Does what readers believe matter more than what debaters say?

Most NLP work on argument persuasion treats persuasion as a function of language — model the words, you model the outcome. Durmus and Cardie's debate-platform study contradicts this. When you label voters by political and religious ideology and add those features alongside linguistic features of the debate text, the prior-belief features outpredict the linguistic features for predicting who wins. The single largest signal in persuasion is not what the debater said but what the audience already believed.

The methodological consequence is sharp. Studies that omit reader-level controls are estimating a confounded version of the language-of-persuasion effect: any feature of the text that correlates with the topic of the debate inherits whatever audience composition is correlated with that topic. The apparent "language effect" includes a hidden audience effect. Adding ideology controls does not eliminate language effects entirely — they remain useful — but it changes which linguistic features emerge as predictive, sometimes dramatically. The most-predictive feature set is unstable across the two regression specifications.

This shifts the framing of persuasion research. Persuasion is not solely a property of the persuasive text; it is a property of the encounter between a text and a reader who comes with priors. The interpretation is reader-mediated, and the reader's interpretive frame is largely set before the argument arrives. Language matters at the margin — most heavily for readers whose priors are already weakly held — but ideology mattered first.

The implication for LLM persuasion studies is uncomfortable. Many papers measure "LLM persuasiveness" on undifferentiated audiences and report aggregate stance shifts. If reader ideology is the dominant variable, those numbers are heavily averaged over heterogeneous reader-level effects. The same LLM output may be highly persuasive to readers with congruent priors and useless against readers with opposed priors.