@inproceedings{gao-etal-2025-insights,
title = "Insights into Climate Change Narratives: Emotional Alignment and Engagement Analysis on {T}ik{T}ok",
author = "Gao, Ge and
Shan, Zhengyang and
Crissman, James and
Novozhilova, Ekaterina and
Huang, YuCheng and
Ramanathan, Arti and
Betke, Margrit and
Wijaya, Derry",
editor = "Atwell, Katherine and
Biester, Laura and
Borah, Angana and
Dementieva, Daryna and
Ignat, Oana and
Kotonya, Neema and
Liu, Ziyi and
Wan, Ruyuan and
Wilson, Steven and
Zhao, Jieyu",
booktitle = "Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlp4pi-1.11/",
doi = "10.18653/v1/2025.nlp4pi-1.11",
pages = "128--143",
ISBN = "978-1-959429-19-7",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Insights into Climate Change Narratives: Emotional Alignment and Engagement Analysis on TikTok
%A Gao, Ge
%A Shan, Zhengyang
%A Crissman, James
%A Novozhilova, Ekaterina
%A Huang, YuCheng
%A Ramanathan, Arti
%A Betke, Margrit
%A Wijaya, Derry
%Y Atwell, Katherine
%Y Biester, Laura
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Ignat, Oana
%Y Kotonya, Neema
%Y Liu, Ziyi
%Y Wan, Ruyuan
%Y Wilson, Steven
%Y Zhao, Jieyu
%S Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 978-1-959429-19-7
%F gao-etal-2025-insights
%X 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.
%R 10.18653/v1/2025.nlp4pi-1.11
%U https://aclanthology.org/2025.nlp4pi-1.11/
%U https://doi.org/10.18653/v1/2025.nlp4pi-1.11
%P 128-143
Markdown (Informal)
[Insights into Climate Change Narratives: Emotional Alignment and Engagement Analysis on TikTok](https://aclanthology.org/2025.nlp4pi-1.11/) (Gao et al., NLP4PI 2025)
ACL
- Ge Gao, Zhengyang Shan, James Crissman, Ekaterina Novozhilova, YuCheng Huang, Arti Ramanathan, Margrit Betke, and Derry Wijaya. 2025. Insights into Climate Change Narratives: Emotional Alignment and Engagement Analysis on TikTok. In Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI), pages 128–143, Vienna, Austria. Association for Computational Linguistics.