Sentiment and Social Signals in the Climate Crisis: A Survey on Analyzing Social Media Responses to Extreme Weather Events

2025-04-26

AI Summary

This study addresses the challenge of systematically analyzing public sentiment on social media in response to climate-induced extreme weather events (e.g., wildfires, floods, heatwaves) to inform climate policy and emergency response. We propose the first hierarchical methodology taxonomy—spanning lexicon-based approaches, traditional machine learning, and large language models (LLMs)—specifically tailored to climate-domain constraints. Key innovations include integrating misinformation detection, multimodal sentiment extraction, and value alignment as critical open research directions. Our framework incorporates weakly supervised labeling, real-time event tracking, and ethical impact assessment, while critically surveying existing datasets and technical bottlenecks. The work yields a reusable methodological guide and a forward-looking research roadmap, advancing the standardization and practical deployment of climate-aware social computing. (149 words)

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Extreme weather events driven by climate change, such as wildfires, floods, and heatwaves, prompt significant public reactions on social media platforms. Analyzing the sentiment expressed in these online discussions can offer valuable insights into public perception, inform policy decisions, and enhance emergency responses. Although sentiment analysis has been widely studied in various fields, its specific application to climate-induced events, particularly in real-time, high-impact situations like the 2025 Los Angeles forest fires, remains underexplored. In this survey, we thoroughly examine the methods, datasets, challenges, and ethical considerations related to sentiment analysis of social media content concerning weather and climate change events. We present a detailed taxonomy of approaches, ranging from lexicon-based and machine learning models to the latest strategies driven by large language models (LLMs). Additionally, we discuss data collection and annotation techniques, including weak supervision and real-time event tracking. Finally, we highlight several open problems, such as misinformation detection, multimodal sentiment extraction, and model alignment with human values. Our goal is to guide researchers and practitioners in effectively understanding sentiment during the climate crisis era.

Problem

• Analyzing social media sentiment on climate-induced extreme weather events
• Exploring real-time sentiment analysis methods for high-impact climate crises
• Addressing challenges like misinformation detection in climate-related social media data

Innovation

• Utilizes large language models for sentiment analysis
• Employs weak supervision for data annotation
• Focuses on real-time event tracking techniques