Insights into Climate Change Narratives: Emotional Alignment and Engagement Analysis on TikTok

ACL ARR 2025 February Submission2564 Authors

Abstract: TikTok has emerged as a key platform for discussing polarizing topics, including climate change. Despite its growing influence, there is limited research exploring how content features shape emotional alignment between video creators and audience comments, as well as their impact on user engagement. Using a combination of pretrained and fine-tuned textual and visual models, we analyzed 7,110 TikTok videos related to climate change, focusing on content features such as semantic clustering of video transcriptions, visual elements, tonal shifts, and detected emotions. (1) Our findings reveal that positive emotions and videos featuring factual content or vivid environmental visuals exhibit stronger emotional alignment. Furthermore, emotional intensity and tonal coherence in video speech are significant predictors of higher engagement levels, offering new insights into the dynamics of climate change communication on social media. (2) Our preference learning analysis reveals that comment emotions play a dominant role in predicting video shareability, with both positive and negative emotional responses acting as key drivers of content diffusion. We conclude that user engagement—particularly emotional discourse in comments—significantly shapes climate change content shareability.

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: human behavior analysis; emotion detection and analysis; NLP tools for social analysis; quantitative analyses of news and/or social media

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 2564