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Causal Reasoning and Online Science Skepticism


Causation is central to the way that people make sense of events and facts, and confusion about causation may breed mistrust, rumors, and speculation. Confronted with conflicting narratives in a landscape of information disorder, social groups cohere around shared heuristics for selecting and constructing causal narratives. Causal Reasoning and Online Science Skepticism (CROSS) is an innovative research program which uses social media data as a window into the causal reasoning and argumentation that science promoters and science skeptics use to motivate their causal narratives. Based around three case studies where popular online counter-narratives conflict with scientific consensus (anthropogenic climate change, vaccine efficacy, and the transmission COVID-19), CROSS is guided by three core research questions:

  • RQ1: What are the linguistic, discursive, rhetorical and topical patterns that characterize science skeptical arguments of a causal nature?
  • RQ2: Which types of science denying causal narratives are potentially universal and which are culturally anchored?
  • RQ3: What types of causal and counterfactual reasoning underly the defining arguments and narratives of science denial movements?

The aim of CROSS is to shed light on the cognitive, inferential, and discursive foundations of science denial by combining the strengths of both theoretical and computational linguistics, with the long-term goal of improving scientific communication strategies and public uptake. It will also entail major contributions to the related fields of cognitive causal modeling and linguistic semantics, and the branch of AI known as Natural Language Understanding (NLU).  Additionally, by working with multilingual datasets in the setting of a linguistics department, CROSS can begin to evaluate which properties of science denial narratives are potentially universal and which are culturally anchored.