Intelligence Squared Debates
EXTRACTIVE
“Intelligence Squared Debates” (IQ2 for short)
EXTRACTIVE
In this study we use transcripts and results of Oxford-style debates from the public debate series “Intelligence Squared Debates” (IQ2 for short). These debates are recorded live, and contain motions covering a diversity of topics ranging from foreign policy issues to the benefits of organic food. Each debate consists of two opposing teams—one for the motion and one against—of two or three experts in the topic of the particular motion, along with a moderator. Each debate follows the Oxford-style format and consists of three rounds. In the introduction, each debater is given 7 minutes to lay out their main points. During the discussion, debaters take questions from the moderator and audience, and respond to attacks from the other team. This round lasts around 30 minutes and is highly interactive; teams frequently engage in direct conversation with each other. Finally, in the conclusion, each debater is given 2 minutes to make final remarks.
In this study we use transcripts and results of Oxford-style debates from the public debate series “Intelligence Squared Debates” (IQ2 for short). These debates are recorded live, and contain motions covering a diversity of topics ranging from foreign policy issues to the benefits of organic food. Each debate consists of two opposing teams—one for the motion and one against—of two or three experts in the topic of the particular motion, along with a moderator. Each debate follows the Oxford-style format and consists of three rounds. In the introduction, each debater is given 7 minutes to lay out their main points. During the discussion, debaters take questions from the moderator and audience, and respond to attacks from the other team. This round lasts around 30 minutes and is highly interactive; teams frequently engage in direct conversation with each other. Finally, in the conclusion, each debater is given 2 minutes to make final remarks.
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Introduction </s> Public Ġdebates Ġare Ġa Ġcommon Ġplatform Ġfor Ġpresenting Ġand Ġjuxtap osing Ġdiver ging Ġviewpoints ĠAs Ġopposed Ġto Ġmon olog ues Ġwhere Ġspeakers Ġare Ġlimited Ġto Ġexpressing Ġtheir Ġown Ġbeliefs , Ġdebates Ġallow Ġfor Ġparticipants Ġto Ġinteract ively Ġattack Ġtheir Ġopponents ' Ġpoints Ġwhile Ġdefending Ġtheir Ġown . ĠThe Ġresulting Ġflow Ġof Ġideas Ġis Ġa Ġkey Ġfeature Ġof Ġthis Ġconversation Ġgenre . </s> In Ġthis Ġwork Ġwe Ġintroduce Ġa Ġcomputational Ġframework Ġfor Ġcharacter izing Ġdebates Ġin Ġterms Ġof Ġconvers ational Ġflow . ĠThis Ġframework Ġcaptures Ġtwo Ġmain Ġdebating Ġstrategies âĢĶ prom oting Ġone 's Ġown Ġpoints Ġand Ġattacking Ġthe Ġopponents ' Ġpoints âĢĶ and Ġtracks Ġtheir Ġrelative Ġusage Ġthroughout Ġthe Ġdebate . ĠBy Ġapplying Ġthis Ġmethodology Ġto Ġa Ġsetting Ġwhere Ġdebate Ġwinners Ġare Ġknown , Ġwe Ġshow Ġthat Ġconvers ational Ġflow Ġpatterns Ġare Ġpredictive Ġof Ġwhich Ġdeb ater Ġis Ġmore Ġlikely Ġto Ġpersuade Ġan Ġaudience . </s> Case Ġstudy : ĠOxford - style Ġdebates . ĠOxford - style Ġdebates Ġprovide Ġa Ġsetting Ġthat Ġis Ġparticularly Ġconvenient Ġfor Ġstudying Ġthe Ġeffects Ġof Ġconvers ational Ġflow . ĠIn Ġthis Ġcompetitive Ġdebate Ġformat , Ġtwo Ġteams Ġargue Ġfor Ġor Ġagainst Ġa Ġpreset Ġmotion Ġin Ġorder Ġto Ġpersuade Ġa Ġlive Ġaudience Ġto Ġtake Ġtheir Ġposition . ĠThe Ġaudience Ġvotes Ġbefore Ġand Ġafter Ġthe Ġdebate , Ġand Ġthe Ġwinning Ġteam Ġis Ġthe Ġone Ġthat Ġs ways Ġmore Ġof Ġthe Ġaudience Ġtowards Ġits Ġview . ĠThis Ġsetup Ġallows Ġus Ġto Ġfocus Ġon Ġthe Ġeffects Ġof Ġconvers ational Ġflow Ġsince Ġit Ġdis ent angles Ġthem Ġfrom Ġthe Ġaudience 's Ġprior Ġleaning . </s> The Ġdebate Ġformat Ġinvolves Ġan Ġopening Ġstatement Ġfrom Ġthe Ġtwo Ġsides , Ġwhich Ġpresents Ġan Ġoverview Ġof Ġtheir Ġarguments Ġbefore Ġthe Ġdiscussion Ġbegins . ĠThis Ġallows Ġus Ġto Ġeasily Ġidentify Ġtalking Ġpoints Ġheld Ġby Ġthe Ġparticipants Ġprior Ġto Ġthe Ġinteraction , Ġand Ġconsider Ġthem Ġseparately Ġfrom Ġpoints Ġintroduced Ġspontaneously Ġto Ġserve Ġthe Ġdiscussion . </s> This Ġwork Ġis Ġtaking Ġsteps Ġtowards Ġbetter Ġmodeling Ġof Ġconvers ational Ġdynamics , Ġby : Ġ( i ) Ġintroducing Ġa Ġdebate Ġdataset Ġwith Ġrich Ġmetadata Ġ( Section ĠSEC REF 2 Ġ), Ġ( ii ) Ġproposing Ġa Ġframework Ġfor Ġtracking Ġthe Ġflow Ġof Ġideas Ġ( Section ĠSEC REF 3 Ġ), Ġand Ġ( iii ) Ġshowing Ġits Ġeffectiveness Ġin Ġa Ġpredictive Ġsetting Ġ( Section ĠSEC REF 4 Ġ). </s> Deb ate ĠDat as et : ĠIntelligence ĠSqu ared </s> In Ġthis Ġstudy Ġwe Ġuse Ġtranscripts Ġand Ġresults Ġof ĠOxford - style Ġdebates Ġfrom Ġthe Ġpublic Ġdebate Ġseries ĠâĢ ľ Int elligence ĠSqu ared ĠDeb ates âĢ Ŀ Ġ( IQ 2 Ġfor Ġshort ). ĠThese Ġdebates Ġare Ġrecorded Ġlive , Ġand Ġcontain Ġmotions Ġcovering Ġa Ġdiversity Ġof Ġtopics Ġranging Ġfrom Ġforeign Ġpolicy Ġissues Ġto Ġthe Ġbenefits Ġof Ġorganic Ġfood . ĠEach Ġdebate Ġconsists Ġof Ġtwo Ġopposing Ġteams âĢĶ one Ġfor Ġthe Ġmotion Ġand Ġone Ġagainst âĢĶ of Ġtwo Ġor Ġthree Ġexperts Ġin Ġthe Ġtopic Ġof Ġthe Ġparticular Ġmotion , Ġalong Ġwith Ġa Ġmoderator . ĠEach Ġdebate Ġfollows Ġthe ĠOxford - style Ġformat Ġand Ġconsists Ġof Ġthree Ġrounds . ĠIn Ġthe Ġintroduction , Ġeach Ġdeb ater Ġis Ġgiven Ġ7 Ġminutes Ġto Ġlay Ġout Ġtheir Ġmain Ġpoints . ĠDuring Ġthe Ġdiscussion , Ġdeb aters Ġtake Ġquestions Ġfrom Ġthe Ġmoderator Ġand Ġaudience , Ġand Ġrespond Ġto Ġattacks Ġfrom Ġthe Ġother Ġteam . ĠThis Ġround Ġlasts Ġaround Ġ30 Ġminutes Ġand Ġis Ġhighly Ġinteractive ; Ġteams Ġfrequently Ġengage Ġin Ġdirect Ġconversation Ġwith Ġeach Ġother . ĠFinally , Ġin Ġthe Ġconclusion , Ġeach Ġdeb ater Ġis Ġgiven Ġ2 Ġminutes Ġto Ġmake Ġfinal Ġremarks . </s> Our Ġdataset Ġconsists Ġof Ġthe Ġtranscripts Ġof Ġall Ġdebates Ġheld Ġby ĠIQ 2 Ġin Ġthe ĠUS Ġfrom ĠSeptember Ġ2006 Ġup Ġto ĠSeptember Ġ2015 ; Ġin Ġtotal , Ġthere Ġare Ġ108 Ġdebates . ĠEach Ġdebate Ġis Ġquite Ġextensive : Ġon Ġaverage , Ġ128 01 Ġwords Ġare Ġuttered Ġin Ġ117 Ġturns Ġby Ġmembers Ġof Ġeither Ġside Ġper Ġdebate . </s> Win ning Ġside Ġlabels . ĠWe Ġfollow ĠIQ 2 's Ġcriteria Ġfor Ġdeciding Ġwho Ġwins Ġa Ġdebate , Ġas Ġfollows . ĠBefore Ġthe Ġdebate , Ġthe Ġlive Ġaudience Ġvotes Ġon Ġwhether Ġthey Ġare Ġfor , Ġagainst , Ġor Ġundecided Ġon Ġthe Ġmotion . ĠA Ġsecond Ġround Ġof Ġvoting Ġoccurs Ġafter Ġthe Ġdebate . ĠA Ġside Ġwins Ġthe Ġdebate Ġif Ġthe Ġdifference Ġbetween Ġthe Ġpercentage Ġof Ġvotes Ġthey Ġreceive Ġpost - Ġand Ġpre - deb ate Ġ( the ĠâĢ ľ d elta âĢ Ŀ ) Ġis Ġgreater Ġthan Ġthat Ġof Ġthe Ġother Ġside 's . ĠOften Ġthe Ġdebates Ġare Ġquite Ġtight : Ġfor Ġ30 % Ġof Ġthe Ġdebates , Ġthe Ġdifference Ġbetween Ġthe Ġwinning Ġand Ġlosing Ġsides ' Ġdel t as Ġis Ġless Ġthan Ġ10 %. </s> Aud ience Ġfeedback . ĠWe Ġcheck Ġthat Ġthe Ġvoting Ġresults Ġare Ġmeaningful Ġby Ġverifying Ġthat Ġaudience Ġreactions Ġto Ġthe Ġdeb aters Ġare Ġrelated Ġto Ġdebate Ġoutcome . ĠUsing Ġlaughter Ġand Ġapplause Ġreceived Ġby Ġeach Ġside Ġin Ġeach Ġround Ġas Ġmarkers Ġof Ġpositive Ġreactions , Ġwe Ġnote Ġthat Ġdifferences Ġin Ġaudience Ġreception Ġof Ġthe Ġtwo Ġsides Ġemerge Ġover Ġthe Ġcourse Ġof Ġthe Ġdebate . ĠWhile Ġboth Ġsides Ġget Ġsimilar Ġlevels Ġof Ġreaction Ġduring Ġthe Ġintroduction , Ġwinning Ġteams Ġtend Ġto Ġreceive Ġmore Ġlaughter Ġduring Ġthe Ġdiscussion Ġ( ĠIN LINE FORM 0 Ġ) Ġand Ġmore Ġapplause Ġduring Ġthe Ġconclusion Ġ( ĠIN LINE FORM 2 Ġ). </s> Example Ġdebate . ĠWe Ġwill Ġuse Ġa Ġdebate Ġover Ġthe Ġmotion ĠâĢ ľ Mill enn ials Ġdon 't Ġstand Ġa Ġchance âĢ Ŀ Ġ( hen ce forth ĠMillennials ) Ġas Ġa Ġrunning Ġexample . ĠThe ĠFor Ġside Ġwon Ġthe Ġdebate Ġwith Ġa Ġdelta Ġof Ġ20 % Ġof Ġthe Ġvotes , Ġcompared Ġto Ġthe ĠAgainst Ġside Ġwhich Ġonly Ġgained Ġ5 %. </s> Mod eling ĠIdea ĠFlow </s> Prom oting Ġone 's Ġown Ġpoints Ġand Ġaddressing Ġthe Ġopponent 's Ġpoints Ġare Ġtwo Ġprimary Ġdebating Ġstrategies . ĠHere Ġwe Ġintroduce Ġa Ġmethodology Ġto Ġidentify Ġthese Ġstrategies , Ġand Ġuse Ġit Ġto Ġinvestigate Ġtheir Ġusage Ġand Ġeffect Ġon Ġa Ġdebate 's Ġoutcome . </s> Ident ifying Ġtalking Ġpoints Ġ. ĠWe Ġfirst Ġfocus Ġon Ġideas Ġwhich Ġform Ġthe Ġbasis Ġof Ġa Ġside 's Ġstance Ġon Ġthe Ġmotion . ĠWe Ġidentify Ġsuch Ġtalking Ġpoints Ġby Ġconsidering Ġwords Ġwhose Ġfrequency Ġof Ġusage Ġdiffers Ġsignificantly Ġbetween Ġthe Ġtwo Ġteams Ġduring Ġthe Ġintroduction , Ġbefore Ġany Ġinteraction Ġtakes Ġplace . ĠTo Ġfind Ġthese Ġwords , Ġwe Ġuse Ġthe Ġmethod Ġintroduced Ġby Ġmon roe 2008 fight in Ġin Ġthe Ġcontext Ġof ĠU . S . ĠSenate Ġspeeches . ĠIn Ġparticular , Ġwe Ġestimate Ġthe Ġdivergence Ġbetween Ġthe Ġtwo Ġsides ' Ġword - usage Ġin Ġthe Ġintroduction , Ġwhere Ġword - usage Ġis Ġmodeled Ġas Ġmult in omial Ġdistributions Ġsmoot hed Ġwith Ġa Ġuniform ĠDir ich let Ġprior , Ġand Ġdivergence Ġis Ġgiven Ġby Ġlog - odd s Ġratio . ĠThe Ġmost Ġdiscriminating Ġwords Ġare Ġthose Ġwith Ġthe Ġhighest Ġand Ġlowest Ġz - sc ores Ġof Ġdivergence Ġestimates . ĠFor Ġa Ġside ĠIN LINE FORM 0 Ġ, Ġwe Ġdefine Ġthe Ġset Ġof Ġtalking Ġpoints ĠIN LINE FORM 1 Ġto Ġbe Ġthe ĠIN LINE FORM 2 Ġwords Ġwith Ġthe Ġhighest Ġor Ġlowest ĠIN LINE FORM 3 Ġ- sc ores . ĠWe Ġdistinguish Ġbetween ĠIN LINE FORM 5 Ġ' s Ġown Ġtalking Ġpoints ĠIN LINE FORM 6 Ġ, Ġand Ġthe Ġopposing Ġtalking Ġpoints ĠIN LINE FORM 7 Ġbelonging Ġto Ġits Ġopponent ĠIN LINE FORM 8 Ġ. ĠThese Ġare Ġexamples Ġof Ġtalking Ġpoints Ġfor Ġthe ĠâĢ ľ Mill enn ials âĢ Ŀ Ġdebate : </s> The Ġflow Ġof Ġtalking Ġpoints Ġ. ĠA Ġside Ġcan Ġeither Ġpromote Ġits Ġown Ġtalking Ġpoints Ġ, Ġaddress Ġits Ġopponent 's Ġpoints , Ġor Ġsteer Ġaway Ġfrom Ġthese Ġinitially Ġsalient Ġideas Ġaltogether . ĠWe Ġquantify Ġthe Ġuse Ġof Ġthese Ġstrategies Ġby Ġcomparing Ġthe Ġair time Ġdeb aters Ġdevote Ġto Ġtalking Ġpoints Ġ. ĠFor Ġa Ġside ĠIN LINE FORM 0 Ġ, Ġlet Ġthe Ġself - co verage ĠIN LINE FORM 1 Ġbe Ġthe Ġfraction Ġof Ġcontent Ġwords Ġuttered Ġby ĠIN LINE FORM 2 Ġin Ġround ĠIN LINE FORM 3 Ġthat Ġare Ġamong Ġtheir Ġown Ġtalking Ġpoints ĠIN LINE FORM 4 Ġ; Ġand Ġthe Ġopponent - co verage ĠIN LINE FORM 5 Ġbe Ġthe Ġfraction Ġof Ġits Ġcontent Ġwords Ġcovering Ġopposing Ġtalking Ġpoints ĠIN LINE FORM 6 Ġ. </s> Not Ġsurprisingly , Ġwe Ġfind Ġthat Ġself - co verage Ġdominates Ġduring Ġthe Ġdiscussion Ġ( ĠIN LINE FORM 0 Ġ, ĠIN LINE FORM 1 Ġ). ĠHowever , Ġthis Ġdoes Ġnot Ġmean Ġdeb aters Ġare Ġsimply Ġgiving Ġmon olog ues Ġand Ġignoring Ġeach Ġother : Ġthe Ġeffect Ġof Ġthe Ġinteraction Ġis Ġreflected Ġin Ġa Ġsharp Ġdrop Ġin Ġself - co verage Ġand Ġa Ġrise Ġin Ġopponent - co verage Ġonce Ġthe Ġdiscussion Ġround Ġbegins . ĠRespect ively , ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġ, Ġboth ĠIN LINE FORM 4 Ġ. ĠExamples Ġof Ġself - Ġand Ġopponent - co verage Ġof Ġtwo Ġtalking Ġpoint Ġs Ġin Ġthe ĠâĢ ľ Mill enn ials âĢ Ŀ Ġdebate Ġfrom Ġthe Ġintroduction Ġand Ġdiscussion Ġare Ġgiven Ġin ĠTable ĠT AB REF 9 Ġ. </s> Does Ġthe Ġchange Ġin Ġfocus Ġtranslate Ġto Ġany Ġstrategic Ġadvantages ? ĠFigure ĠFIG REF 11 Ġsuggests Ġthis Ġis Ġthe Ġcase : Ġthe Ġdrop Ġin Ġself - co verage Ġis Ġslightly Ġlarger Ġfor Ġthe Ġside Ġthat Ġeventually Ġwins Ġthe Ġdebate Ġ( ĠIN LINE FORM 0 Ġ). ĠThe Ġdrop Ġin Ġthe Ġsum Ġof Ġself - Ġand Ġopponent - co verage Ġis Ġalso Ġlarger Ġfor Ġwinning Ġteams , Ġsuggesting Ġthat Ġthey Ġare Ġmore Ġlikely Ġto Ġsteer Ġaway Ġfrom Ġdiscussing Ġany Ġtalking Ġpoints Ġfrom Ġeither Ġside Ġ( ĠIN LINE FORM 1 Ġ). </s> Ident ifying Ġdiscussion Ġpoints . ĠHaving Ġseen Ġthat Ġdeb aters Ġcan Ġbenefit Ġby Ġshifting Ġaway Ġfrom Ġtalking Ġpoints Ġthat Ġwere Ġsalient Ġduring Ġthe Ġintroduction , Ġwe Ġnow Ġexamine Ġthe Ġideas Ġthat Ġspontaneously Ġarise Ġto Ġserve Ġthe Ġdiscussion . ĠWe Ġmodel Ġsuch Ġdiscussion Ġpoints Ġas Ġwords Ġintroduced Ġto Ġthe Ġdebate Ġduring Ġthe Ġdiscussion Ġby Ġa Ġdeb ater Ġand Ġadopted Ġby Ġhis Ġopponents Ġat Ġleast Ġtwice . ĠThis Ġallows Ġus Ġto Ġfocus Ġon Ġwords Ġthat Ġbecome Ġrelevant Ġto Ġthe Ġconversation ; Ġonly Ġ3 % Ġof Ġall Ġnewly Ġintroduced Ġwords Ġqualify , Ġamount ing Ġto Ġabout Ġ10 Ġdiscussion Ġpoints Ġper Ġdebate . </s> The Ġflow Ġof Ġdiscussion Ġpoints Ġ. ĠThe Ġadoption Ġof Ġdiscussion Ġpoints Ġplays Ġan Ġimportant Ġrole Ġin Ġpersu ading Ġthe Ġaudience : Ġduring Ġthe Ġdiscussion , Ġeventual Ġwinners Ġadopt Ġmore Ġdiscussion Ġpoints Ġintroduced Ġby Ġtheir Ġopponents Ġthan Ġeventual Ġlosers Ġ( ĠIN LINE FORM 0 Ġ). ĠTwo Ġpossible Ġstrategic Ġinterpretations Ġemerge . ĠFrom Ġa Ġtopic Ġcontrol Ġangle ĠB IB REF 0 Ġ, Ġperhaps Ġlosers Ġare Ġmore Ġsuccessful Ġat Ġimposing Ġtheir Ġdiscussion Ġpoints Ġto Ġgain Ġcontrol Ġof Ġthe Ġdiscussion . ĠThis Ġview Ġappears Ġcounter intuitive Ġgiven Ġwork Ġlinking Ġtopic Ġcontrol Ġto Ġinfluence Ġin Ġother Ġsettings ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ. </s> An Ġalternative Ġinterpretation Ġcould Ġbe Ġthat Ġwinners Ġare Ġmore Ġactive Ġthan Ġlosers Ġin Ġcontest ing Ġtheir Ġopponents ' Ġpoints , Ġa Ġstrategy Ġthat Ġmight Ġplay Ġout Ġfavorably Ġto Ġthe Ġaudience . ĠA Ġpost - h oc Ġmanual Ġexamination Ġsupports Ġthis Ġinterpretation : Ġ78 % Ġof Ġthe Ġvalid Ġdiscussion Ġpoints Ġare Ġpicked Ġup Ġby Ġthe Ġopposing Ġside Ġin Ġorder Ġto Ġbe Ġchallenged ; Ġthis Ġstrategy Ġis Ġexempl ified Ġin ĠTable ĠT AB REF 14 Ġ. ĠOverall , Ġthese Ġobservations Ġtying Ġthe Ġflow Ġof Ġdiscussion Ġpoints Ġto Ġthe Ġdebate 's Ġoutcome Ġsuggest Ġthat Ġwinners Ġare Ġmore Ġsuccessful Ġat Ġusing Ġthe Ġinteraction Ġto Ġengage Ġwith Ġtheir Ġopponents ' Ġideas . </s> P redict ive ĠPower </s> We Ġevaluate Ġthe Ġpredictive Ġpower Ġof Ġour Ġflow Ġfeatures Ġin Ġa Ġbinary Ġclassification Ġsetting : Ġpredict Ġwhether Ġthe ĠFor Ġor ĠAgainst Ġside Ġwins Ġthe Ġdebate . ĠThis Ġis Ġa Ġchallenging Ġtask Ġeven Ġfor Ġhumans , Ġthus Ġthe Ġdramatic Ġreveal Ġat Ġthe Ġend Ġof Ġeach ĠIQ 2 Ġdebate Ġthat Ġpartly Ġexplains Ġthe Ġpopularity Ġof Ġthe Ġshow . ĠOur Ġgoal Ġhere Ġis Ġlimited Ġto Ġunderstanding Ġwhich Ġof Ġthe Ġflow Ġfeatures Ġthat Ġwe Ġdeveloped Ġcarry Ġpredictive Ġpower . </s> Con vers ation Ġflow Ġfeatures . ĠWe Ġuse Ġall Ġconvers ational Ġfeatures Ġdiscussed Ġabove . ĠFor Ġeach Ġside ĠIN LINE FORM 0 Ġwe Ġinclude ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġ, Ġand Ġtheir Ġsum . ĠWe Ġalso Ġuse Ġthe Ġdrop Ġin Ġself - co verage Ġgiven Ġby Ġsubtract ing Ġcorresponding Ġvalues Ġfor ĠIN LINE FORM 3 Ġ, Ġand Ġthe Ġnumber Ġof Ġdiscussion Ġpoints Ġadopted Ġby Ġeach Ġside . ĠWe Ġcall Ġthese Ġthe ĠFlow Ġfeatures . </s> Bas eline Ġfeatures . ĠTo Ġdiscard Ġthe Ġpossibility Ġthat Ġour Ġresults Ġare Ġsimply Ġexplained Ġby Ġdeb ater Ġverb osity , Ġwe Ġuse Ġthe Ġnumber Ġof Ġwords Ġuttered Ġand Ġnumber Ġof Ġturns Ġtaken Ġby Ġeach Ġside Ġ( length ) Ġas Ġbas elines . ĠWe Ġalso Ġcompare Ġto Ġa Ġun ig ram Ġbaseline Ġ( B OW ). </s> Aud ience Ġfeatures . ĠWe Ġuse Ġthe Ġcounts Ġof Ġapplause Ġand Ġlaughter Ġreceived Ġby Ġeach Ġside Ġ( described Ġin ĠSection ĠSEC REF 2 Ġ) Ġas Ġrough Ġindicators Ġof Ġhow Ġwell Ġthe Ġaudience Ġcan Ġforesee Ġa Ġdebate 's Ġoutcome . </s> Pred iction Ġaccuracy Ġis Ġevaluated Ġusing Ġa Ġleave - one - out Ġ( L OO ) Ġapproach . ĠWe Ġuse Ġlog istic Ġregression ; Ġmodel Ġparameters Ġfor Ġeach ĠL OO Ġtrain - test Ġsplit Ġare Ġselected Ġvia Ġ3 - fold Ġcross - valid ation Ġon Ġthe Ġtraining Ġset . ĠTo Ġfind Ġparticularly Ġpredictive Ġflow Ġfeatures , Ġwe Ġalso Ġtry Ġusing Ġun ivariate Ġfeature Ġselection Ġon Ġthe Ġflow Ġfeatures Ġbefore Ġthe Ġmodel Ġis Ġfitted Ġin Ġeach Ġsplit ; Ġwe Ġrefer Ġto Ġthis Ġsetting Ġas ĠFlow *. </s> We Ġfind Ġthat Ġconversation Ġflow Ġfeatures Ġobtain Ġthe Ġbest Ġaccuracy Ġamong Ġall Ġlisted Ġfeature Ġtypes Ġ( Flow : Ġ63 %; ĠFlow *: Ġ65 %), Ġperforming Ġsignificantly Ġhigher Ġthan Ġa Ġ50 % Ġrandom Ġbaseline Ġ( bin omial Ġtest ĠIN LINE FORM 0 Ġ), Ġand Ġcomparable Ġto Ġaudience Ġfeatures Ġ( 60 %). ĠIn Ġcontrast , Ġthe Ġlength Ġand ĠB OW Ġbas elines Ġdo Ġnot Ġperform Ġbetter Ġthan Ġchance . ĠWe Ġnote Ġthat ĠFlow Ġfeatures Ġperform Ġcompetitive ly Ġdespite Ġbeing Ġthe Ġonly Ġones Ġthat Ġdo Ġnot Ġfactor Ġin Ġthe Ġconcluding Ġround . </s> The Ġfeatures Ġselected Ġmost Ġoften Ġin Ġthe ĠFlow * Ġtask Ġare : Ġthe Ġnumber Ġof Ġdiscussion Ġpoints Ġadopted Ġ( with Ġpositive Ġregression Ġcoefficients ), Ġthe Ġrecall Ġof Ġtalking Ġpoints Ġduring Ġthe Ġdiscussion Ġround Ġ( negative Ġcoefficients ), Ġand Ġthe Ġdrop Ġin Ġusage Ġof Ġown Ġtalking Ġpoints Ġfrom Ġintroduction Ġto Ġdiscussion Ġ( positive Ġcoefficients ). ĠThe Ġrelative Ġimportance Ġof Ġthese Ġfeatures , Ġwhich Ġfocus Ġon Ġthe Ġinteraction Ġbetween Ġteams , Ġsuggests Ġthat Ġaudiences Ġtend Ġto Ġfavor Ġdebating Ġstrategies Ġwhich Ġemphasize Ġthe Ġdiscussion . </s> Further ĠRelated ĠWork </s> Previous Ġwork Ġon Ġconvers ational Ġstructure Ġhas Ġproposed Ġapproaches Ġto Ġmodel Ġdialogue Ġacts ĠB IB REF 3 Ġ, ĠB IB REF 4 Ġ, ĠB IB REF 5 Ġor Ġdis ent angle Ġinter le aved Ġconversations ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ. ĠOther Ġresearch Ġhas Ġconsidered Ġthe Ġproblem Ġof Ġdetecting Ġconversation - level Ġtraits Ġsuch Ġas Ġthe Ġpresence Ġof Ġdisagreements ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġor Ġthe Ġlikelihood Ġof Ġrelation Ġdissolution ĠB IB REF 10 Ġ. ĠAt Ġthe Ġparticipant Ġlevel , Ġseveral Ġstudies Ġpresent Ġapproaches Ġto Ġidentify Ġideological Ġstances ĠB IB REF 11 Ġ, ĠB IB REF 12 Ġ, Ġusing Ġfeatures Ġbased Ġon Ġparticipant Ġinteractions ĠB IB REF 13 Ġ, ĠB IB REF 14 Ġ, Ġor Ġextracting Ġwords Ġand Ġreasons Ġcharacter izing Ġa Ġstance ĠB IB REF 15 Ġ, ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ. ĠIn Ġour Ġsetting , Ġboth Ġthe Ġstances Ġand Ġthe Ġturn Ġstructure Ġof Ġa Ġdebate Ġare Ġknown , Ġallowing Ġus Ġto Ġinstead Ġfocus Ġon Ġthe Ġdebate 's Ġoutcome . </s> Ex isting Ġresearch Ġon Ġargument ation Ġstrategies Ġhas Ġlargely Ġfocused Ġon Ġexploiting Ġthe Ġstructure Ġof Ġmon ologic Ġarguments ĠB IB REF 18 Ġ, Ġlike Ġthose Ġof Ġpersuasive Ġessays ĠB IB REF 19 Ġ, ĠB IB REF 20 Ġ. ĠIn Ġaddition , Ġtan + et al : 16 a Ġhas Ġexamined Ġthe Ġeffectiveness Ġof Ġarguments Ġin Ġthe Ġcontext Ġof Ġa Ġforum Ġwhere Ġpeople Ġinvite Ġothers Ġto Ġchallenge Ġtheir Ġopinions . ĠWe Ġcomplement Ġthis Ġline Ġof Ġwork Ġby Ġlooking Ġat Ġthe Ġrelative Ġpersu as iveness Ġof Ġparticipants Ġin Ġextended Ġconversations Ġas Ġthey Ġexchange Ġarguments Ġover Ġmultiple Ġturns . </s> Previous Ġstudies Ġof Ġinfluence Ġin Ġextended Ġconversations Ġhave Ġlargely Ġdealt Ġwith Ġthe Ġpolitical Ġdomain , Ġexamining Ġmoder ated Ġbut Ġrelatively Ġun struct ured Ġsettings Ġsuch Ġas Ġtalk Ġshows Ġor Ġpresidential Ġdebates , Ġand Ġsuggesting Ġfeatures Ġlike Ġtopic Ġcontrol ĠB IB REF 0 Ġ, Ġlinguistic Ġstyle Ġmatching ĠB IB REF 21 Ġand Ġturn - taking ĠB IB REF 22 Ġ. ĠWith Ġpersuasion Ġin Ġmind , Ġour Ġwork Ġextends Ġthese Ġstudies Ġto Ġexplore Ġa Ġnew Ġdynamic , Ġthe Ġflow Ġof Ġideas Ġbetween Ġspeakers , Ġin Ġa Ġhighly Ġstructured Ġsetting Ġthat Ġcontrols Ġfor Ġconfounding Ġfactors . </s> Lim itations Ġand ĠFuture ĠWork </s> This Ġstudy Ġopens Ġseveral Ġavenues Ġfor Ġfuture Ġresearch . ĠOne Ġcould Ġexplore Ġmore Ġcomplex Ġrepresentations Ġof Ġtalking Ġpoints Ġand Ġdiscussion Ġpoints Ġ, Ġfor Ġinstance Ġusing Ġtopic Ġmodels Ġor Ġword Ġembed d ings . ĠFurthermore , Ġaugment ing Ġthe Ġflow Ġof Ġcontent Ġin Ġa Ġconversation Ġwith Ġthe Ġspeakers ' Ġlinguistic Ġchoices Ġcould Ġbetter Ġcapture Ġtheir Ġintentions . ĠIn Ġaddition , Ġit Ġwould Ġbe Ġinteresting Ġto Ġstudy Ġthe Ġinter play Ġbetween Ġour Ġconvers ational Ġflow Ġfeatures Ġand Ġrelatively Ġmon ologic Ġfeatures Ġthat Ġconsider Ġthe Ġargument ative Ġand Ġrhetorical Ġtraits Ġof Ġeach Ġside Ġseparately . ĠMore Ġexplicitly Ġcomparing Ġand Ġcontrasting Ġmon ologic Ġand Ġinteractive Ġdynamics Ġcould Ġlead Ġto Ġbetter Ġmodels Ġof Ġconversations . ĠSuch Ġapproaches Ġcould Ġalso Ġhelp Ġclarify Ġsome Ġof Ġthe Ġintu itions Ġabout Ġconversations Ġexplored Ġin Ġthis Ġwork , Ġparticularly Ġthat Ġengaging Ġin Ġdialogue Ġcarries Ġdifferent Ġstrategic Ġimplications Ġfrom Ġself - prom otion . </s> Our Ġfocus Ġin Ġthis Ġpaper Ġis Ġon Ġcapturing Ġand Ġunderstanding Ġconvers ational Ġflow . ĠWe Ġhence Ġmake Ġsome Ġsimpl ifying Ġassumptions Ġthat Ġcould Ġbe Ġrefined Ġin Ġfuture Ġwork . ĠFor Ġinstance , Ġby Ġusing Ġa Ġbasic Ġun ig ram - based Ġdefinition Ġof Ġdiscussion Ġpoints Ġ, Ġwe Ġdo Ġnot Ġaccount Ġfor Ġthe Ġcontext Ġor Ġsemantic Ġsense Ġin Ġwhich Ġthese Ġpoints Ġoccur . ĠIn Ġparticular , Ġour Ġannot ators Ġfound Ġthat Ġa Ġsignificant Ġproportion Ġof Ġthe Ġdiscussion Ġpoints Ġunder Ġour Ġdefinition Ġactually Ġreferred Ġto Ġdiffering Ġideas Ġin Ġthe Ġvarious Ġcontexts Ġin Ġwhich Ġthey Ġappeared . ĠWe Ġexpect Ġthat Ġimproving Ġour Ġretrieval Ġmodel Ġwill Ġalso Ġimprove Ġthe Ġrobust ness Ġof Ġour Ġidea Ġflow Ġanalysis . ĠA Ġbetter Ġmodel Ġof Ġdiscussion Ġpoints Ġcould Ġalso Ġprovide Ġmore Ġinsight Ġinto Ġthe Ġrole Ġof Ġthese Ġpoints Ġin Ġpersu ading Ġthe Ġaudience . </s> While ĠOxford - style Ġdebates Ġare Ġa Ġparticularly Ġconvenient Ġsetting Ġfor Ġstudying Ġthe Ġeffects Ġof Ġconvers ational Ġflow , Ġour Ġdataset Ġis Ġlimited Ġin Ġterms Ġof Ġsize . ĠIt Ġwould Ġbe Ġworthwhile Ġto Ġexamine Ġthe Ġflow Ġfeatures Ġwe Ġdeveloped Ġin Ġthe Ġcontext Ġof Ġsettings Ġwith Ġricher Ġincentives Ġbeyond Ġpersu ading Ġan Ġaudience , Ġsuch Ġas Ġin Ġthe Ġsemi - co operative Ġenvironment Ġof ĠWikipedia Ġtalk Ġpages . ĠFinally , Ġour Ġmethodology Ġcould Ġpoint Ġto Ġapplications Ġin Ġareas Ġsuch Ġas Ġeducation Ġand Ġcooperative Ġwork , Ġwhere Ġit Ġis Ġkey Ġto Ġestablish Ġthe Ġlink Ġbetween Ġconversation Ġfeatures Ġand Ġan Ġinter loc utor 's Ġability Ġto Ġconvey Ġtheir Ġpoint ĠB IB REF 23 Ġ. </s> Acknowled gements . ĠWe Ġthank Ġthe Ġreviewers Ġand ĠV . ĠNic ul ae Ġfor Ġtheir Ġhelpful Ġcomments , Ġand ĠI . ĠA raw jo Ġand ĠD . ĠSed ra Ġfor Ġannotations . ĠThis Ġwork Ġwas Ġsupported Ġin Ġpart Ġby Ġa ĠGoogle ĠFaculty ĠResearch ĠAward .
Introduction Public debates are a common platform for presenting and juxtaposing diverging viewpoints As opposed to monologues where speakers are limited to expressing their own beliefs, debates allow for participants to interactively attack their opponents' points while defending their own. The resulting flow of ideas is a key feature of this conversation genre. In this work we introduce a computational framework for characterizing debates in terms of conversational flow. This framework captures two main debating strategies—promoting one's own points and attacking the opponents' points—and tracks their relative usage throughout the debate. By applying this methodology to a setting where debate winners are known, we show that conversational flow patterns are predictive of which debater is more likely to persuade an audience. Case study: Oxford-style debates. Oxford-style debates provide a setting that is particularly convenient for studying the effects of conversational flow. In this competitive debate format, two teams argue for or against a preset motion in order to persuade a live audience to take their position. The audience votes before and after the debate, and the winning team is the one that sways more of the audience towards its view. This setup allows us to focus on the effects of conversational flow since it disentangles them from the audience's prior leaning. The debate format involves an opening statement from the two sides, which presents an overview of their arguments before the discussion begins. This allows us to easily identify talking points held by the participants prior to the interaction, and consider them separately from points introduced spontaneously to serve the discussion. This work is taking steps towards better modeling of conversational dynamics, by: (i) introducing a debate dataset with rich metadata (Section SECREF2 ), (ii) proposing a framework for tracking the flow of ideas (Section SECREF3 ), and (iii) showing its effectiveness in a predictive setting (Section SECREF4 ). Debate Dataset: Intelligence Squared In this study we use transcripts and results of Oxford-style debates from the public debate series “Intelligence Squared Debates” (IQ2 for short). These debates are recorded live, and contain motions covering a diversity of topics ranging from foreign policy issues to the benefits of organic food. Each debate consists of two opposing teams—one for the motion and one against—of two or three experts in the topic of the particular motion, along with a moderator. Each debate follows the Oxford-style format and consists of three rounds. In the introduction, each debater is given 7 minutes to lay out their main points. During the discussion, debaters take questions from the moderator and audience, and respond to attacks from the other team. This round lasts around 30 minutes and is highly interactive; teams frequently engage in direct conversation with each other. Finally, in the conclusion, each debater is given 2 minutes to make final remarks. Our dataset consists of the transcripts of all debates held by IQ2 in the US from September 2006 up to September 2015; in total, there are 108 debates. Each debate is quite extensive: on average, 12801 words are uttered in 117 turns by members of either side per debate. Winning side labels. We follow IQ2's criteria for deciding who wins a debate, as follows. Before the debate, the live audience votes on whether they are for, against, or undecided on the motion. A second round of voting occurs after the debate. A side wins the debate if the difference between the percentage of votes they receive post- and pre-debate (the “delta”) is greater than that of the other side's. Often the debates are quite tight: for 30% of the debates, the difference between the winning and losing sides' deltas is less than 10%. Audience feedback. We check that the voting results are meaningful by verifying that audience reactions to the debaters are related to debate outcome. Using laughter and applause received by each side in each round as markers of positive reactions, we note that differences in audience reception of the two sides emerge over the course of the debate. While both sides get similar levels of reaction during the introduction, winning teams tend to receive more laughter during the discussion ( INLINEFORM0 ) and more applause during the conclusion ( INLINEFORM2 ). Example debate. We will use a debate over the motion “Millennials don't stand a chance” (henceforth Millennials) as a running example. The For side won the debate with a delta of 20% of the votes, compared to the Against side which only gained 5%. Modeling Idea Flow Promoting one's own points and addressing the opponent's points are two primary debating strategies. Here we introduce a methodology to identify these strategies, and use it to investigate their usage and effect on a debate's outcome. Identifying talking points . We first focus on ideas which form the basis of a side's stance on the motion. We identify such talking points by considering words whose frequency of usage differs significantly between the two teams during the introduction, before any interaction takes place. To find these words, we use the method introduced by monroe2008fightin in the context of U.S. Senate speeches. In particular, we estimate the divergence between the two sides' word-usage in the introduction, where word-usage is modeled as multinomial distributions smoothed with a uniform Dirichlet prior, and divergence is given by log-odds ratio. The most discriminating words are those with the highest and lowest z-scores of divergence estimates. For a side INLINEFORM0 , we define the set of talking points INLINEFORM1 to be the INLINEFORM2 words with the highest or lowest INLINEFORM3 -scores. We distinguish between INLINEFORM5 's own talking points INLINEFORM6 , and the opposing talking points INLINEFORM7 belonging to its opponent INLINEFORM8 . These are examples of talking points for the “Millennials” debate: The flow of talking points . A side can either promote its own talking points , address its opponent's points, or steer away from these initially salient ideas altogether. We quantify the use of these strategies by comparing the airtime debaters devote to talking points . For a side INLINEFORM0 , let the self-coverage INLINEFORM1 be the fraction of content words uttered by INLINEFORM2 in round INLINEFORM3 that are among their own talking points INLINEFORM4 ; and the opponent-coverage INLINEFORM5 be the fraction of its content words covering opposing talking points INLINEFORM6 . Not surprisingly, we find that self-coverage dominates during the discussion ( INLINEFORM0 , INLINEFORM1 ). However, this does not mean debaters are simply giving monologues and ignoring each other: the effect of the interaction is reflected in a sharp drop in self-coverage and a rise in opponent-coverage once the discussion round begins. Respectively, INLINEFORM2 and INLINEFORM3 , both INLINEFORM4 . Examples of self- and opponent-coverage of two talking point s in the “Millennials” debate from the introduction and discussion are given in Table TABREF9 . Does the change in focus translate to any strategic advantages? Figure FIGREF11 suggests this is the case: the drop in self-coverage is slightly larger for the side that eventually wins the debate ( INLINEFORM0 ). The drop in the sum of self- and opponent-coverage is also larger for winning teams, suggesting that they are more likely to steer away from discussing any talking points from either side ( INLINEFORM1 ). Identifying discussion points. Having seen that debaters can benefit by shifting away from talking points that were salient during the introduction, we now examine the ideas that spontaneously arise to serve the discussion. We model such discussion points as words introduced to the debate during the discussion by a debater and adopted by his opponents at least twice. This allows us to focus on words that become relevant to the conversation; only 3% of all newly introduced words qualify, amounting to about 10 discussion points per debate. The flow of discussion points . The adoption of discussion points plays an important role in persuading the audience: during the discussion, eventual winners adopt more discussion points introduced by their opponents than eventual losers ( INLINEFORM0 ). Two possible strategic interpretations emerge. From a topic control angle BIBREF0 , perhaps losers are more successful at imposing their discussion points to gain control of the discussion. This view appears counterintuitive given work linking topic control to influence in other settings BIBREF1 , BIBREF2 . An alternative interpretation could be that winners are more active than losers in contesting their opponents' points, a strategy that might play out favorably to the audience. A post-hoc manual examination supports this interpretation: 78% of the valid discussion points are picked up by the opposing side in order to be challenged; this strategy is exemplified in Table TABREF14 . Overall, these observations tying the flow of discussion points to the debate's outcome suggest that winners are more successful at using the interaction to engage with their opponents' ideas. Predictive Power We evaluate the predictive power of our flow features in a binary classification setting: predict whether the For or Against side wins the debate. This is a challenging task even for humans, thus the dramatic reveal at the end of each IQ2 debate that partly explains the popularity of the show. Our goal here is limited to understanding which of the flow features that we developed carry predictive power. Conversation flow features. We use all conversational features discussed above. For each side INLINEFORM0 we include INLINEFORM1 , INLINEFORM2 , and their sum. We also use the drop in self-coverage given by subtracting corresponding values for INLINEFORM3 , and the number of discussion points adopted by each side. We call these the Flow features. Baseline features. To discard the possibility that our results are simply explained by debater verbosity, we use the number of words uttered and number of turns taken by each side (length) as baselines. We also compare to a unigram baseline (BOW). Audience features. We use the counts of applause and laughter received by each side (described in Section SECREF2 ) as rough indicators of how well the audience can foresee a debate's outcome. Prediction accuracy is evaluated using a leave-one-out (LOO) approach. We use logistic regression; model parameters for each LOO train-test split are selected via 3-fold cross-validation on the training set. To find particularly predictive flow features, we also try using univariate feature selection on the flow features before the model is fitted in each split; we refer to this setting as Flow*. We find that conversation flow features obtain the best accuracy among all listed feature types (Flow: 63%; Flow*: 65%), performing significantly higher than a 50% random baseline (binomial test INLINEFORM0 ), and comparable to audience features (60%). In contrast, the length and BOW baselines do not perform better than chance. We note that Flow features perform competitively despite being the only ones that do not factor in the concluding round. The features selected most often in the Flow* task are: the number of discussion points adopted (with positive regression coefficients), the recall of talking points during the discussion round (negative coefficients), and the drop in usage of own talking points from introduction to discussion (positive coefficients). The relative importance of these features, which focus on the interaction between teams, suggests that audiences tend to favor debating strategies which emphasize the discussion. Further Related Work Previous work on conversational structure has proposed approaches to model dialogue acts BIBREF3 , BIBREF4 , BIBREF5 or disentangle interleaved conversations BIBREF6 , BIBREF7 . Other research has considered the problem of detecting conversation-level traits such as the presence of disagreements BIBREF8 , BIBREF9 or the likelihood of relation dissolution BIBREF10 . At the participant level, several studies present approaches to identify ideological stances BIBREF11 , BIBREF12 , using features based on participant interactions BIBREF13 , BIBREF14 , or extracting words and reasons characterizing a stance BIBREF15 , BIBREF16 , BIBREF17 . In our setting, both the stances and the turn structure of a debate are known, allowing us to instead focus on the debate's outcome. Existing research on argumentation strategies has largely focused on exploiting the structure of monologic arguments BIBREF18 , like those of persuasive essays BIBREF19 , BIBREF20 . In addition, tan+etal:16a has examined the effectiveness of arguments in the context of a forum where people invite others to challenge their opinions. We complement this line of work by looking at the relative persuasiveness of participants in extended conversations as they exchange arguments over multiple turns. Previous studies of influence in extended conversations have largely dealt with the political domain, examining moderated but relatively unstructured settings such as talk shows or presidential debates, and suggesting features like topic control BIBREF0 , linguistic style matching BIBREF21 and turn-taking BIBREF22 . With persuasion in mind, our work extends these studies to explore a new dynamic, the flow of ideas between speakers, in a highly structured setting that controls for confounding factors. Limitations and Future Work This study opens several avenues for future research. One could explore more complex representations of talking points and discussion points , for instance using topic models or word embeddings. Furthermore, augmenting the flow of content in a conversation with the speakers' linguistic choices could better capture their intentions. In addition, it would be interesting to study the interplay between our conversational flow features and relatively monologic features that consider the argumentative and rhetorical traits of each side separately. More explicitly comparing and contrasting monologic and interactive dynamics could lead to better models of conversations. Such approaches could also help clarify some of the intuitions about conversations explored in this work, particularly that engaging in dialogue carries different strategic implications from self-promotion. Our focus in this paper is on capturing and understanding conversational flow. We hence make some simplifying assumptions that could be refined in future work. For instance, by using a basic unigram-based definition of discussion points , we do not account for the context or semantic sense in which these points occur. In particular, our annotators found that a significant proportion of the discussion points under our definition actually referred to differing ideas in the various contexts in which they appeared. We expect that improving our retrieval model will also improve the robustness of our idea flow analysis. A better model of discussion points could also provide more insight into the role of these points in persuading the audience. While Oxford-style debates are a particularly convenient setting for studying the effects of conversational flow, our dataset is limited in terms of size. It would be worthwhile to examine the flow features we developed in the context of settings with richer incentives beyond persuading an audience, such as in the semi-cooperative environment of Wikipedia talk pages. Finally, our methodology could point to applications in areas such as education and cooperative work, where it is key to establish the link between conversation features and an interlocutor's ability to convey their point BIBREF23 . Acknowledgements. We thank the reviewers and V. Niculae for their helpful comments, and I. Arawjo and D. Sedra for annotations. This work was supported in part by a Google Faculty Research Award.
what debates dataset was used?
ababb79dd3c301f4541beafa181f6a6726839a10
what Ġdebates Ġdataset Ġwas Ġused ?
Int elligence ĠSqu ared ĠDeb ates
<s> what Ġdebates Ġdataset Ġwas Ġused ? </s> Introduction </s> Public Ġdebates Ġare Ġa Ġcommon Ġplatform Ġfor Ġpresenting Ġand Ġjuxtap osing Ġdiver ging Ġviewpoints ĠAs Ġopposed Ġto Ġmon olog ues Ġwhere Ġspeakers Ġare Ġlimited Ġto Ġexpressing Ġtheir Ġown Ġbeliefs , Ġdebates Ġallow Ġfor Ġparticipants Ġto Ġinteract ively Ġattack Ġtheir Ġopponents ' Ġpoints Ġwhile Ġdefending Ġtheir Ġown . ĠThe Ġresulting Ġflow Ġof Ġideas Ġis Ġa Ġkey Ġfeature Ġof Ġthis Ġconversation Ġgenre . </s> In Ġthis Ġwork Ġwe Ġintroduce Ġa Ġcomputational Ġframework Ġfor Ġcharacter izing Ġdebates Ġin Ġterms Ġof Ġconvers ational Ġflow . ĠThis Ġframework Ġcaptures Ġtwo Ġmain Ġdebating Ġstrategies âĢĶ prom oting Ġone 's Ġown Ġpoints Ġand Ġattacking Ġthe Ġopponents ' Ġpoints âĢĶ and Ġtracks Ġtheir Ġrelative Ġusage Ġthroughout Ġthe Ġdebate . ĠBy Ġapplying Ġthis Ġmethodology Ġto Ġa Ġsetting Ġwhere Ġdebate Ġwinners Ġare Ġknown , Ġwe Ġshow Ġthat Ġconvers ational Ġflow Ġpatterns Ġare Ġpredictive Ġof Ġwhich Ġdeb ater Ġis Ġmore Ġlikely Ġto Ġpersuade Ġan Ġaudience . </s> Case Ġstudy : ĠOxford - style Ġdebates . ĠOxford - style Ġdebates Ġprovide Ġa Ġsetting Ġthat Ġis Ġparticularly Ġconvenient Ġfor Ġstudying Ġthe Ġeffects Ġof Ġconvers ational Ġflow . ĠIn Ġthis Ġcompetitive Ġdebate Ġformat , Ġtwo Ġteams Ġargue Ġfor Ġor Ġagainst Ġa Ġpreset Ġmotion Ġin Ġorder Ġto Ġpersuade Ġa Ġlive Ġaudience Ġto Ġtake Ġtheir Ġposition . ĠThe Ġaudience Ġvotes Ġbefore Ġand Ġafter Ġthe Ġdebate , Ġand Ġthe Ġwinning Ġteam Ġis Ġthe Ġone Ġthat Ġs ways Ġmore Ġof Ġthe Ġaudience Ġtowards Ġits Ġview . ĠThis Ġsetup Ġallows Ġus Ġto Ġfocus Ġon Ġthe Ġeffects Ġof Ġconvers ational Ġflow Ġsince Ġit Ġdis ent angles Ġthem Ġfrom Ġthe Ġaudience 's Ġprior Ġleaning . </s> The Ġdebate Ġformat Ġinvolves Ġan Ġopening Ġstatement Ġfrom Ġthe Ġtwo Ġsides , Ġwhich Ġpresents Ġan Ġoverview Ġof Ġtheir Ġarguments Ġbefore Ġthe Ġdiscussion Ġbegins . ĠThis Ġallows Ġus Ġto Ġeasily Ġidentify Ġtalking Ġpoints Ġheld Ġby Ġthe Ġparticipants Ġprior Ġto Ġthe Ġinteraction , Ġand Ġconsider Ġthem Ġseparately Ġfrom Ġpoints Ġintroduced Ġspontaneously Ġto Ġserve Ġthe Ġdiscussion . </s> This Ġwork Ġis Ġtaking Ġsteps Ġtowards Ġbetter Ġmodeling Ġof Ġconvers ational Ġdynamics , Ġby : Ġ( i ) Ġintroducing Ġa Ġdebate Ġdataset Ġwith Ġrich Ġmetadata Ġ( Section ĠSEC REF 2 Ġ), Ġ( ii ) Ġproposing Ġa Ġframework Ġfor Ġtracking Ġthe Ġflow Ġof Ġideas Ġ( Section ĠSEC REF 3 Ġ), Ġand Ġ( iii ) Ġshowing Ġits Ġeffectiveness Ġin Ġa Ġpredictive Ġsetting Ġ( Section ĠSEC REF 4 Ġ). </s> Deb ate ĠDat as et : ĠIntelligence ĠSqu ared </s> In Ġthis Ġstudy Ġwe Ġuse Ġtranscripts Ġand Ġresults Ġof ĠOxford - style Ġdebates Ġfrom Ġthe Ġpublic Ġdebate Ġseries ĠâĢ ľ Int elligence ĠSqu ared ĠDeb ates âĢ Ŀ Ġ( IQ 2 Ġfor Ġshort ). ĠThese Ġdebates Ġare Ġrecorded Ġlive , Ġand Ġcontain Ġmotions Ġcovering Ġa Ġdiversity Ġof Ġtopics Ġranging Ġfrom Ġforeign Ġpolicy Ġissues Ġto Ġthe Ġbenefits Ġof Ġorganic Ġfood . ĠEach Ġdebate Ġconsists Ġof Ġtwo Ġopposing Ġteams âĢĶ one Ġfor Ġthe Ġmotion Ġand Ġone Ġagainst âĢĶ of Ġtwo Ġor Ġthree Ġexperts Ġin Ġthe Ġtopic Ġof Ġthe Ġparticular Ġmotion , Ġalong Ġwith Ġa Ġmoderator . ĠEach Ġdebate Ġfollows Ġthe ĠOxford - style Ġformat Ġand Ġconsists Ġof Ġthree Ġrounds . ĠIn Ġthe Ġintroduction , Ġeach Ġdeb ater Ġis Ġgiven Ġ7 Ġminutes Ġto Ġlay Ġout Ġtheir Ġmain Ġpoints . ĠDuring Ġthe Ġdiscussion , Ġdeb aters Ġtake Ġquestions Ġfrom Ġthe Ġmoderator Ġand Ġaudience , Ġand Ġrespond Ġto Ġattacks Ġfrom Ġthe Ġother Ġteam . ĠThis Ġround Ġlasts Ġaround Ġ30 Ġminutes Ġand Ġis Ġhighly Ġinteractive ; Ġteams Ġfrequently Ġengage Ġin Ġdirect Ġconversation Ġwith Ġeach Ġother . ĠFinally , Ġin Ġthe Ġconclusion , Ġeach Ġdeb ater Ġis Ġgiven Ġ2 Ġminutes Ġto Ġmake Ġfinal Ġremarks . </s> Our Ġdataset Ġconsists Ġof Ġthe Ġtranscripts Ġof Ġall Ġdebates Ġheld Ġby ĠIQ 2 Ġin Ġthe ĠUS Ġfrom ĠSeptember Ġ2006 Ġup Ġto ĠSeptember Ġ2015 ; Ġin Ġtotal , Ġthere Ġare Ġ108 Ġdebates . ĠEach Ġdebate Ġis Ġquite Ġextensive : Ġon Ġaverage , Ġ128 01 Ġwords Ġare Ġuttered Ġin Ġ117 Ġturns Ġby Ġmembers Ġof Ġeither Ġside Ġper Ġdebate . </s> Win ning Ġside Ġlabels . ĠWe Ġfollow ĠIQ 2 's Ġcriteria Ġfor Ġdeciding Ġwho Ġwins Ġa Ġdebate , Ġas Ġfollows . ĠBefore Ġthe Ġdebate , Ġthe Ġlive Ġaudience Ġvotes Ġon Ġwhether Ġthey Ġare Ġfor , Ġagainst , Ġor Ġundecided Ġon Ġthe Ġmotion . ĠA Ġsecond Ġround Ġof Ġvoting Ġoccurs Ġafter Ġthe Ġdebate . ĠA Ġside Ġwins Ġthe Ġdebate Ġif Ġthe Ġdifference Ġbetween Ġthe Ġpercentage Ġof Ġvotes Ġthey Ġreceive Ġpost - Ġand Ġpre - deb ate Ġ( the ĠâĢ ľ d elta âĢ Ŀ ) Ġis Ġgreater Ġthan Ġthat Ġof Ġthe Ġother Ġside 's . ĠOften Ġthe Ġdebates Ġare Ġquite Ġtight : Ġfor Ġ30 % Ġof Ġthe Ġdebates , Ġthe Ġdifference Ġbetween Ġthe Ġwinning Ġand Ġlosing Ġsides ' Ġdel t as Ġis Ġless Ġthan Ġ10 %. </s> Aud ience Ġfeedback . ĠWe Ġcheck Ġthat Ġthe Ġvoting Ġresults Ġare Ġmeaningful Ġby Ġverifying Ġthat Ġaudience Ġreactions Ġto Ġthe Ġdeb aters Ġare Ġrelated Ġto Ġdebate Ġoutcome . ĠUsing Ġlaughter Ġand Ġapplause Ġreceived Ġby Ġeach Ġside Ġin Ġeach Ġround Ġas Ġmarkers Ġof Ġpositive Ġreactions , Ġwe Ġnote Ġthat Ġdifferences Ġin Ġaudience Ġreception Ġof Ġthe Ġtwo Ġsides Ġemerge Ġover Ġthe Ġcourse Ġof Ġthe Ġdebate . ĠWhile Ġboth Ġsides Ġget Ġsimilar Ġlevels Ġof Ġreaction Ġduring Ġthe Ġintroduction , Ġwinning Ġteams Ġtend Ġto Ġreceive Ġmore Ġlaughter Ġduring Ġthe Ġdiscussion Ġ( ĠIN LINE FORM 0 Ġ) Ġand Ġmore Ġapplause Ġduring Ġthe Ġconclusion Ġ( ĠIN LINE FORM 2 Ġ). </s> Example Ġdebate . ĠWe Ġwill Ġuse Ġa Ġdebate Ġover Ġthe Ġmotion ĠâĢ ľ Mill enn ials Ġdon 't Ġstand Ġa Ġchance âĢ Ŀ Ġ( hen ce forth ĠMillennials ) Ġas Ġa Ġrunning Ġexample . ĠThe ĠFor Ġside Ġwon Ġthe Ġdebate Ġwith Ġa Ġdelta Ġof Ġ20 % Ġof Ġthe Ġvotes , Ġcompared Ġto Ġthe ĠAgainst Ġside Ġwhich Ġonly Ġgained Ġ5 %. </s> Mod eling ĠIdea ĠFlow </s> Prom oting Ġone 's Ġown Ġpoints Ġand Ġaddressing Ġthe Ġopponent 's Ġpoints Ġare Ġtwo Ġprimary Ġdebating Ġstrategies . ĠHere Ġwe Ġintroduce Ġa Ġmethodology Ġto Ġidentify Ġthese Ġstrategies , Ġand Ġuse Ġit Ġto Ġinvestigate Ġtheir Ġusage Ġand Ġeffect Ġon Ġa Ġdebate 's Ġoutcome . </s> Ident ifying Ġtalking Ġpoints Ġ. ĠWe Ġfirst Ġfocus Ġon Ġideas Ġwhich Ġform Ġthe Ġbasis Ġof Ġa Ġside 's Ġstance Ġon Ġthe Ġmotion . ĠWe Ġidentify Ġsuch Ġtalking Ġpoints Ġby Ġconsidering Ġwords Ġwhose Ġfrequency Ġof Ġusage Ġdiffers Ġsignificantly Ġbetween Ġthe Ġtwo Ġteams Ġduring Ġthe Ġintroduction , Ġbefore Ġany Ġinteraction Ġtakes Ġplace . ĠTo Ġfind Ġthese Ġwords , Ġwe Ġuse Ġthe Ġmethod Ġintroduced Ġby Ġmon roe 2008 fight in Ġin Ġthe Ġcontext Ġof ĠU . S . ĠSenate Ġspeeches . ĠIn Ġparticular , Ġwe Ġestimate Ġthe Ġdivergence Ġbetween Ġthe Ġtwo Ġsides ' Ġword - usage Ġin Ġthe Ġintroduction , Ġwhere Ġword - usage Ġis Ġmodeled Ġas Ġmult in omial Ġdistributions Ġsmoot hed Ġwith Ġa Ġuniform ĠDir ich let Ġprior , Ġand Ġdivergence Ġis Ġgiven Ġby Ġlog - odd s Ġratio . ĠThe Ġmost Ġdiscriminating Ġwords Ġare Ġthose Ġwith Ġthe Ġhighest Ġand Ġlowest Ġz - sc ores Ġof Ġdivergence Ġestimates . ĠFor Ġa Ġside ĠIN LINE FORM 0 Ġ, Ġwe Ġdefine Ġthe Ġset Ġof Ġtalking Ġpoints ĠIN LINE FORM 1 Ġto Ġbe Ġthe ĠIN LINE FORM 2 Ġwords Ġwith Ġthe Ġhighest Ġor Ġlowest ĠIN LINE FORM 3 Ġ- sc ores . ĠWe Ġdistinguish Ġbetween ĠIN LINE FORM 5 Ġ' s Ġown Ġtalking Ġpoints ĠIN LINE FORM 6 Ġ, Ġand Ġthe Ġopposing Ġtalking Ġpoints ĠIN LINE FORM 7 Ġbelonging Ġto Ġits Ġopponent ĠIN LINE FORM 8 Ġ. ĠThese Ġare Ġexamples Ġof Ġtalking Ġpoints Ġfor Ġthe ĠâĢ ľ Mill enn ials âĢ Ŀ Ġdebate : </s> The Ġflow Ġof Ġtalking Ġpoints Ġ. ĠA Ġside Ġcan Ġeither Ġpromote Ġits Ġown Ġtalking Ġpoints Ġ, Ġaddress Ġits Ġopponent 's Ġpoints , Ġor Ġsteer Ġaway Ġfrom Ġthese Ġinitially Ġsalient Ġideas Ġaltogether . ĠWe Ġquantify Ġthe Ġuse Ġof Ġthese Ġstrategies Ġby Ġcomparing Ġthe Ġair time Ġdeb aters Ġdevote Ġto Ġtalking Ġpoints Ġ. ĠFor Ġa Ġside ĠIN LINE FORM 0 Ġ, Ġlet Ġthe Ġself - co verage ĠIN LINE FORM 1 Ġbe Ġthe Ġfraction Ġof Ġcontent Ġwords Ġuttered Ġby ĠIN LINE FORM 2 Ġin Ġround ĠIN LINE FORM 3 Ġthat Ġare Ġamong Ġtheir Ġown Ġtalking Ġpoints ĠIN LINE FORM 4 Ġ; Ġand Ġthe Ġopponent - co verage ĠIN LINE FORM 5 Ġbe Ġthe Ġfraction Ġof Ġits Ġcontent Ġwords Ġcovering Ġopposing Ġtalking Ġpoints ĠIN LINE FORM 6 Ġ. </s> Not Ġsurprisingly , Ġwe Ġfind Ġthat Ġself - co verage Ġdominates Ġduring Ġthe Ġdiscussion Ġ( ĠIN LINE FORM 0 Ġ, ĠIN LINE FORM 1 Ġ). ĠHowever , Ġthis Ġdoes Ġnot Ġmean Ġdeb aters Ġare Ġsimply Ġgiving Ġmon olog ues Ġand Ġignoring Ġeach Ġother : Ġthe Ġeffect Ġof Ġthe Ġinteraction Ġis Ġreflected Ġin Ġa Ġsharp Ġdrop Ġin Ġself - co verage Ġand Ġa Ġrise Ġin Ġopponent - co verage Ġonce Ġthe Ġdiscussion Ġround Ġbegins . ĠRespect ively , ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġ, Ġboth ĠIN LINE FORM 4 Ġ. ĠExamples Ġof Ġself - Ġand Ġopponent - co verage Ġof Ġtwo Ġtalking Ġpoint Ġs Ġin Ġthe ĠâĢ ľ Mill enn ials âĢ Ŀ Ġdebate Ġfrom Ġthe Ġintroduction Ġand Ġdiscussion Ġare Ġgiven Ġin ĠTable ĠT AB REF 9 Ġ. </s> Does Ġthe Ġchange Ġin Ġfocus Ġtranslate Ġto Ġany Ġstrategic Ġadvantages ? ĠFigure ĠFIG REF 11 Ġsuggests Ġthis Ġis Ġthe Ġcase : Ġthe Ġdrop Ġin Ġself - co verage Ġis Ġslightly Ġlarger Ġfor Ġthe Ġside Ġthat Ġeventually Ġwins Ġthe Ġdebate Ġ( ĠIN LINE FORM 0 Ġ). ĠThe Ġdrop Ġin Ġthe Ġsum Ġof Ġself - Ġand Ġopponent - co verage Ġis Ġalso Ġlarger Ġfor Ġwinning Ġteams , Ġsuggesting Ġthat Ġthey Ġare Ġmore Ġlikely Ġto Ġsteer Ġaway Ġfrom Ġdiscussing Ġany Ġtalking Ġpoints Ġfrom Ġeither Ġside Ġ( ĠIN LINE FORM 1 Ġ). </s> Ident ifying Ġdiscussion Ġpoints . ĠHaving Ġseen Ġthat Ġdeb aters Ġcan Ġbenefit Ġby Ġshifting Ġaway Ġfrom Ġtalking Ġpoints Ġthat Ġwere Ġsalient Ġduring Ġthe Ġintroduction , Ġwe Ġnow Ġexamine Ġthe Ġideas Ġthat Ġspontaneously Ġarise Ġto Ġserve Ġthe Ġdiscussion . ĠWe Ġmodel Ġsuch Ġdiscussion Ġpoints Ġas Ġwords Ġintroduced Ġto Ġthe Ġdebate Ġduring Ġthe Ġdiscussion Ġby Ġa Ġdeb ater Ġand Ġadopted Ġby Ġhis Ġopponents Ġat Ġleast Ġtwice . ĠThis Ġallows Ġus Ġto Ġfocus Ġon Ġwords Ġthat Ġbecome Ġrelevant Ġto Ġthe Ġconversation ; Ġonly Ġ3 % Ġof Ġall Ġnewly Ġintroduced Ġwords Ġqualify , Ġamount ing Ġto Ġabout Ġ10 Ġdiscussion Ġpoints Ġper Ġdebate . </s> The Ġflow Ġof Ġdiscussion Ġpoints Ġ. ĠThe Ġadoption Ġof Ġdiscussion Ġpoints Ġplays Ġan Ġimportant Ġrole Ġin Ġpersu ading Ġthe Ġaudience : Ġduring Ġthe Ġdiscussion , Ġeventual Ġwinners Ġadopt Ġmore Ġdiscussion Ġpoints Ġintroduced Ġby Ġtheir Ġopponents Ġthan Ġeventual Ġlosers Ġ( ĠIN LINE FORM 0 Ġ). ĠTwo Ġpossible Ġstrategic Ġinterpretations Ġemerge . ĠFrom Ġa Ġtopic Ġcontrol Ġangle ĠB IB REF 0 Ġ, Ġperhaps Ġlosers Ġare Ġmore Ġsuccessful Ġat Ġimposing Ġtheir Ġdiscussion Ġpoints Ġto Ġgain Ġcontrol Ġof Ġthe Ġdiscussion . ĠThis Ġview Ġappears Ġcounter intuitive Ġgiven Ġwork Ġlinking Ġtopic Ġcontrol Ġto Ġinfluence Ġin Ġother Ġsettings ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ. </s> An Ġalternative Ġinterpretation Ġcould Ġbe Ġthat Ġwinners Ġare Ġmore Ġactive Ġthan Ġlosers Ġin Ġcontest ing Ġtheir Ġopponents ' Ġpoints , Ġa Ġstrategy Ġthat Ġmight Ġplay Ġout Ġfavorably Ġto Ġthe Ġaudience . ĠA Ġpost - h oc Ġmanual Ġexamination Ġsupports Ġthis Ġinterpretation : Ġ78 % Ġof Ġthe Ġvalid Ġdiscussion Ġpoints Ġare Ġpicked Ġup Ġby Ġthe Ġopposing Ġside Ġin Ġorder Ġto Ġbe Ġchallenged ; Ġthis Ġstrategy Ġis Ġexempl ified Ġin ĠTable ĠT AB REF 14 Ġ. ĠOverall , Ġthese Ġobservations Ġtying Ġthe Ġflow Ġof Ġdiscussion Ġpoints Ġto Ġthe Ġdebate 's Ġoutcome Ġsuggest Ġthat Ġwinners Ġare Ġmore Ġsuccessful Ġat Ġusing Ġthe Ġinteraction Ġto Ġengage Ġwith Ġtheir Ġopponents ' Ġideas . </s> P redict ive ĠPower </s> We Ġevaluate Ġthe Ġpredictive Ġpower Ġof Ġour Ġflow Ġfeatures Ġin Ġa Ġbinary Ġclassification Ġsetting : Ġpredict Ġwhether Ġthe ĠFor Ġor ĠAgainst Ġside Ġwins Ġthe Ġdebate . ĠThis Ġis Ġa Ġchallenging Ġtask Ġeven Ġfor Ġhumans , Ġthus Ġthe Ġdramatic Ġreveal Ġat Ġthe Ġend Ġof Ġeach ĠIQ 2 Ġdebate Ġthat Ġpartly Ġexplains Ġthe Ġpopularity Ġof Ġthe Ġshow . ĠOur Ġgoal Ġhere Ġis Ġlimited Ġto Ġunderstanding Ġwhich Ġof Ġthe Ġflow Ġfeatures Ġthat Ġwe Ġdeveloped Ġcarry Ġpredictive Ġpower . </s> Con vers ation Ġflow Ġfeatures . ĠWe Ġuse Ġall Ġconvers ational Ġfeatures Ġdiscussed Ġabove . ĠFor Ġeach Ġside ĠIN LINE FORM 0 Ġwe Ġinclude ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġ, Ġand Ġtheir Ġsum . ĠWe Ġalso Ġuse Ġthe Ġdrop Ġin Ġself - co verage Ġgiven Ġby Ġsubtract ing Ġcorresponding Ġvalues Ġfor ĠIN LINE FORM 3 Ġ, Ġand Ġthe Ġnumber Ġof Ġdiscussion Ġpoints Ġadopted Ġby Ġeach Ġside . ĠWe Ġcall Ġthese Ġthe ĠFlow Ġfeatures . </s> Bas eline Ġfeatures . ĠTo Ġdiscard Ġthe Ġpossibility Ġthat Ġour Ġresults Ġare Ġsimply Ġexplained Ġby Ġdeb ater Ġverb osity , Ġwe Ġuse Ġthe Ġnumber Ġof Ġwords Ġuttered Ġand Ġnumber Ġof Ġturns Ġtaken Ġby Ġeach Ġside Ġ( length ) Ġas Ġbas elines . ĠWe Ġalso Ġcompare Ġto Ġa Ġun ig ram Ġbaseline Ġ( B OW ). </s> Aud ience Ġfeatures . ĠWe Ġuse Ġthe Ġcounts Ġof Ġapplause Ġand Ġlaughter Ġreceived Ġby Ġeach Ġside Ġ( described Ġin ĠSection ĠSEC REF 2 Ġ) Ġas Ġrough Ġindicators Ġof Ġhow Ġwell Ġthe Ġaudience Ġcan Ġforesee Ġa Ġdebate 's Ġoutcome . </s> Pred iction Ġaccuracy Ġis Ġevaluated Ġusing Ġa Ġleave - one - out Ġ( L OO ) Ġapproach . ĠWe Ġuse Ġlog istic Ġregression ; Ġmodel Ġparameters Ġfor Ġeach ĠL OO Ġtrain - test Ġsplit Ġare Ġselected Ġvia Ġ3 - fold Ġcross - valid ation Ġon Ġthe Ġtraining Ġset . ĠTo Ġfind Ġparticularly Ġpredictive Ġflow Ġfeatures , Ġwe Ġalso Ġtry Ġusing Ġun ivariate Ġfeature Ġselection Ġon Ġthe Ġflow Ġfeatures Ġbefore Ġthe Ġmodel Ġis Ġfitted Ġin Ġeach Ġsplit ; Ġwe Ġrefer Ġto Ġthis Ġsetting Ġas ĠFlow *. </s> We Ġfind Ġthat Ġconversation Ġflow Ġfeatures Ġobtain Ġthe Ġbest Ġaccuracy Ġamong Ġall Ġlisted Ġfeature Ġtypes Ġ( Flow : Ġ63 %; ĠFlow *: Ġ65 %), Ġperforming Ġsignificantly Ġhigher Ġthan Ġa Ġ50 % Ġrandom Ġbaseline Ġ( bin omial Ġtest ĠIN LINE FORM 0 Ġ), Ġand Ġcomparable Ġto Ġaudience Ġfeatures Ġ( 60 %). ĠIn Ġcontrast , Ġthe Ġlength Ġand ĠB OW Ġbas elines Ġdo Ġnot Ġperform Ġbetter Ġthan Ġchance . ĠWe Ġnote Ġthat ĠFlow Ġfeatures Ġperform Ġcompetitive ly Ġdespite Ġbeing Ġthe Ġonly Ġones Ġthat Ġdo Ġnot Ġfactor Ġin Ġthe Ġconcluding Ġround . </s> The Ġfeatures Ġselected Ġmost Ġoften Ġin Ġthe ĠFlow * Ġtask Ġare : Ġthe Ġnumber Ġof Ġdiscussion Ġpoints Ġadopted Ġ( with Ġpositive Ġregression Ġcoefficients ), Ġthe Ġrecall Ġof Ġtalking Ġpoints Ġduring Ġthe Ġdiscussion Ġround Ġ( negative Ġcoefficients ), Ġand Ġthe Ġdrop Ġin Ġusage Ġof Ġown Ġtalking Ġpoints Ġfrom Ġintroduction Ġto Ġdiscussion Ġ( positive Ġcoefficients ). ĠThe Ġrelative Ġimportance Ġof Ġthese Ġfeatures , Ġwhich Ġfocus Ġon Ġthe Ġinteraction Ġbetween Ġteams , Ġsuggests Ġthat Ġaudiences Ġtend Ġto Ġfavor Ġdebating Ġstrategies Ġwhich Ġemphasize Ġthe Ġdiscussion . </s> Further ĠRelated ĠWork </s> Previous Ġwork Ġon Ġconvers ational Ġstructure Ġhas Ġproposed Ġapproaches Ġto Ġmodel Ġdialogue Ġacts ĠB IB REF 3 Ġ, ĠB IB REF 4 Ġ, ĠB IB REF 5 Ġor Ġdis ent angle Ġinter le aved Ġconversations ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ. ĠOther Ġresearch Ġhas Ġconsidered Ġthe Ġproblem Ġof Ġdetecting Ġconversation - level Ġtraits Ġsuch Ġas Ġthe Ġpresence Ġof Ġdisagreements ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġor Ġthe Ġlikelihood Ġof Ġrelation Ġdissolution ĠB IB REF 10 Ġ. ĠAt Ġthe Ġparticipant Ġlevel , Ġseveral Ġstudies Ġpresent Ġapproaches Ġto Ġidentify Ġideological Ġstances ĠB IB REF 11 Ġ, ĠB IB REF 12 Ġ, Ġusing Ġfeatures Ġbased Ġon Ġparticipant Ġinteractions ĠB IB REF 13 Ġ, ĠB IB REF 14 Ġ, Ġor Ġextracting Ġwords Ġand Ġreasons Ġcharacter izing Ġa Ġstance ĠB IB REF 15 Ġ, ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ. ĠIn Ġour Ġsetting , Ġboth Ġthe Ġstances Ġand Ġthe Ġturn Ġstructure Ġof Ġa Ġdebate Ġare Ġknown , Ġallowing Ġus Ġto Ġinstead Ġfocus Ġon Ġthe Ġdebate 's Ġoutcome . </s> Ex isting Ġresearch Ġon Ġargument ation Ġstrategies Ġhas Ġlargely Ġfocused Ġon Ġexploiting Ġthe Ġstructure Ġof Ġmon ologic Ġarguments ĠB IB REF 18 Ġ, Ġlike Ġthose Ġof Ġpersuasive Ġessays ĠB IB REF 19 Ġ, ĠB IB REF 20 Ġ. ĠIn Ġaddition , Ġtan + et al : 16 a Ġhas Ġexamined Ġthe Ġeffectiveness Ġof Ġarguments Ġin Ġthe Ġcontext Ġof Ġa Ġforum Ġwhere Ġpeople Ġinvite Ġothers Ġto Ġchallenge Ġtheir Ġopinions . ĠWe Ġcomplement Ġthis Ġline Ġof Ġwork Ġby Ġlooking Ġat Ġthe Ġrelative Ġpersu as iveness Ġof Ġparticipants Ġin Ġextended Ġconversations Ġas Ġthey Ġexchange Ġarguments Ġover Ġmultiple Ġturns . </s> Previous Ġstudies Ġof Ġinfluence Ġin Ġextended Ġconversations Ġhave Ġlargely Ġdealt Ġwith Ġthe Ġpolitical Ġdomain , Ġexamining Ġmoder ated Ġbut Ġrelatively Ġun struct ured Ġsettings Ġsuch Ġas Ġtalk Ġshows Ġor Ġpresidential Ġdebates , Ġand Ġsuggesting Ġfeatures Ġlike Ġtopic Ġcontrol ĠB IB REF 0 Ġ, Ġlinguistic Ġstyle Ġmatching ĠB IB REF 21 Ġand Ġturn - taking ĠB IB REF 22 Ġ. ĠWith Ġpersuasion Ġin Ġmind , Ġour Ġwork Ġextends Ġthese Ġstudies Ġto Ġexplore Ġa Ġnew Ġdynamic , Ġthe Ġflow Ġof Ġideas Ġbetween Ġspeakers , Ġin Ġa Ġhighly Ġstructured Ġsetting Ġthat Ġcontrols Ġfor Ġconfounding Ġfactors . </s> Lim itations Ġand ĠFuture ĠWork </s> This Ġstudy Ġopens Ġseveral Ġavenues Ġfor Ġfuture Ġresearch . ĠOne Ġcould Ġexplore Ġmore Ġcomplex Ġrepresentations Ġof Ġtalking Ġpoints Ġand Ġdiscussion Ġpoints Ġ, Ġfor Ġinstance Ġusing Ġtopic Ġmodels Ġor Ġword Ġembed d ings . ĠFurthermore , Ġaugment ing Ġthe Ġflow Ġof Ġcontent Ġin Ġa Ġconversation Ġwith Ġthe Ġspeakers ' Ġlinguistic Ġchoices Ġcould Ġbetter Ġcapture Ġtheir Ġintentions . ĠIn Ġaddition , Ġit Ġwould Ġbe Ġinteresting Ġto Ġstudy Ġthe Ġinter play Ġbetween Ġour Ġconvers ational Ġflow Ġfeatures Ġand Ġrelatively Ġmon ologic Ġfeatures Ġthat Ġconsider Ġthe Ġargument ative Ġand Ġrhetorical Ġtraits Ġof Ġeach Ġside Ġseparately . ĠMore Ġexplicitly Ġcomparing Ġand Ġcontrasting Ġmon ologic Ġand Ġinteractive Ġdynamics Ġcould Ġlead Ġto Ġbetter Ġmodels Ġof Ġconversations . ĠSuch Ġapproaches Ġcould Ġalso Ġhelp Ġclarify Ġsome Ġof Ġthe Ġintu itions Ġabout Ġconversations Ġexplored Ġin Ġthis Ġwork , Ġparticularly Ġthat Ġengaging Ġin Ġdialogue Ġcarries Ġdifferent Ġstrategic Ġimplications Ġfrom Ġself - prom otion . </s> Our Ġfocus Ġin Ġthis Ġpaper Ġis Ġon Ġcapturing Ġand Ġunderstanding Ġconvers ational Ġflow . ĠWe Ġhence Ġmake Ġsome Ġsimpl ifying Ġassumptions Ġthat Ġcould Ġbe Ġrefined Ġin Ġfuture Ġwork . ĠFor Ġinstance , Ġby Ġusing Ġa Ġbasic Ġun ig ram - based Ġdefinition Ġof Ġdiscussion Ġpoints Ġ, Ġwe Ġdo Ġnot Ġaccount Ġfor Ġthe Ġcontext Ġor Ġsemantic Ġsense Ġin Ġwhich Ġthese Ġpoints Ġoccur . ĠIn Ġparticular , Ġour Ġannot ators Ġfound Ġthat Ġa Ġsignificant Ġproportion Ġof Ġthe Ġdiscussion Ġpoints Ġunder Ġour Ġdefinition Ġactually Ġreferred Ġto Ġdiffering Ġideas Ġin Ġthe Ġvarious Ġcontexts Ġin Ġwhich Ġthey Ġappeared . ĠWe Ġexpect Ġthat Ġimproving Ġour Ġretrieval Ġmodel Ġwill Ġalso Ġimprove Ġthe Ġrobust ness Ġof Ġour Ġidea Ġflow Ġanalysis . ĠA Ġbetter Ġmodel Ġof Ġdiscussion Ġpoints Ġcould Ġalso Ġprovide Ġmore Ġinsight Ġinto Ġthe Ġrole Ġof Ġthese Ġpoints Ġin Ġpersu ading Ġthe Ġaudience . </s> While ĠOxford - style Ġdebates Ġare Ġa Ġparticularly Ġconvenient Ġsetting Ġfor Ġstudying Ġthe Ġeffects Ġof Ġconvers ational Ġflow , Ġour Ġdataset Ġis Ġlimited Ġin Ġterms Ġof Ġsize . ĠIt Ġwould Ġbe Ġworthwhile Ġto Ġexamine Ġthe Ġflow Ġfeatures Ġwe Ġdeveloped Ġin Ġthe Ġcontext Ġof Ġsettings Ġwith Ġricher Ġincentives Ġbeyond Ġpersu ading Ġan Ġaudience , Ġsuch Ġas Ġin Ġthe Ġsemi - co operative Ġenvironment Ġof ĠWikipedia Ġtalk Ġpages . ĠFinally , Ġour Ġmethodology Ġcould Ġpoint Ġto Ġapplications Ġin Ġareas Ġsuch Ġas Ġeducation Ġand Ġcooperative Ġwork , Ġwhere Ġit Ġis Ġkey Ġto Ġestablish Ġthe Ġlink Ġbetween Ġconversation Ġfeatures Ġand Ġan Ġinter loc utor 's Ġability Ġto Ġconvey Ġtheir Ġpoint ĠB IB REF 23 Ġ. </s> Acknowled gements . ĠWe Ġthank Ġthe Ġreviewers Ġand ĠV . ĠNic ul ae Ġfor Ġtheir Ġhelpful Ġcomments , Ġand ĠI . ĠA raw jo Ġand ĠD . ĠSed ra Ġfor Ġannotations . ĠThis Ġwork Ġwas Ġsupported Ġin Ġpart Ġby Ġa ĠGoogle ĠFaculty ĠResearch ĠAward .