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VIDEO DOI: https://doi.org/10.48448/58d6-d891

workshop paper

ACL 2024

August 16, 2024

Bangkok, Thailand

Granular Analysis of Social Media Users’ Truthfulness Stances Toward Climate Change Factual Claims

keywords:

truthfulness stance detection

llm

taxonomy construction

climate change

Climate change poses an urgent global problem that requires efficient data analysis mechanisms to provide insights into climate-related discussions on social media platforms. This paper presents a framework aimed at understanding social media users' perceptions of various climate change topics and uncovering the insights behind these perceptions. Our framework employs large language model to develop a taxonomy of factual claims related to climate change and build a classification model that detects the truthfulness stance of tweets toward the factual claims. The findings reveal two key conclusions: (1) The public tends to believe the claims are true, regardless of the actual claim veracity; (2) The public shows a lack of discernment between facts and misinformation across different topics, particularly in areas related to politics, economy, and environment.

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