Harnessing AI for Climate Change Communication: Analyzing Public Perception through NLP and Machine Learning

Authors

  • Gohar Rahman Islamia College Peshawar
  • Aidatul Fitriyah Universitas Airlangga

DOI:

https://doi.org/10.61194/ijcs.v3i2.683

Keywords:

Sentiment Analysis, Climate Change Communication, Social Media Discourse, Framing Theory, Public Perception

Abstract

This study explores how Artificial Intelligence (AI), particularly Natural Language Processing (NLP) and machine learning, can be leveraged to analyze public discourse on climate change and improve climate communication strategies. Focusing on four key questions, the research examines how AI classifies public sentiment, identifies dominant topics, detects framing structures, and generates actionable insights to inform targeted communication. Over one million climate-related posts were collected from Twitter and Facebook between January and June 2024. Sentiment analysis using a fine-tuned BERT model categorized posts into positive, negative, and neutral sentiments, while Latent Dirichlet Allocation (LDA) revealed key topics. Framing analysis employed supervised machine learning to classify posts into narrative frames, and AI-powered visualization tools were used for interpretation. The results indicate a polarized sentiment distribution: 45% negative, 35% positive, and 20% neutral. Negative posts centered on skepticism and political inaction, while positive posts supported renewable energy and activism. Thematic analysis highlighted five key topics: scientific evidence, activism, economic implications, political debate, and environmental justice. Framing analysis revealed four dominant frames—climate urgency, economic impact, political action, and environmental justice—that shape public perception. Temporal sentiment shifts aligned with major events, suggesting public discourse is responsive to political and activist developments. This research demonstrates the potential of AI to provide scalable, data-driven insights into public climate discourse. By integrating these insights into strategic planning, communicators can design more effective, emotionally resonant messages, enhancing public engagement and supporting collective climate action.

References

Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment Analysis of Twitter Data. In M. Nagarajan & M. Gamon (Eds.), Proceedings of the Workshop on Language in Social Media (LSM 2011) (pp. 30–38). Association for Computational Linguistics. https://aclanthology.org/W11-0705/

Blei, D. M., Ng, A. Y., & Lafferty, J. D. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

Bord, R. J., O’Connor, R. E., & Fisher, A. (2000). In What Sense Does the Public See Climate Change as a Risk? Risk Analysis, 20(3), 359–368.

Boulianne, S. (2015). Social Media Use and Participation: A Meta-Analysis of Current Research. Information, Communication & Society, 18(5), 524–538. DOI: https://doi.org/10.1080/1369118X.2015.1008542

Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. DOI: https://doi.org/10.1109/MIS.2013.30

Chen, F.-H. (2021). Sustainable Education through E-Learning: The Case Study of iLearn2.0. Sustainability, 13(18), 10186. https://doi.org/10.3390/su131810186 DOI: https://doi.org/10.3390/su131810186

Chong, D., Druckman, J. N., & Linn, S. (2020). Public Opinion on Climate Change and Its Communication: Analyzing the Influence of Media and Partisan Framing. Environmental Communication, 14(2), 231–250.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT, 4171–4186.

Entman, R. M. (1993). Framing: Toward Clarification of a Fractured Paradigm. Journal of Communication, 43(4), 51–58. DOI: https://doi.org/10.1111/j.1460-2466.1993.tb01304.x

Fitriyah, A., & Abdulovna, D. D. (2024). EU’s AI Regulation Approaches and Their Implication for Human Rights. Media Iuris, 7(3), 417–438. https://doi.org/10.20473/mi.v7i3.62050 DOI: https://doi.org/10.20473/mi.v7i3.62050

Ge, Z., Song, Z., Ding, S. X., & Huang, B. (n.d.). Data mining and analytics in the process industry: The role of machine learning. Ieee Access, 5, 20590–20616. DOI: https://doi.org/10.1109/ACCESS.2017.2756872

Gottfried, J., & Shearer, E. (2020). News Use Across Social Media Platforms 2020. Pew Research Center. https://www.pewresearch.org.

Jang, S. M., & Hart, P. S. (2015). Polarized frames on “climate change” and “global warming” across countries and states: Evidence from Twitter big data. Global Environmental Change, 32, 11–17. https://doi.org/10.1016/j.gloenvcha.2015.02.010 DOI: https://doi.org/10.1016/j.gloenvcha.2015.02.010

Kovacs, K. (2020). Machine Learning for Climate Change Communication: The Promise and Potential. Journal of Environmental Communication, 34(1), 57–72.

Lakoff, G. (2010). The Political Mind: A Cognitive Scientist’s Guide to Your Brain and Its Politics. Penguin Books.

Lewandowsky, S., Ecker, U. K. H., & Cook, J. (2017). Beyond Misinformation: Understanding and Coping with the “Post-Truth” Era. Journal of Applied Research in Memory and Cognition, 6(4), 371–380. DOI: https://doi.org/10.1016/j.jarmac.2017.07.008

Moser, S. C. (2010). Communicating Climate Change: History, Challenges, and Opportunities. Wiley Interdisciplinary Reviews: Climate Change, 1(1), 31–53. DOI: https://doi.org/10.1002/wcc.11

Nisbet, M. C. (2009a). Communicating Climate Change: Why Frames Matter for Public Engagement. Environment: Science and Policy for Sustainable Development, 51(2), 12–23. DOI: https://doi.org/10.3200/ENVT.51.2.12-23

Nisbet, M. C. (2009b). Framing Science: A New Paradigm in Public Engagement. Bulletin of Science, Technology & Society, 29(1), 18–28.

Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. DOI: https://doi.org/10.1561/1500000011

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Published

2025-04-22

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