġand Ġthe Ġdegree Ġto Ġwhich Ġthe Ġforeign Ġpolicies Ġof Ġnations Ġin Ġdifferent Ġclusters Ġare Ġdiss imilar " ĠB IB REF 9 Ġ. ĠContinuing Ġwith Ġthe ĠB OW Ġrepresentation Ġof Ġthe Ġspeeches , Ġit Ġis Ġinstruct ive Ġto Ġexplore Ġpreference Ġpolarization Ġusing Ġa Ġmore Ġsophisticated Ġmeasure Ġwhich Ġcaptures Ġthis Ġtheoretical Ġdefinition . </s> Mod ularity Ġis Ġa Ġrecently Ġdeveloped Ġcommunity Ġdetection Ġhe uristic Ġin Ġthe Ġnetwork Ġsciences Ġwhich Ġattempts Ġto Ġpartition Ġa Ġnetwork Ġsuch Ġthat Ġthe Ġtotal Ġnumber Ġof Ġintra - community Ġedges Ġis Ġoptimized Ġrelative Ġto Ġa Ġbaseline Ġexpectation Ġfrom Ġan Ġappropriate Ġnull Ġmodel Ġ( i . e . Ġa Ġrandom Ġgraph ) ĠB IB REF 22 Ġ. ĠThis Ġalign s Ġwith Ġour Ġtheoretical Ġdefinition Ġvia Ġits Ġcomparison Ġof Ġwithin - group Ġties Ġwith Ġother Ġsub group Ġties . ĠThe Ġintuition Ġis Ġthat Ġa Ġcommunity Ġshould Ġhave Ġmore Ġor Ġstronger Ġties Ġamong Ġthe Ġactors Ġwithin Ġthe Ġcommunity Ġcompared Ġto Ġties Ġwith Ġactors Ġin Ġother Ġcommunities . ĠMod ularity Ġis Ġmeasured Ġbetween Ġ[ 0 , 1 ] Ġwhere Ġhigher Ġlevels Ġof Ġmodular ity Ġindicate Ġstronger Ġdivisions Ġin Ġa Ġgiven Ġnetwork , Ġand Ġtherefore Ġhigher Ġlevels Ġof Ġpolarization . ĠThe Ġalgorithm Ġcan Ġbe Ġexpressed Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġtotal Ġweight Ġof Ġthe Ġedges Ġin Ġthe Ġnetwork , ĠIN LINE FORM 1 Ġis Ġthe Ġweighted Ġdegree Ġof Ġthe ĠIN LINE FORM 2 Ġth Ġnode , ĠIN LINE FORM 3 Ġis Ġthe Ġcommunity Ġto Ġwhich ĠIN LINE FORM 4 Ġbelongs , Ġand ĠIN LINE FORM 5 Ġ= Ġ1 Ġif ĠIN LINE FORM 6 Ġand ĠIN LINE FORM 7 Ġbelong Ġto Ġthe Ġsame Ġcommunity , Ġand Ġ0 Ġotherwise ĠB IB REF 34 Ġ. ĠSince Ġmodular ity Ġoptimization Ġis Ġan ĠNP - complete Ġproblem , Ġwe Ġutilize Ġthe Ġgreedy Ġvariant Ġwhich Ġis Ġimplemented Ġthrough Ġthe Ġig raph Ġpackage . </s> This Ġapproach Ġhas Ġenjoyed Ġwidespread Ġnetwork Ġscience Ġemployment Ġby Ġapplied Ġmathematic ians Ġand Ġphysicists ĠB IB REF 35 Ġ, Ġand Ġhas Ġsurfaced Ġin Ġpolitical Ġscience Ġstudies Ġof Ġinternational Ġtrade ĠB IB REF 36 Ġand Ġinternational Ġlaw ĠB IB REF 37 Ġ. ĠAs Ġa Ġdirect Ġmeasure Ġof Ġpolarization , Ġthis Ġapproach Ġhas Ġbeen Ġused Ġto Ġmeasure Ġroll Ġcall Ġpolarization Ġin Ġthe ĠUS ĠCongress ĠB IB REF 23 Ġ, ĠB IB REF 24 Ġ, ĠB IB REF 25 Ġ, Ġas Ġwell Ġas Ġthe ĠUN GA ĠB IB REF 22 Ġ. ĠTo Ġbridge Ġfrom Ġroll Ġcall Ġdata Ġto Ġour Ġspeeches , Ġit Ġis Ġinstruct ive Ġto Ġreplicate Ġthe Ġfindings Ġof ĠB IB REF 22 Ġusing Ġideal Ġpoint Ġdata Ġbased Ġon Ġvotes Ġand Ġto Ġcompare Ġthis Ġto Ġa Ġsimple Ġweighted Ġgraph Ġbased Ġon Ġthe Ġcos ine Ġsimilarity Ġof Ġthe Ġspeeches Ġbetween Ġstates Ġover Ġtime . ĠCos ine Ġsimilarity Ġis Ġwell Ġestablished Ġin Ġthe Ġnatural Ġlanguage Ġprocessing Ġliterature Ġbut Ġhas Ġalso Ġreceived Ġrecent Ġattention Ġin Ġpolitical Ġscience ĠB IB REF 38 Ġ. ĠThis Ġprovides Ġa Ġmeasure Ġof Ġsimilarity Ġbetween Ġtwo Ġvectors Ġof Ġan Ġinner Ġproduct Ġspace Ġwhich Ġmeasures Ġthe Ġcos ine Ġof Ġthe Ġangle Ġbetween Ġthem Ġand Ġis Ġexpressed Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġare Ġvectors Ġof Ġattributes Ġ( i . e . Ġterm Ġfrequency Ġvectors ), Ġand ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġare Ġcomponents Ġof Ġvectors ĠIN LINE FORM 4 Ġand ĠIN LINE FORM 5 Ġ( i . e . Ġterm Ġfrequencies ), Ġrespectively . ĠIn Ġthe Ġcase Ġof Ġtext , Ġsimilarity Ġscores Ġare Ġbounded Ġto Ġpositive Ġspace Ġ[ 0 , 1 ]. ĠFinally , Ġwe Ġnormal ize Ġusing : ĠDIS PLAY FORM 0 </s> The Ġresult Ġis Ġan ĠIN LINE FORM 0 Ġadj ac ency Ġmatrix ĠIN LINE FORM 1 Ġwhere ĠIN LINE FORM 2 Ġcontains Ġthe Ġnormalized Ġtextual Ġsimilarity Ġscore Ġ( i . e . Ġweighted Ġedge ) Ġfor Ġeach Ġpair Ġof Ġstates ĠIN LINE FORM 3 Ġand ĠIN LINE FORM 4 Ġ. </s> We Ġreplicate Ġthe Ġresults Ġof ĠB IB REF 22 Ġusing Ġideal Ġpoint Ġdata , Ġwhich Ġis Ġrepresented Ġas Ġthe Ġgray Ġline . ĠThe Ġblue Ġline Ġrepresents Ġthe Ġmodular ity Ġcalculated Ġon Ġthe Ġspeeches . ĠInformation Ġon Ġthe Ġroll Ġcall Ġdata Ġcan Ġbe Ġfound Ġin ĠB IB REF 21 Ġand ĠB IB REF 10 Ġ. ĠOur Ġaim Ġhere Ġis Ġto Ġestablish Ġa Ġpreliminary Ġidea Ġof Ġhow Ġpolarization Ġchanges Ġover Ġtime Ġin Ġthese Ġtwo Ġsources Ġof Ġdata . ĠTo Ġthe Ġextent Ġthat Ġthe Ġpatterns Ġdiver ge , Ġboth Ġsources Ġof Ġdata Ġprovide Ġpotentially Ġdifferent Ġinformation Ġabout Ġunderlying Ġpreferences . </s> In Ġline Ġwith Ġthe Ġsimple Ġmeasures Ġpresented Ġabove , Ġboth Ġnetworks Ġin ĠFigure ĠFIG REF 24 Ġexhibit Ġgenerally Ġdecreasing Ġlevels Ġof Ġpolarization Ġtowards Ġthe Ġend Ġof Ġthe ĠCold ĠWar Ġand Ġthen Ġsubsequent Ġincreases Ġor Ġstagnation Ġafterwards . ĠInterestingly , Ġthe Ġspeech Ġnetwork Ġdisplays Ġslightly Ġhigher Ġlevels Ġof Ġpolarization Ġthan Ġthe Ġideal Ġpoint Ġnetwork . ĠThis Ġindicates Ġthat Ġmore Ġheterogeneity Ġexists Ġin Ġspeeches Ġthan Ġvotes , Ġand Ġalign s Ġwith ĠFigure Ġ1 Ġand ĠFigure Ġ2 a Ġin Ġthe Ġmain Ġbody Ġof Ġthe Ġpaper . </s> For ĠNATO - Wars aw Ġin ĠFigure ĠFIG REF 25 Ġ, Ġthe Ġspeech Ġnetwork Ġfollows Ġa Ġsimilar Ġtrend Ġas Ġusing Ġroll Ġcall Ġdata Ġalone : Ġpol arity Ġdecreases Ġtowards Ġthe Ġend Ġof Ġthe ĠCold ĠWar . ĠIt Ġappears , Ġhowever , Ġthat Ġthe Ġspeech Ġweighted Ġnetwork Ġpicks Ġup Ġhigher Ġvariation Ġin Ġpreferences , Ġwhich Ġindicates Ġthat Ġlooking Ġto Ġideal Ġpoint Ġdata Ġalone Ġmight Ġover - Ġor Ġunder - est imate Ġlevels Ġof Ġpreference Ġpolarization Ġat Ġcertain Ġpoints Ġin Ġtime . ĠFinally , Ġthe ĠWest - Rest Ġalignment Ġprovides Ġfurther Ġconfirmation Ġthat Ġpol arity Ġcan Ġbe Ġdetected Ġin Ġthe Ġvotes Ġand Ġspeeches : ĠWest - Rest Ġmembers Ġexhibit Ġhigher Ġlevels Ġof Ġpolarization Ġcompared Ġto Ġthe ĠAssembly - level . ĠPolar ization Ġexhibits Ġa Ġdownward Ġtrend Ġin Ġthe Ġpost - Cold ĠWar Ġera ĠâĢĵ Ġthe Ġperiod Ġin Ġwhich Ġscholars Ġnote Ġan Ġincreased Ġprominence Ġof Ġliberal , Ġinter dependent Ġglobalization . ĠThese Ġresults Ġalign Ġwith Ġthe Ġtrends Ġfound Ġby ĠB IB REF 22 Ġ. ĠThese Ġresults Ġindicate Ġthat Ġweighted Ġword Ġcounts Ġprovide Ġuseful Ġinformation , Ġbut Ġwe Ġaim Ġto Ġleverage Ġthe Ġinformation Ġin Ġthe Ġspeeches Ġin Ġa Ġmore Ġsophisticated Ġway Ġand Ġin Ġa Ġway Ġthat Ġcan Ġincrease Ġour Ġconfidence Ġthat Ġdivergence Ġis Ġnot Ġsimply Ġdue Ġto Ġdifferences Ġin Ġword Ġchoice . ĠFor Ġthis Ġtask , Ġwe Ġmove Ġbeyond Ġthe ĠB OW Ġapproach Ġand Ġuse Ġvector Ġspace Ġrepresentations . </s> At Ġpresent , Ġthe Ġdevelopment Ġof Ġvector Ġspace Ġmodels Ġfor Ġtextual Ġdata Ġis Ġone Ġof Ġthe Ġareas Ġthat Ġhas Ġattracted Ġthe Ġmost Ġresearch Ġinterest Ġin Ġnatural Ġlanguage Ġprocessing . ĠThis Ġinterest Ġis Ġmotivated Ġby Ġthe Ġdesire Ġto Ġmove Ġaway Ġfrom Ġsimple Ġcounts Ġand Ġweights Ġof Ġwords , Ġtowards Ġrepresentations Ġwhich Ġcan Ġpreserve Ġword Ġcontext Ġand Ġlinguistic Ġfeatures Ġof Ġhuman Ġspeech . ĠStudies Ġfind Ġthat Ġthese Ġapproaches Ġare Ġnot Ġonly Ġintuitive ly Ġdesirable , Ġbut Ġalso Ġincrease Ġclassification Ġaccuracy Ġin Ġmachine Ġlearning Ġtasks . </s> Vector Ġspace Ġapproaches Ġinvolve Ġthe Ġembed ding Ġof Ġdocument Ġfeatures Ġfrom Ġone Ġdimension Ġper Ġword Ġspace Ġto Ġa Ġcontinuous , Ġlower Ġdimensional Ġvector Ġspace . ĠEach Ġdocument Ġfeature Ġis Ġrepresented Ġas Ġa Ġreal - valued Ġvector Ġand Ġit Ġhas Ġbeen Ġshown Ġthat Ġthese Ġrepresentations Ġretain Ġdesirable Ġsynt act ical Ġqualities , Ġsuch Ġas Ġcontext Ġand Ġstructure Ġof Ġspeech . ĠTrad itionally , Ġdimension ality Ġreduction Ġcould Ġbe Ġobtained Ġthrough Ġlatent Ġsemantic Ġanalysis Ġ( L SA ) Ġused Ġto Ġfactor ize Ġthe Ġfeature Ġmat rices , Ġbut Ġtwo Ġrecently Ġdeveloped Ġmodels Ġhave Ġbeen Ġintroduced Ġwhich Ġrely Ġon Ġdifferent Ġlogic : Ġword 2 vec Ġfrom ĠMik ol ov Ġand Ġthe Ġresearch Ġgroup Ġat ĠGoogle , Ġand Ġthe Ġglobal Ġvectors Ġfor Ġword Ġrepresentations Ġ( G lo Ve ) Ġun super vised Ġlearning Ġalgorithm Ġfrom Ġthe ĠStanford ĠNatural ĠLanguage ĠProcessing Ġgroup . </s> We Ġuse ĠGl o Ve Ġintroduced Ġby ĠB IB REF 15 Ġbecause Ġwhile ĠL SA Ġtends Ġto Ġmaximize Ġthe Ġstatistical Ġinformation Ġused , Ġit Ġdoes Ġnot Ġperform Ġwell Ġon Ġanalogy Ġtasks . ĠWord 2 vec Ġdoes Ġbetter Ġon Ġthe Ġanalogy Ġtest Ġbut Ġdoes Ġnot Ġutilize Ġstatistics Ġof Ġthe Ġcorpus Ġbecause Ġit Ġtrains Ġon Ġlocal Ġcontext Ġwindows . ĠGl o Ve Ġwas Ġintroduced Ġto Ġhelp Ġbridge Ġthis Ġgap Ġand Ġcombine Ġboth Ġdesirable Ġqualities . ĠIt Ġis Ġa Ġlog Ġbil in ear , Ġweighted Ġleast Ġsquares Ġmodel Ġthat Ġtrains Ġon Ġglobal Ġword - word Ġco - occ ure nce Ġcounts Ġand Ġthus Ġmakes Ġefficient Ġuse Ġof Ġthe Ġstatistics . ĠB IB REF 15 Ġshow Ġthat Ġtheir Ġapproach Ġyields Ġstate - of - the - art Ġperformance Ġon Ġthe Ġword Ġanalogy Ġtask . ĠGl o Ve Ġis Ġsometimes Ġcriticized Ġfor Ġscal ability Ġissues Ġbut Ġgiven Ġthat Ġwe Ġare Ġworking Ġwith Ġa Ġfixed Ġsize Ġcorpus Ġthis Ġdoes Ġnot Ġpose Ġan Ġissue Ġfor Ġour Ġanalysis . ĠReaders Ġare Ġencouraged Ġto Ġconsult Ġthe ĠGl o Ve Ġpaper Ġfor Ġfull Ġmodel Ġdetails , Ġbut Ġwe Ġdescribe Ġour Ġapproach Ġand Ġdecisions Ġhere . ĠThe Ġmodel Ġis Ġexpressed Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġrepresents Ġparameters , ĠIN LINE FORM 1 Ġis Ġthe Ġvocabulary Ġsize , ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġare Ġcolumn Ġand Ġrow Ġword Ġvectors , ĠIN LINE FORM 4 Ġis Ġthe Ġco - occ urrence Ġmatrix Ġof Ġall Ġpairs Ġof Ġwords Ġthat Ġever Ġco - occ ur , Ġand ĠIN LINE FORM 5 Ġis Ġa Ġweight ing Ġfunction Ġwhich Ġassigns Ġlower Ġweights Ġto Ġwords Ġwhich Ġfrequently Ġco - occ ur . ĠThis Ġlatter most Ġterm Ġserves Ġas Ġa Ġcap Ġon Ġvery Ġfrequent Ġwords , Ġfor Ġexample Ġarticles Ġlike ĠâĢ ľ the " Ġwhich Ġprovide Ġlittle Ġpredictive Ġinformation . ĠThe Ġalgorithm Ġseeks Ġto Ġminimize Ġthe Ġdistance Ġbetween Ġthe Ġinner Ġproduct Ġof Ġthe Ġword Ġvectors Ġand Ġthe Ġlog Ġcount Ġof Ġthe Ġco - occ urrence Ġof Ġthe Ġtwo Ġwords . ĠCompared Ġto Ġskip - gram Ġapproaches Ġwhich Ġupdate Ġat Ġeach Ġcontext Ġwindow , Ġit Ġis Ġclear Ġfrom Ġthe Ġutilization Ġof ĠIN LINE FORM 6 Ġthat Ġthe Ġmodel Ġtrains Ġrelatively Ġquickly Ġsince Ġit Ġuses Ġthe Ġknown Ġcorpus Ġstatistic Ġof Ġword Ġco - occ ur rences Ġfor Ġthe Ġentire Ġcorpus Ġat Ġonce . </s> We Ġfirst Ġstem , Ġtoken ize , Ġand Ġconvert Ġthe Ġwords Ġto Ġlower case . ĠUnlike Ġa ĠB OW Ġapproach , Ġhowever , Ġthe Ġpunct uation Ġis Ġretained . ĠThe Ġmodel Ġis Ġtrained Ġon Ġeach Ġindividual Ġyear Ġin Ġthe Ġcorpus Ġwith Ġthe Ġvocabulary Ġpr uned Ġto Ġinclude Ġa Ġminimum Ġterm Ġcount Ġof Ġ4 Ġacross Ġdocuments Ġand Ġthe Ġterm Ġmust Ġexist Ġin Ġ25 % Ġof Ġthe Ġdocuments . ĠThese Ġrelatively Ġstringent Ġparameter Ġlevels Ġare Ġemployed Ġbecause Ġwe Ġtrain Ġon Ġindividual Ġyears Ġin Ġorder Ġto Ġavoid Ġlanguage Ġdrift Ġover Ġtime Ġand Ġto Ġensure Ġthat Ġour Ġestimated Ġembed d ings Ġcorrespond Ġto Ġthe Ġexact Ġpolicy Ġlanguage Ġused Ġin Ġa Ġgiven Ġyear . ĠWe Ġemploy Ġa Ġskip Ġgram Ġwindow Ġof Ġ4 Ġand Ġsearch Ġusing Ġa Ġword Ġvector Ġsize Ġof Ġ50 . ĠAt Ġpresent , Ġwe Ġfollow Ġthe Ġcomputer Ġscience Ġliterature Ġsuggestion Ġof Ġtuning Ġthese Ġparameters Ġuntil Ġreasonable Ġand Ġreliable Ġlinear Ġcombinations Ġof Ġlanguage Ġare Ġlocated . ĠFuture Ġwork Ġshould Ġexplore Ġin Ġgreater Ġdetail Ġhow Ġsystematic Ġtuning Ġdecisions Ġfor Ġsocial Ġscience Ġapplications Ġcan Ġbe Ġmade . </s> M ik ol ov Ġand Ġcollabor ors Ġintroduce Ġa Ġnew Ġevaluation Ġscheme Ġbased Ġon Ġword Ġanalog ies Ġthat Ġexamines Ġdimensions Ġof Ġdifference Ġin Ġvector Ġspace , Ġas Ġopposed Ġto Ġscal ar Ġdistances Ġbetween Ġvectors ĠB IB REF 13 Ġ, ĠB IB REF 14 Ġ. ĠIn Ġorder Ġto Ġvalidate Ġthe Ġquality Ġof Ġthe Ġlocated Ġembed d ings , Ġwe Ġthus Ġfollow Ġcurrent Ġstandard Ġpractice Ġand Ġassess Ġwhether Ġreasonable Ġlinear Ġcombinations Ġof Ġwords Ġcan Ġbe Ġreturned . ĠThe Ġmain Ġbody Ġof Ġthe Ġpaper Ġpresents Ġthree Ġexamples , Ġand Ġa Ġfurther Ġexample Ġis Ġpresented Ġhere : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġdescribes Ġa Ġvector Ġspace Ġlocation Ġof Ġthe Ġgiven Ġfeature Ġand Ġthe Ġcos ine Ġdistance Ġbetween Ġeach Ġvector Ġspace Ġlocation Ġis Ġadded Ġor Ġsubt racted Ġto Ġfind Ġthe Ġclosest Ġvector Ġspace Ġfeature Ġ( present ed Ġon Ġthe Ġright Ġhand - side Ġof Ġthe Ġequality Ġwith Ġthe Ġcos ine Ġsimilarity Ġprinted Ġunderneath ). ĠThe Ġexample Ġis Ġinterpreted Ġas : Ġif Ġwe Ġadd Ġthe Ġlocation Ġof ĠIN LINE FORM 1 Ġto ĠIN LINE FORM 2 Ġ, Ġwe Ġarrive Ġat Ġthe Ġvector Ġspace Ġlocation Ġof Ġthe Ġword ĠIN LINE FORM 3 Ġ. ĠThis , Ġas Ġwell Ġas Ġthe Ġexamples Ġpresented Ġin Ġthe Ġmain Ġbody , Ġprovide Ġsome Ġindication Ġthat Ġintuitive Ġand Ġreasonable Ġembed d ings Ġhave Ġbeen Ġlocated . </s> Vector Ġspace Ġrepresentations Ġare Ġparticularly Ġpromising Ġfor Ġthe Ġmeasurement Ġof Ġpolarization . ĠIn Ġcontrast Ġto Ġa ĠB OW Ġapproach , Ġwe Ġwant Ġto Ġknow Ġnot Ġonly Ġthat Ġtwo Ġactors Ġuse Ġ( dis ) similar Ġwords , Ġbut Ġwe Ġwant Ġhigh Ġresolution Ġinsights Ġinto Ġhow Ġand Ġwhen Ġthey Ġspeak Ġdifferently Ġon Ġdifferent Ġtopics . ĠFor Ġexample , Ġwe Ġwould Ġlike Ġto Ġcapture Ġthe Ġdiss imilar ity Ġof Ġstatements Ġlike Ġ" we Ġoppose Ġthe Ġproliferation Ġof Ġnuclear Ġweapons " Ġversus Ġ" the Ġproliferation Ġof Ġatom Ġbombs Ġis Ġnecessary ." ĠThe Ġwords ĠâĢ ľ necessary " Ġand ĠâĢ ľ opp ose " Ġwould Ġbe Ġcounted Ġin Ġa ĠB OW Ġmatrix , Ġbut Ġtheir Ġusage Ġin Ġthe Ġcontext Ġof Ġnuclear Ġweapons Ġwould Ġbe Ġlost . ĠFurther , Ġif Ġone Ġstate Ġsays ĠâĢ ľ nuclear Ġweapon " Ġand Ġanother Ġsays ĠâĢ ľ atomic Ġbomb ," Ġthe Ġtwo Ġphrases Ġhave Ġno Ġwords Ġin Ġcommon Ġand Ġthe Ġphrases Ġwill Ġbe Ġthought Ġto Ġbe Ġdistant Ġor Ġdiss imilar , Ġdespite Ġreferring Ġto Ġthe Ġsame Ġthing . ĠAlthough ĠB OW Ġapproaches Ġoften Ġperform Ġvery Ġwell , Ġfor Ġthe Ġmeasurement Ġof Ġpolarization Ġin ĠIR , Ġwe Ġneed Ġto Ġmake Ġsure Ġthat Ġthe Ġdifferences Ġuncovered Ġare Ġnot Ġsimply Ġdue Ġto Ġdifferent Ġword Ġusage Ġor Ġpolicy Ġtopics Ġbeing Ġdiscussed . </s> To Ġmeasure Ġexpressed Ġ( dis ) ag reement Ġin Ġthese Ġspeeches , Ġit Ġis Ġnecessary Ġto Ġderive Ġa Ġdocument - level Ġrepresentation Ġof Ġthe Ġlearned Ġembed d ings . ĠAlthough Ġwell - established Ġmeasurements Ġbased Ġon Ġcos ine Ġsimilarity , ĠEucl ide an Ġdistance , Ġor ĠPearson Ġcorrelations Ġcould Ġbe Ġapplied Ġto Ġthe Ġword Ġembed d ings , Ġwe Ġutilized Ġthe Ġrelaxed Ġvariant Ġof Ġa Ġnewly Ġintroduced Ġdocument Ġdistance Ġmeasure Ġthat Ġexploits Ġinformation Ġcontained Ġin Ġboth Ġthe Ġword Ġembed d ings Ġand Ġterm - document Ġmat rices : Ġthe Ġ( rel ax ed ) ĠWord ĠM over 's ĠDistance Ġ( RW MD ) ĠB IB REF 17 Ġ. </s> W MD Ġinnov ates Ġby Ġleveraging Ġthe Ġfinding Ġthat Ġembed ding Ġdistances Ġbetween Ġword Ġvectors Ġare Ġsem antically Ġmeaningful . ĠW MD Ġrepresents Ġtext Ġdocuments Ġas Ġa Ġweighted Ġpoint Ġcloud Ġof Ġembedded Ġwords Ġwhere Ġthe Ġdistance Ġbetween Ġtwo Ġdocuments Ġis Ġthe Ġminimum Ġcumulative Ġdistance Ġthat Ġwords Ġfrom Ġdocument ĠA Ġwould Ġneed Ġto Ġtravel Ġto Ġmatch Ġexactly Ġthe Ġpoint Ġcloud Ġof Ġdocument ĠB . ĠThis Ġhas Ġbeen Ġshown Ġto Ġyield Ġstate - of - the - art Ġclassification Ġaccuracy ĠB IB REF 39 Ġ. ĠAlthough ĠW MD Ġis Ġrelatively Ġfast Ġto Ġcompute , Ġwe Ġuse Ġthe Ġrelaxed Ġvariant Ġ( RW MD ), Ġwhich Ġresults Ġin Ġtighter Ġbounds Ġand Ġis Ġshown Ġto Ġyield Ġlower Ġtest Ġerror Ġrates . ĠIn Ġshort , Ġthis Ġrelax es Ġthe Ġoptimization Ġproblem Ġthrough Ġthe Ġremoval Ġof Ġone Ġof Ġthe Ġtwo Ġconstraints . ĠIf Ġwe Ġlet ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġbe Ġthe ĠB OW Ġrepresentations Ġof Ġtwo Ġdocuments Ġin Ġthe ĠIN LINE FORM 2 Ġdimensional Ġsimple x Ġof Ġword Ġdistributions Ġwhich Ġwe Ġobtained Ġabove , Ġwe Ġcan Ġexpress ĠRW MD Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġsparse Ġflow Ġmatrix Ġwhere ĠIN LINE FORM 1 Ġdenotes Ġhow Ġmuch Ġof Ġword ĠIN LINE FORM 2 Ġin ĠIN LINE FORM 3 Ġtravels Ġto Ġword ĠIN LINE FORM 4 Ġin ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġis Ġdefined Ġto Ġbe Ġthe Ġdistance Ġbetween Ġthe Ġtwo Ġdocuments Ġas Ġthe Ġminimum Ġweighted Ġcumulative Ġcost Ġrequired Ġto Ġmove Ġall Ġwords Ġfrom ĠIN LINE FORM 7 Ġto ĠIN LINE FORM 8 Ġ. ĠThen , Ġthe Ġoptimal Ġsolution Ġis Ġfound Ġwhen Ġeach Ġword Ġin ĠIN LINE FORM 9 Ġmoves Ġall Ġof Ġits Ġprobability Ġmass Ġto Ġthe Ġmost Ġsimilar Ġword Ġin ĠIN LINE FORM 10 Ġ. ĠThis Ġoptimal Ġmatrix ĠIN LINE FORM 11 Ġis Ġdecided Ġby : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġdistance Ġof Ġinterest Ġwhich Ġwe Ġnormal ize Ġand Ġconvert Ġto Ġa Ġsimilarity Ġscore Ġusing Ġequation Ġ4 Ġabove Ġand ĠIN LINE FORM 1 Ġ, Ġrespectively . ĠThe Ġresult Ġis Ġa Ġlist Ġof ĠIN LINE FORM 2 Ġmat rices ĠIN LINE FORM 3 Ġfor Ġeach Ġyear Ġin Ġthe Ġcorpus Ġwhere ĠIN LINE FORM 4 Ġis Ġthe Ġspeech Ġsimilarity Ġscore Ġbetween Ġstates ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġfor Ġthe Ġgiven Ġyear Ġin Ġthe Ġcorpus Ġwith Ġthe Ġdi agon als Ġof Ġthe Ġmat rices Ġset Ġto Ġ0 . </s> Network ĠAnalysis </s> Although Ġwe Ġpresent Ġa Ġnovel Ġapproach Ġfor Ġleveraging Ġposition Ġsimilarity Ġinformation Ġfound Ġin Ġpolitical Ġtext , Ġit Ġis Ġnot Ġimmediately Ġobvious Ġthat Ġthis Ġapproach Ġis Ġuseful Ġfor Ġthe Ġtask Ġof Ġpolitical Ġanalysis . ĠWe Ġposit Ġthat Ġfor Ġthis Ġapproach Ġto Ġbe Ġconsidered Ġuseful , Ġit Ġshould Ġbe Ġcapable Ġof Ġproviding Ġinformation Ġuseful Ġfor Ġthe Ġperformance Ġof Ġinference Ġon Ġobserved Ġstate Ġbehavior . ĠWe Ġchoose Ġviolent Ġconflict Ġonset Ġbecause Ġthis Ġis Ġone Ġof Ġthe Ġenduring Ġmethodological Ġchallenges Ġfaced Ġby Ġthe Ġdiscipline . ĠFor Ġthis Ġtask , Ġwe Ġaim Ġto Ġsee Ġwhether Ġor Ġnot Ġthese Ġembedded Ġspeeches Ġprovide Ġinformation Ġon Ġstate Ġpreferences Ġwhich Ġimprove Ġupon Ġcurrent Ġout - of - sample Ġpredictions Ġrelative Ġto Ġcurrent Ġmodels Ġwhich Ġemploy ĠUN Ġroll Ġcall Ġdata . ĠWe Ġchoose Ġto Ġcompare Ġour Ġapproach Ġto Ġthe Ġrecently Ġpublished Ġmodels Ġof ĠB IB REF 8 Ġ, Ġbecause Ġtheir Ġgoal Ġand Ġapplications Ġwhich Ġuse ĠUN Ġroll Ġcall Ġdata Ġclosely Ġparallel Ġour Ġresearch Ġmotivations . ĠThis Ġsection Ġprovides Ġfurther Ġinformation Ġon Ġthe Ġgraph Ġpartition ing Ġapproach , Ġthe Ġnew ĠMult il ayer ĠExt raction Ġalgorithm , Ġand Ġmodel Ġperformance Ġassessments . </s> For Ġclusters Ġbased Ġon Ġspeeches Ġalone , Ġwe Ġfollow ĠPaul s Ġand ĠCran mer 's Ġ( ĠB IB REF 8 Ġ) Ġapproach Ġthrough Ġthe Ġperformance Ġof Ġ5 - ne arest Ġneighbor Ġclust ering Ġon Ġthe Ġmatrix Ġof ĠRW MD Ġstate - state Ġsimilarities , Ġwhich Ġyields Ġcandidates Ġfor Ġmembership Ġin Ġan Ġaffinity Ġcommunity . ĠAs Ġwe Ġare Ġdealing Ġwith Ġtextual Ġdata , Ġand Ġin Ġcontrast Ġto Ġtheir Ġsign - test Ġapproach , Ġwe Ġassign Ġties Ġbetween Ġaffinity Ġcandidate Ġstate Ġpairs ĠIN LINE FORM 0 Ġbased Ġon Ġrelatively Ġstrict Ġsimilarity Ġthresholds Ġbetween Ġ0 . 50 Ġand Ġ0 . 60 . ĠThe Ġresult Ġis Ġa Ġsquare ĠIN LINE FORM 1 Ġadj ac ency Ġmatrix ĠIN LINE FORM 2 Ġof Ġun weight ed Ġties Ġfor Ġeach Ġyear Ġwhich Ġcontains Ġall Ġstates Ġwho Ġvoted Ġand Ġdelivered Ġa ĠGD Ġstatement . ĠTo Ġlocate Ġmultiple x Ġbloc s , Ġwe Ġuse Ġthe Ġvoting Ġclusters Ġfound Ġby ĠPaul s Ġand ĠCran mer Ġ( ĠB IB REF 8 Ġ) Ġas Ġone Ġlayer , Ġand Ġthe Ġspeech Ġclusters Ġjust Ġdescribed Ġas Ġa Ġsecond Ġlayer . ĠThe ĠMult il ayer ĠExt raction Ġalgorithm Ġ( described Ġbelow ) Ġis Ġthen Ġapplied Ġto Ġthese Ġtwo Ġlayers , Ġwhich Ġreturns Ġcommunity Ġmembership Ġlabels Ġin Ġvectors . ĠThese Ġare Ġtransformed Ġinto Ġadj ac ency Ġmat rices Ġof Ġun weight ed Ġties Ġfor Ġeach Ġyear . ĠThis Ġis Ġa Ġstrong Ġtest Ġfor Ġour Ġhypothesized Ġeffects Ġbecause Ġmult il ayer Ġcommunities Ġmust Ġbe Ġdetectable Ġafter Ġindividual Ġlayers Ġhave Ġalready Ġbeen Ġclustered . </s> The Ġintuition Ġof Ġemploying Ġa Ġthreshold Ġis Ġthat Ġevery Ġnode Ġis Ġconnected Ġto Ġevery Ġother Ġnode Ġin Ġthe Ġnetwork Ġthrough Ġa Ġgiven Ġtext Ġsimilarity , Ġbut Ġthis Ġcomput ationally Ġbecomes Ġinf eas ible Ġand Ġunnecessary Ġas Ġmany Ġof Ġthe Ġties Ġare Ġlow Ġvalued . ĠFurther , Ġin Ġorder Ġto Ġbe Ġcompatible Ġwith Ġthe ĠTER G Ms , Ġthis Ġbin ar ization Ġis Ġnecessary , Ġand Ġso Ġwe Ġfollow Ġthreshold ing Ġguidelines Ġoutlined Ġin Ġthe Ġnetwork Ġscience Ġliterature , Ġsee ĠB IB REF 40 Ġ, ĠB IB REF 41 Ġ, ĠB IB REF 42 Ġ, ĠB IB REF 43 Ġ. ĠBecause Ġthe Ġchoice Ġof Ġthreshold Ġis Ġcase Ġdependent Ġand Ġmust Ġtake Ġinto Ġaccount Ġthe Ġtype Ġof Ġdata Ġunder Ġscrutiny , Ġwe Ġturn Ġto Ġthe ĠN LP Ġliterature , Ġwhich Ġsuggests Ġthat Ġa Ġthreshold Ġof ĠIN LINE FORM 0 Ġis Ġconsidered Ġto Ġbe Ġa Ġrelatively Ġcommon Ġbenchmark Ġfor Ġdisc arding Ġlow - information Ġsimilarity Ġscores ĠB IB REF 44 Ġ, ĠB IB REF 45 Ġ, ĠB IB REF 46 Ġ. ĠAs Ġdifferent Ġthresholds Ġresult Ġin Ġdifferent Ġgraphs , Ġwe Ġcheck Ġand Ġconfirm Ġthe Ġrobust ness Ġof Ġour Ġresults Ġat Ġdifferent Ġlevels Ġand Ġreport Ġthese Ġresults Ġbelow . </s> Cl usters Ġbased Ġon Ġspeeches Ġor Ġvotes Ġin Ġisolation Ġprovide Ġone Ġslice Ġof Ġinformation Ġabout Ġstate Ġpreferences , Ġbut Ġwe Ġare Ġmotivated Ġto Ġlocate Ġan Ġapproach Ġwhich Ġexploits Ġboth Ġsources Ġof Ġinformation Ġin Ġtandem . ĠThis Ġis Ġbroadly Ġaligned Ġwith Ġother Ġwork Ġin Ġpolitical Ġscience Ġwhich Ġaims Ġto Ġcombine Ġvotes Ġand Ġspeeches Ġin Ġstatistical Ġmodels . ĠSpecifically , Ġwe Ġposit Ġthat Ġvaluable Ġand Ġperhaps Ġdifferent Ġinformation Ġmight Ġbe Ġgarnered Ġfrom Ġthe Ġvoting Ġbehavior Ġand Ġspeeches Ġmade Ġby Ġstates Ġand Ġwe Ġconsider Ġthese Ġas Ġtwo Ġelementary Ġlayers Ġof Ġa Ġmult il ayer Ġnetwork . ĠA Ġtwo - layer Ġnetwork Ġillustration Ġis Ġpresented Ġin ĠFigure ĠFIG REF 32 Ġ. ĠExtract ing Ġcommunities Ġbased Ġon Ġthis Ġmult il ayer Ġnetwork Ġprovides Ġone Ġstrategy Ġof Ġhol istically Ġexploiting Ġboth Ġsources Ġof Ġpreference Ġinformation Ġin Ġtandem Ġand Ġis Ġone Ġof Ġthe Ġcurrent Ġforefront s Ġof Ġnetwork Ġscience Ġresearch ĠB IB REF 26 Ġ. </s> At Ġpresent , Ġmost Ġmult il ayer Ġcommunity Ġdetection Ġhe ur istics Ġrequire Ġthe Ġcommunity Ġstructure Ġto Ġbe Ġhom ogenous Ġat Ġeach Ġlayer , Ġbut Ġas Ġdisplayed Ġabove , Ġspeech Ġand Ġvoting Ġpatterns Ġoften Ġdiver ge . ĠA Ġrecently Ġproposed Ġsolution Ġto Ġthis Ġproblem Ġis Ġthe ĠMult il ayer ĠExt raction Ġprocedure ĠB IB REF 27 Ġ. ĠThe Ġalgorithm Ġidentifies Ġdensely Ġconnected Ġvertex - l ayers Ġin Ġmult il ayer Ġnetworks Ġthrough Ġa Ġsignificance - based Ġscore Ġthat Ġquant ifies Ġthe Ġconnectivity Ġof Ġan Ġobserved Ġvertex - layer Ġset Ġby Ġcomparison Ġwith Ġa Ġmult il ayer Ġfixed Ġdegree Ġrandom Ġgraph Ġmodel . ĠFor Ġour Ġanalysis , Ġthe Ġclusters Ġfrom Ġvoting Ġdata Ġcomprise Ġthe Ġfirst Ġlayer Ġand Ġthe Ġtext - based Ġclusters Ġcomprise Ġthe Ġsecond Ġlayer Ġof Ġthe Ġgraph Ġfor Ġeach Ġyear . </s> This Ġunique Ġapproach Ġto Ġcommunity Ġdetection Ġallows Ġus Ġto Ġexploit Ġpreference Ġsimilarity Ġinformation Ġacross Ġspeeches Ġand Ġvoting Ġhabits Ġwithout Ġrequiring Ġstates Ġto Ġbe Ġsimilar Ġon Ġboth Ġdimensions . ĠForm ally , Ġour Ġvote - speech Ġlayered Ġnetwork Ġis Ġa Ġnode - aligned ĠIN LINE FORM 0 Ġund irect ed Ġmult il ayer Ġnetwork ĠIN LINE FORM 1 Ġwith Ġno Ġself - ed ges ĠIN LINE FORM 2 Ġ. ĠThe Ġmult il ayer Ġnetwork Ġis Ġnode Ġaligned Ġbecause Ġwe Ġadd Ġextr aneous Ġnodes Ġsuch Ġthat ĠIN LINE FORM 3 Ġwhere Ġtypically Ġthe ĠIN LINE FORM 4 Ġ. ĠSuch Ġan Ġapproach Ġis Ġshown Ġin Ġthe Ġliterature Ġto Ġyield Ġmat hematically Ġdesirable Ġproperties Ġfor Ġthe Ġtask Ġof Ġmult il ayer Ġcommunity Ġdetection ĠB IB REF 26 Ġand Ġis Ġalso Ġrequired Ġfor Ġthe ĠTER GM Ġestim ations . </s> The ĠMult il ayer ĠExt raction Ġprocedure Ġloc ates Ġcommunities Ġacross Ġthe Ġtwo Ġnetwork Ġlayers Ġand Ġprovides Ġa Ġresulting Ġmembership Ġidentification Ġvector Ġwhich Ġcan Ġbe Ġused Ġto Ġconstruct Ġa Ġone - mode Ġrepresentation Ġof Ġthe Ġcross - layer Ġcommunities Ġfor Ġa Ġgiven Ġyear Ġwhere Ġan Ġedge Ġbetween ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġexists Ġif Ġthe Ġextraction Ġprocedure Ġidentifies ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġas Ġbelonging Ġto Ġthe Ġsame Ġcommunity . ĠIf Ġthe Ġextraction Ġprocedure Ġfails Ġto Ġlocate Ġmult il ayer Ġcommunities , Ġwe Ġcan Ġconclude Ġthat Ġno Ġinformation Ġis Ġlost Ġby Ġusing Ġthe Ġmore Ġcommon Ġaggregation Ġprocedure Ġof Ġconsidering Ġthe Ġunion Ġof Ġedges Ġin Ġthe Ġtwo Ġlayers ĠIN LINE FORM 4 Ġand Ġconsider Ġan Ġedge Ġto Ġexist Ġbetween ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġif Ġan Ġedge Ġexists Ġin Ġeither Ġof Ġthe Ġtwo Ġlayers , Ġsee ĠB IB REF 26 Ġ. ĠThe Ġresult Ġis Ġa Ġone Ġmode Ġnetwork Ġfor Ġeach Ġyear Ġbased Ġon Ġthe Ġtwo Ġsources Ġof Ġpreference Ġinformation . ĠThese Ġmultiple x Ġbloc s Ġare Ġemployed Ġas Ġa Ġsubstitute Ġfor Ġvotes - Ġand Ġtext - based Ġclusters Ġin ĠModel Ġ4 , Ġand Ġthe Ġresults Ġare Ġreported Ġin Ġ[ table : ter gm ] Table Ġ1 Ġof Ġthe Ġmain Ġpaper . </s> For Ġthe Ġresults Ġreported Ġin Ġthe Ġpaper 's Ġmain Ġbody , Ġwe Ġfound Ġthat ĠMult il ayer ĠExt raction Ġdetected Ġheter ogenous Ġcommunity Ġstructure Ġin Ġthe Ġtwo Ġlayers Ġof Ġspeeches Ġand Ġvotes Ġfor Ġ12 Ġof Ġthe Ġyears . ĠFor Ġthe Ġother Ġ18 Ġyears , Ġno Ġmult il ayer Ġcommunities Ġwere Ġdetected Ġand Ġtherefore Ġcould Ġbe Ġaggreg ated Ġinto Ġa Ġsingle Ġnetwork Ġwithout Ġthe Ġrisk Ġof Ġignoring Ġmult il ayer Ġcommunity Ġstructure Ġin Ġthe Ġanalysis . ĠThis Ġhelps Ġto Ġexplain Ġwhy Ġsome Ġfindings Ġusing ĠUN Ġroll Ġcall Ġdata Ġalone Ġcome Ġup Ġwith Ġvarying Ġconclusions . ĠRoll Ġcall Ġdata Ġwould Ġperform Ġbetter Ġin Ġyears Ġwhere Ġcomplex Ġspeech - vote Ġdependencies Ġdid Ġnot Ġexist Ġ( i . e . Ġwhen Ġa Ġgiven Ġstate 's Ġvotes Ġand Ġspeeches Ġare Ġboth ĠâĢ ľ similar " Ġand Ġtherefore Ġconsidering Ġone Ġsource Ġof Ġinformation Ġalone Ġis Ġanaly t ically Ġsufficient ). ĠHowever , Ġin Ġyears Ġwhere Ġa Ġgiven Ġstate 's Ġvoting Ġbehavior Ġand Ġspeeches Ġdiver ge Ġ( e . g . Ġa ĠNATO Ġmember Ġvoting Ġwith Ġthe Ġbloc Ġbut Ġdelivering Ġa Ġspeech Ġwhich Ġcontains Ġposition Ġinformation Ġwhich Ġdiver ges Ġfrom Ġthe Ġrest Ġof Ġthe Ġbloc ), Ġthen Ġfavoring Ġone Ġsource Ġof Ġinformation Ġover Ġthe Ġother Ġwill Ġlikely Ġyield Ġmisleading Ġestimates Ġof Ġpreference Ġpolarization . ĠThis Ġmult il ayer Ġcommunity Ġdetection Ġapproach Ġallows Ġus Ġto Ġcapture Ġthese Ġpotentially Ġcomplex Ġstructures Ġat Ġboth Ġlevels Ġof Ġpreference Ġinformation . </s> In Ġthe Ġbody Ġof Ġthe Ġmain Ġpaper , Ġwe Ġpresent Ġthe Ġresults Ġof Ġthe Ġoriginal Ġmodel Ġand Ġdate - adjusted Ġmodels Ġof ĠB IB REF 8 Ġ, Ġas Ġwell Ġas Ġour Ġtextual Ġand Ġmultiple x Ġcluster Ġresults . ĠThe Ġreaders Ġcan Ġrefer Ġto Ġthe Ġoriginal Ġpaper Ġof ĠB IB REF 8 Ġfor Ġcomplete Ġdetails Ġon Ġvariable Ġoperational ization Ġand Ġmodel Ġspecification . ĠWe Ġfocus Ġhere Ġon Ġthe Ġperformance Ġof Ġthe Ġmodels . </s> Incre asing Ġattention Ġis Ġdevoted Ġto Ġthe Ġappropriate Ġrole Ġof Ġun super vised Ġmethods Ġin Ġpolitical Ġscience Ġresearch , Ġbecause Ġthey Ġrequire Ġhuman Ġinterpretation Ġand Ġlack Ġwell - defined Ġcriteria Ġfor Ġaccuracy Ġand Ġperformance Ġassessment . ĠBoth Ġthe Ġword Ġembed d ings Ġand Ġmult il ayer Ġcommunity Ġdetection Ġprocedure Ġare Ġun super vised Ġmethods , Ġand Ġso Ġwe Ġposit Ġthat Ġin Ġorder Ġto Ġvalidate Ġthat Ġthese Ġlocated Ġaffinity Ġbloc s Ġreally Ġdo Ġcapture Ġmeaningful Ġpreference Ġsimilarity Ġin Ġinternational Ġpolitics , Ġthey Ġshould Ġbe Ġable Ġto Ġimprove Ġupon Ġour Ġcurrent Ġability Ġto Ġpredict Ġconflict Ġonset Ġout - of - sample . ĠThis Ġwould Ġenable Ġus Ġto Ġmore Ġadequately Ġexplain Ġobserved Ġstate Ġbehavior Ġand Ġprovides Ġa Ġrigorous Ġcriteria Ġfor Ġperformance Ġassessment . ĠAs Ġreported Ġin Ġthe Ġmain Ġpaper , Ġclusters Ġbased Ġon Ġpartitions Ġof Ġour Ġspeech Ġgraph Ġand Ġour Ġspeech - vote Ġmultiple x Ġgraph Ġare Ġboth Ġstatistically Ġsignificant Ġpredict ors Ġof Ġconflict Ġonset . ĠHere , Ġwe Ġassess Ġthe Ġgoodness - of - fit Ġof Ġour Ġmodels , Ġthe Ġout - of - sample Ġpredictive Ġaccuracy Ġof Ġthe Ġmodels , Ġand Ġrobust ness Ġchecks Ġof Ġour Ġresults Ġat Ġdifferent Ġtie Ġsimilarity Ġthresholds . </s> To Ġassess Ġthe Ġin - sample Ġgoodness - of - fit Ġ( GO F ) Ġof Ġexponential Ġrandom Ġgraph Ġmodels , Ġit Ġis Ġcommon Ġto Ġsimulate Ġnetwork Ġstatistics Ġwhich Ġwere Ġnot Ġspecified Ġin Ġthe Ġmodel Ġto Ġsee Ġhow Ġwell Ġthe Ġfitted Ġmodel Ġcan Ġsimulate Ġstatistics Ġon Ġthe Ġoutcome Ġnetwork . ĠIn Ġour Ġcase , Ġthe Ġoutcome Ġnetwork Ġis Ġthe Ġconflict Ġonset Ġnetwork , Ġand Ġwe Ġperform Ġ50 Ġsimulations Ġover Ġeach Ġof Ġthe Ġ30 Ġtime Ġsteps Ġfor Ġ1 , 500 Ġtotal Ġsimulations Ġfor Ġeach Ġof Ġthe Ġfour Ġmodels . ĠThe Ġin - sample Ġareas Ġunder Ġthe ĠR OC Ġand ĠPR Ġcurves Ġfor Ġthe Ġthree Ġmodels Ġof Ġprimary Ġconcern Ġare Ġpresented Ġin ĠFigure ĠFIG REF 36 Ġand ĠFigure ĠFIG REF 37 Ġ, Ġrespectively . ĠFurthermore , Ġthe Ġdy ad - wise Ġand Ġedge - wise Ġshared Ġpartners Ġand Ġmodular ities Ġare Ġpresented Ġfor Ġall Ġfour Ġmodels Ġin ĠFigure ĠFIG REF 38 Ġand ĠFigure ĠFIG REF 39 Ġ. ĠAlthough Ġthe Ġoriginal Ġmodels Ġfrom ĠB IB REF 8 Ġand Ġour Ġtextual Ġand Ġmultiple x Ġmodels Ġall Ġexhibit Ġimpressive ĠGO Fs , Ġthe Ġmultiple x Ġmodel Ġexhibits Ġthe Ġbest Ġin - sample ĠGO F Ġas Ġmeasured Ġby Ġareas Ġunder Ġthe ĠR OC Ġand ĠPR Ġcurves . ĠThis Ġincreases Ġour Ġconfidence Ġin Ġthe Ġmodel Ġspecifications , Ġbut Ġit Ġis Ġnecessary Ġto Ġassess Ġout - of - sample Ġpredictive Ġcapability Ġsince Ġall Ġfour Ġmodels Ġfit Ġquite Ġwell . </s> To Ġassess Ġwhether Ġor Ġnot Ġthe Ġinclusion Ġof Ġtextual Ġor Ġmultiple x Ġclusters Ġimproves Ġupon Ġthe Ġexisting Ġmodels Ġwith Ġroll Ġcall - based Ġclusters Ġalone , Ġwe Ġfollow ĠB IB REF 8 Ġin Ġtraining Ġon Ġfive - year Ġwindows Ġand Ġattempt Ġto Ġpredict Ġthe Ġnext Ġyear Ġof Ġconflict Ġonset . ĠThe Ġmultiple x Ġmodel Ġexhibits Ġa Ġ20 . 5 % Ġincrease Ġin Ġarea Ġunder Ġthe Ġprecision Ġrecall Ġcurve Ġcompared Ġto Ġthe Ġoriginal Ġdate - adjusted Ġmodel Ġ( 1 . 156 Ġvs . Ġ0 . 9 59 ). ĠIn Ġcontrast , Ġthe Ġmodel Ġwith Ġtextual Ġclusters Ġalone Ġunder per forms Ġthe Ġoriginal Ġdate - adjusted Ġmodel Ġ( 0 . 08 1 Ġvs . Ġ0 . 9 59 ). ĠWe Ġplot Ġthese Ġout - of - sample Ġperformance Ġresults Ġfor Ġthe Ġmultiple x Ġmodels Ġalongside Ġother Ġtie Ġthresholds Ġas Ġchecks Ġon Ġthe Ġstability Ġof Ġthe Ġresults Ġin ĠFigure ĠFIG REF 40 Ġwhich Ġdisplays Ġthe Ġsum Ġof Ġthe Ġareas Ġunder Ġthe Ġprecision Ġrecall Ġcurves Ġat Ġvarious Ġthresholds Ġcompared Ġto Ġthe Ġbaseline Ġ( date - adjusted ) Ġmodel Ġpresented Ġin ĠB IB REF 8 Ġ. </s> We Ġfind Ġthat Ġseveral Ġof Ġthe Ġthresholds Ġprovide Ġreliable Ġand Ġstatistically Ġsignificant Ġestimates Ġof Ġconflict Ġonset . ĠIn Ġorder Ġto Ġchoose Ġthe Ġfinal Ġmodel , Ġwe Ġselect Ġthe Ġmodel Ġwith Ġthe Ġlowest Ġvariance Ġin Ġout - of - sample Ġprediction Ġcapability Ġ( i . e . Ġthe Ġarea Ġunder Ġthe Ġprecision Ġrecall Ġcurve ), Ġthat Ġis , Ġwe Ġselect Ġthe Ġmodel Ġwhich Ġyields Ġthe Ġmost Ġconsistent Ġpredictions Ġover Ġtime . ĠBecause Ġout - of - sample Ġpredictions Ġon Ġsparse Ġnetworks Ġ( such Ġas Ġthe Ġconflict Ġonset Ġnetwork ) Ġis Ġa Ġchallenging Ġtask , Ġthis Ġselection Ġapproach Ġhelps Ġto Ġeliminate Ġmodels Ġwith Ġpredictions Ġthat Ġvary Ġwidely Ġfrom Ġone Ġyear Ġto Ġthe Ġnext . ĠAs Ġpreviously Ġmentioned , Ġa Ġthreshold Ġof Ġ. 50 Ġis Ġcommon Ġin Ġthe ĠN LP Ġliterature Ġand Ġso Ġwe Ġreport Ġevery Ġthreshold Ġbetween Ġ[ 0 . 50 , 0 . 60 ] Ġat Ġ. 01 Ġincrements . ĠWe Ġselect Ġthe Ġ. 58 Ġtie Ġthreshold Ġbecause Ġthis Ġmodel Ġexhibits Ġthe Ġlowest Ġvariance Ġin Ġpredictions . </s> To Ġensure Ġthat Ġwe Ġare Ġnot Ġselecting Ġa Ġsingle Ġmodel Ġwhich Ġvastly Ġoutper forms Ġall Ġother Ġthresholds Ġ( i . e . Ġto Ġensure Ġthat Ġour Ġresults Ġare Ġrobust ), Ġwe Ġconsider Ġthe Ġout - of - sample Ġpredictive Ġaccuracy Ġfor Ġthe Ġmodels Ġin Ġwhich Ġthe Ġmultiple x Ġclusters Ġyielded Ġa Ġstatistically Ġsignificant Ġrelationship Ġwith Ġconflict Ġonset . ĠThis Ġwas Ġthe Ġcase Ġfor Ġ7 Ġout Ġof Ġthe Ġ11 Ġthresholds Ġtested . ĠThe Ġsums Ġof Ġthe Ġareas Ġunder Ġthe Ġprecision Ġrecall Ġcurve Ġfor Ġthese Ġmodels Ġare Ġplotted Ġin ĠFigure ĠFIG REF 41 Ġ. ĠThese Ġbox Ġplots Ġmake Ġclear Ġthat Ġour Ġmultiple x Ġmodels Ġdo Ġindeed Ġdisplay Ġincreased Ġout - of - sample Ġpredictive Ġcapability Ġrelative Ġto Ġthe Ġbaseline Ġmodel Ġacross Ġvarious Ġthresholds . </s> The Ġabove Ġamounts Ġto Ġa Ġtool kit Ġfor Ġthe Ġexploitation Ġof Ġmult id imensional Ġinformation Ġon Ġpositions Ġand Ġpreferences Ġin Ġpolitical Ġresearch . ĠWe Ġshow Ġhow Ġthe Ġinformation Ġin Ġtextual Ġdata Ġcan Ġbe Ġuse fully Ġexploited Ġbeyond Ġsimple Ġword Ġcounts Ġand Ġweighted Ġfrequencies . ĠWe Ġshow Ġhow Ġthis Ġinformation Ġcan Ġin Ġits Ġown Ġright Ġbe Ġuseful , Ġbut Ġalso Ġhow Ġit Ġcan Ġbe Ġexploited Ġin Ġtandem Ġwith Ġother Ġexisting Ġsources Ġof Ġpreference Ġinformation Ġlike Ġvotes Ġthrough Ġa Ġmult il ayer Ġnetwork Ġapproach . ĠFinally , Ġwe Ġshow Ġthat Ġthese Ġmeasures Ġare Ġnot Ġonly Ġsubstant ively Ġreasonable , Ġbut Ġcan Ġbe Ġused Ġto Ġextend Ġcurrent Ġstate - of - the - art Ġnetwork Ġmodels Ġwhich Ġinfer Ġthe Ġimpact Ġof Ġpreferences Ġon Ġinternational Ġconflict .