Identifying the reasons for disagreements between influential points of view on issues that affect the public can help produce informed policy responses, even if they do not bring disagreeing parties closer to agreement. We present a methodology for extracting reasoning chains - the sequences of premises that motivate or justify opinions - from natural discourse, and for characterizing the types of premises (facts, forecasts, definitions, causal beliefs, and evaluations) that make up these chains. We demonstrate the utility of this approach for two practical goals: diagnosing specific points of contention and aggregating arguments across speakers. We illustrate the methodology through an analysis of the debates on the nature of risks that AI poses to the public, using a corpus of interviews from the Lex Fridman podcast. We find that differences in perspectives among the podcast's guests on existential risk and employment risk from AI arise primarily from differences in causal premises and forecasts, whereas in the case of AI's effects on human social relationships, premises regarding what is valued and definitions about what counts as genuine human connection play a distinctively larger role. Our approach to analyzing reasoning chains at scale, using an ensemble of LLMs to parse textual data, can be applied to facilitate deliberation and aggregation of opinions on any topic.

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