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

## TL;DR

This paper introduces a new method to detect causal relationships in social media discussions, focusing on how events and opinions influence each other in the context of climate change.

## Contribution

A novel methodology combining multiple causal inference techniques to analyze social media discourse and detect causal relationships.

## Key findings

- Public sentiment and discourse evolve in response to key events and influential figures.
- Causal relationships can be detected among topics, sentiments, and real-world events like natural disasters.
- The methodology can hypothesize that discussions on specific topics precede changes in sentiment or stance proportions.

## 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 disasters. This approach provides a framework for analyzing various causal questions. In the specific case of climate change, we can hypothesize that a surge in discussions on a specific topic consistently precedes a change in overall sentiment, level of aggressiveness, or the proportion of users expressing certain stances. We can also conjecture that real-world events, like natural disasters and the rise to power of politicians leaning towards climate change denial, may have a noticeable impact on the discussion on social media. We illustrate how the proposed methodology can be applied to examine these questions by combining datasets on tweets and climate disasters.