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

Haiqi Zhang, Zhengyuan Zhu, Zeyu Zhang, Jacob Devasier, Chengkai Li

Abstract
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.
Anthology ID:
2024.climatenlp-1.18
Volume:
Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Dominik Stammbach, Jingwei Ni, Tobias Schimanski, Kalyan Dutia, Alok Singh, Julia Bingler, Christophe Christiaen, Neetu Kushwaha, Veruska Muccione, Saeid A. Vaghefi, Markus Leippold
Venues:
ClimateNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
233–240
Language:
URL:
https://aclanthology.org/2024.climatenlp-1.18
DOI:
Bibkey:
Cite (ACL):
Haiqi Zhang, Zhengyuan Zhu, Zeyu Zhang, Jacob Devasier, and Chengkai Li. 2024. Granular Analysis of Social Media Users’ Truthfulness Stances Toward Climate Change Factual Claims. In Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024), pages 233–240, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Granular Analysis of Social Media Users’ Truthfulness Stances Toward Climate Change Factual Claims (Zhang et al., ClimateNLP-WS 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.climatenlp-1.18.pdf
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