2017/05/15 by Lu Wang, Nick Beauchamp, Wang, Lu +5 · 1 citation
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Media Influence and Politics #Misinformation and Its Impacts #Sentiment Analysis and Opinion Mining
paper · pdf · doi:10.48550/arxiv.1705.05040
openalex publication_date 2017/05/15 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments. We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two. Using a dataset of 118 Oxford-style debates, our model's combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%). Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments.