Conversational flow in Oxford-style debates

Zhang, Justine, Kumar, Ravi, Ravi, Sujith, Danescu-Niculescu-Mizil, Cristian

arXiv.org Machine Learning 

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.

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