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.
Identifying influential speakers in multi-party conversations has been the focus of research in communication, sociology, and psychology for decades. It has been long acknowledged qualitatively that controlling the topic of a conversation is a sign of influence. To capture who introduces new topics in conversations, we introduce SITS--Speaker Identity for Topic Segmentation--a nonparametric hierarchical Bayesian model that is capable of discovering (1) the topics used in a set of conversations, (2) how these topics are shared across conversations, (3) when these topics change during conversations, and (4) a speaker-specific measure of "topic control". We validate the model via evaluations using multiple datasets, including work meetings, online discussions, and political debates. Experimental results confirm the effectiveness of SITS in both intrinsic and extrinsic evaluations. In this paper, we present an automatic system to rank participants of an interaction in terms of their relative power. We find several linguistic and structural features to be effective in predicting these rankings. We conduct our study in the domain of political debates, specifically the 2012 Republican presidential primary debates. Our dataset includes textual transcripts of 20 debates with 4-9 candidates as participants per debate. We model the power index of each candidate in terms of their relative poll standings in the state and national polls. We find that the candidates’ power indices affect the way they interact with others and the way others interact with them. We obtained encouraging results in our experiments and we expect these findings to carry across to other genres of multi-party conversations. The current research used the contexts of U.S. presidential debates and negotiations to examine whether matching the linguistic style of an opponent in a two-party exchange affects the reactions of third-party observers. Building off communication accommodation theory (CAT), interaction alignment theory (IAT), and processing fluency, we propose that language style matching (LSM) will improve subsequent third-party evaluations because matching an opponent’s linguistic style reflects greater perspective taking and will make one’s arguments easier to process. In contrast, research on status inferences predicts that LSM will negatively impact third-party evaluations because LSM implies followership. We conduct two studies to test these competing hypotheses. Study 1 analyzed transcripts of U.S. presidential debates between 1976 and 2012 and found that candidates who matched their opponent’s linguistic style increased their standing in the polls. Study 2 demonstrated a causal relationship between LSM and third-...