Granular Analysis of Social Media Users’ Truthfulness Stances Toward Climate Change Factual Claims (2024)

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.

## introduction climate change is one of the most pressing global challenges of our time, profoundly impacting the environment, economy, and society. amidst the urgency to address this global crisis, there is a large volume of discourse on climate change across social media platforms, reflecting growing public awareness and engagement. understanding and analyzing discourse on climate change is crucial for informing public policy, media strategies, and societal awareness. prior studies have explored various aspects of text analysis on climate change. @xcite constructed a taxonomy of climate contrarian claims to analyze climate change myths and associated factual claims. topic modeling performed on tweets by @xcite showed that discussions of climate change span various topics. stance detection @xcite and sentiment analysis @xcite have also been widely studied to understand people's beliefs and attitudes toward climate change. in our study, we streamline a framework that involves collecting factual claims, collecting their corresponding social media posts, constructing an automated taxonomy, and detecting truthfulness stances to understand public perceptions of climate change. specifically, we collect and analyze factual claims related to climate change and employ the large language model (llm) with human-inthe-loop to automatically construct a taxonomy of important, fact-checked claims. beyond the taxonomy, we gather discussions related to these factual claims on social media and perform truthfulness stance detection on these social media posts toward their corresponding factual claims in the taxonomy to examine people's judgments on various climate change-related topics. our work enhances the understanding of social media users' perceptions of climate change by: 1) providing a framework to understand people's judgments about climate change-related factual claims across different sub-categories of climate change; 2) yielding several significant insights into people's perceptions of climate change, including the observation that the public lacks discernment between facts and misinformation across different topics. additionally, our findings reveal that the public tends to believe claims are true, regardless of the actual claim veracity, aligning with the findings of previous research by @xcite . ## methodology in the framework, we first collect factual claims from five credible fact-checking websites using the keywords selected from the environmental protection agency (epa) topics (section 2.1). next, we gather corresponding social media posts using keywords extracted from the collected factual claims (section 2.2). we then leverage llm with humanin-the-loop to automatically construct a climate change-related taxonomy (section 2.3). finally, we fine-tune a truthfulness stance detection model to assess the truthfulness stances of social media posts toward their corresponding factual claims within the taxonomy (section 2.4). an overview of the framework is depicted in figure 1 . ## conclusion our framework provides an effective way to analyze public judgments across multi-level topics related to climate change, aiding in understanding people's perceptions of various climate change topics discussed in online discourse. the results reveal challenges in distinguishing truth from misinformation. more specifically, people tend to accept claims as true, regardless of their accuracy. this issue is particularly evident in discussions on politics, economy, and environment. the findings highlight the need for targeted interventions, such as improved critical thinking education and robust fact-checking, to enhance public discernment and the accuracy of information on social media.