The structure of reasoning: Inferring conceptual networks from text

For decades, public opinion scholars have argued for the need to go beyond measuring isolated political preferences to more richly examine how individuals reason about and justify the interconnections between their preferences. While early efforts used interviews and hand-coding to elicit the network structure of subjects’ political reasoning, this manual approach can be time-consuming and difficult. Thankfully, modern computational methods and computing power hold the potential to revolutionize this space and allow for network inference at scale. In this paper, we therefore present a computational, text-based approach for inferring the network structure of individuals’ political reasoning. This method identifies the key concepts a person raises and examines the implicit connections between those concepts—what ideas do they connect to which other ideas? This structural approach is theoretically justified in both the cognitive and linguistic literatures, which repeatedly suggest that humans store, retrieve, and interpret information through network structures. We show that this approach reveals meaningful individual variation that is correlated with known behavioral traits. The ability to measure and interrogate individuals’ expressions of political reasoning holds the potential to shed new light on the dynamics of public opinion and political behavior. Questions of persuasion, ideological fracturing, and conversation quality all rely upon understanding individual styles of political expression. These dynamics are driven not just by what someone says but by how they say it.