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AI-Driven Social Media Analytics for Assessing Climate Change Perceptions and Supporting Adaptation and Sustainability Policies

気候変動認識の評価と適応・持続可能性政策支援のためのAI駆動型ソーシャルメディア分析 (AI 翻訳)

Mehmet Kayakuş, Önder Kabaş, Georgiana Moiceanu

Sustainability📚 査読済 / ジャーナル2026-05-13#政策Origin: Global
DOI: 10.3390/su18104859
原典: https://doi.org/10.3390/su18104859

🤖 gxceed AI 要約

日本語

AIを用いてX（旧Twitter）上の気候変動に関する投稿約3万件を分析。感情分析では否定的感情が優勢で、懸念やリスク認識が高い一方、肯定的内容は解決策や行動を強調。トピックモデリングにより、気象経験に基づく懐疑論、科学的・政策的議論、炭素排出と人間の影響という3テーマを特定。ソーシャルメディアが適応政策のためのリスク認識や誤情報把握に有用と結論。

English

This study analyzes 29,576 climate change posts from X (December 2025) using AI text mining, sentiment analysis, and topic modeling. Negative sentiment dominates, indicating high concern and urgency. Three main topics emerge: skepticism tied to weather, scientific/policy debates, and carbon emissions. Results show social media can inform adaptation policy by revealing public risk perception and misinformation patterns.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本のGX政策において、国民の気候変動認識をリアルタイムで把握する手法として参考になる。特に適応策やコミュニケーション戦略立案にAI分析を活用する示唆を提供する。

In the global GX context

Globally, this paper demonstrates a scalable AI-driven method for tracking public climate perceptions and misinformation, which can support evidence-based adaptation policies and climate communication strategies under frameworks like the Paris Agreement.

👥 読者別の含意

🔬研究者:Methodology for combining NLP and LDA to analyze climate discourse from social media data.

🏢実務担当者:Insights into public sentiment and misinformation patterns that can inform climate communication and risk management.

🏛政策担当者:Evidence that social media analytics can monitor public risk perception and support adaptation policy design.

📄 Abstract（原文）

This study examines public perceptions and discourse on climate change using artificial intelligence (AI)-based analysis of social media data, with implications for climate adaptation and sustainability policy. A dataset of 29,576 posts from the X platform (December 2025) was analyzed through an integrated framework combining text mining, TF-IDF-based word analysis, deep learning-based sentiment analysis, and Latent Dirichlet Allocation (LDA) topic modelling. The findings reveal that climate change discourse is predominantly characterized by negative sentiment, reflecting high levels of concern, perceived risk, and urgency, while positive content emphasizes awareness, solutions, and collective action. Topic modelling identifies three main themes: skepticism shaped by daily weather experiences, scientific and policy-oriented climate debates, and discussions on carbon emissions and human impact. These results demonstrate that social media serves not only as a space for emotional expression but also as a dynamic platform for information exchange and public opinion formation. From an adaptation perspective, AI-driven social media analytics provide valuable insights into public risk perception, misinformation patterns, and knowledge gaps, supporting evidence-based climate communication and policy development.

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