A methodological approach for inferring causal relationships from opinions and news-derived events with an application to climate change.

ABSTRACT: Social media platforms like Twitter (now X) provide a global forum for discussing ideas. In this work, we propose a novel methodology for detecting causal relationships in online discourse. Our approach integrates multiple causal inference techniques to analyze how public sentiment and discourse evolve in response to key events and influential figures, using five causal detection methods: Direct-LiNGAM, PC, PCMCI, VAR, and stochastic causality. The datasets contain variables, such as different topics, sentiments, and real-world events, among which we seek to detect causal relationships at different frequencies. The proposed methodology is applied to climate change opinions and data, offering insights into the causal relationships among public sentiment, specific topics, and natural disaste

SUBMITTER: Marten J 

PROVIDER: S-EPMC12193450 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature