@inproceedings{zhang-etal-2024-granular,
title = "Granular Analysis of Social Media Users{'} Truthfulness Stances Toward Climate Change Factual Claims",
author = "Zhang, Haiqi and
Zhu, Zhengyuan and
Zhang, Zeyu and
Devasier, Jacob and
Li, Chengkai",
editor = "Stammbach, Dominik and
Ni, Jingwei and
Schimanski, Tobias and
Dutia, Kalyan and
Singh, Alok and
Bingler, Julia and
Christiaen, Christophe and
Kushwaha, Neetu and
Muccione, Veruska and
A. Vaghefi, Saeid and
Leippold, Markus",
booktitle = "Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.climatenlp-1.18",
doi = "10.18653/v1/2024.climatenlp-1.18",
pages = "233--240",
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.",
}
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%0 Conference Proceedings
%T Granular Analysis of Social Media Users’ Truthfulness Stances Toward Climate Change Factual Claims
%A Zhang, Haiqi
%A Zhu, Zhengyuan
%A Zhang, Zeyu
%A Devasier, Jacob
%A Li, Chengkai
%Y Stammbach, Dominik
%Y Ni, Jingwei
%Y Schimanski, Tobias
%Y Dutia, Kalyan
%Y Singh, Alok
%Y Bingler, Julia
%Y Christiaen, Christophe
%Y Kushwaha, Neetu
%Y Muccione, Veruska
%Y A. Vaghefi, Saeid
%Y Leippold, Markus
%S Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhang-etal-2024-granular
%X 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.
%R 10.18653/v1/2024.climatenlp-1.18
%U https://aclanthology.org/2024.climatenlp-1.18
%U https://doi.org/10.18653/v1/2024.climatenlp-1.18
%P 233-240
Markdown (Informal)
[Granular Analysis of Social Media Users’ Truthfulness Stances Toward Climate Change Factual Claims](https://aclanthology.org/2024.climatenlp-1.18) (Zhang et al., ClimateNLP-WS 2024)
ACL