Leveraging Linguistic Content and Debater Traits to Predict Debate Outcomes

Since the earliest televised debates, cognitive and political sciences have been interested in how voters respond to political candidates and their messages, both verbal and nonverbal. The present work draws from this long tradition and combines it with work on persuasion and rhetoric to inform analyses of a new corpus of debate data: 48 transcripts from the Intelligence Squared U.S. series, televised Oxford-style debates on relevant sociopolitical issues (http://www.iq2us.org). As a first look at this corpus, we focus on how linguistic content (i.e., hedging and pronoun use) and debater traits (i.e., attractiveness and negativity) interact with arbitrary group identity (i.e., “for” vs. “against”) to affect debate outcomes. Interestingly, we find that arbitrary group identity (i.e., “for” vs. “against” labels created by the framing of the debate rather than the actual opinions held) significantly affects the ways in which linguistic content and debater traits influence voters.