Conversational Flow in Oxford-style Debates

Public debates are a common platform for presenting and juxtaposing diverging views on important issues. In this work we propose a methodology for tracking how ideas flow between participants throughout a debate. We use this approach in a case study of Oxford-style debates---a competitive format where the winner is determined by audience votes---and show how the outcome of a debate depends on aspects of conversational flow. In particular, we find that winners tend to make better use of a debate's interactive component than losers, by actively pursuing their opponents' points rather than promoting their own ideas over the course of the conversation.

[1]  Philip Resnik,et al.  Modeling topic control to detect influence in conversations using nonparametric topic models , 2014, Machine Learning.

[2]  Cristian Danescu-Niculescu-Mizil,et al.  Conversational Markers of Constructive Discussions , 2016, NAACL.

[3]  Graeme Hirst,et al.  Classifying arguments by scheme , 2011, ACL.

[4]  Kathy McKeown,et al.  I Couldn't Agree More: The Role of Conversational Structure in Agreement and Disagreement Detection in Online Discussions , 2015, SIGDIAL Conference.

[5]  S. Planalp,et al.  Not to Change the Topic But …: A Cognitive Approach to the Management of Conversation , 1980 .

[6]  Matt Thomas,et al.  Get out the vote: Determining support or opposition from Congressional floor-debate transcripts , 2006, EMNLP.

[7]  Marie-Francine Moens,et al.  Argumentation mining , 2011, Artificial Intelligence and Law.

[8]  Ken Samuel,et al.  Dialogue Act Tagging with Transformation-Based Learning , 1998, ACL.

[9]  Carolyn Penstein Rosé,et al.  An analysis of perspectives in interactive settings , 2010, SOMA '10.

[10]  Daniel Gatica-Perez,et al.  Detection and application of influence rankings in small group meetings , 2006, ICMI '06.

[11]  Cristian Danescu-Niculescu-Mizil,et al.  Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions , 2016, WWW.

[12]  Claire Cardie,et al.  A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection , 2014, ACL.

[13]  Micha Elsner,et al.  Disentangling Chat , 2010, CL.

[14]  Swapna Somasundaran,et al.  Recognizing Stances in Ideological On-Line Debates , 2010, HLT-NAACL 2010.

[15]  Vincent Ng,et al.  Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates , 2014, EMNLP.

[16]  Cristian Danescu-Niculescu-Mizil,et al.  Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game , 2015, ACL.

[17]  Micha Elsner,et al.  Disentangling Chat with Local Coherence Models , 2011, ACL.

[18]  James R. Foulds,et al.  Joint Models of Disagreement and Stance in Online Debate , 2015, ACL.

[19]  Alan Ritter,et al.  Unsupervised Modeling of Twitter Conversations , 2010, NAACL.

[20]  Oliver Ferschke,et al.  Behind the Article: Recognizing Dialog Acts in Wikipedia Talk Pages , 2012, EACL.

[21]  Burt L. Monroe,et al.  Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict , 2008, Political Analysis.

[22]  Giuseppe Carenini,et al.  Detecting Disagreement in Conversations using Pseudo-Monologic Rhetorical Structure , 2014, EMNLP.

[23]  Daniel M. Romero,et al.  Mimicry Is Presidential , 2015, Personality & social psychology bulletin.

[24]  Iryna Gurevych,et al.  Identifying Argumentative Discourse Structures in Persuasive Essays , 2014, EMNLP.

[25]  Dorée D. Seligmann,et al.  Who Had the Upper Hand? Ranking Participants of Interactions Based on Their Relative Power , 2013, IJCNLP.