探索社交媒体内容中的气候相关焦虑
Exploring Climate-Related Anxiety Through Social Media Content
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中文总结 AI 辅助
本研究利用Reddit社交媒体数据，通过BERTopic主题建模和RoBERTa-base GoEmotions情感分类，揭示气候话语的核心主题及负面情绪主导、评论反应多样化的模式，为理解气候焦虑提供初步框架。
中文摘要 AI 辅助
本研究探讨了通过Reddit上的社交媒体讨论所表达的气候相关焦虑。利用自然语言处理技术，我们分析了大规模文本数据，以识别反复出现的主题、情感模式及其随时间的变化。文本数据经过预处理后，使用BERTopic进行主题建模，并使用基于Transformer的模型（RoBERTa-base GoEmotions模型）进行28个类别的情感分类。结果表明，气候相关话语围绕少数核心主题展开，主要将倡导和政策等行动导向的讨论与信息性和反思性内容区分开来。情感分析显示，负面情绪如恐惧和悲伤在帖子中更为突出，而评论往往引入更广泛的反应，包括关心、鼓励和中性反应。这些发现表明，在线气候话语不仅受所讨论主题的影响，还受用户如何相互互动和回应的影响。这项工作为通过大规模社交媒体分析理解气候相关焦虑提供了一个初步框架，并强调了改进未来模型、扩大平台覆盖范围以及纳入以青年为中心的视角的机会。
英文摘要
This study explores climate-related anxiety as expressed through social media discussions on Reddit. Using natural language processing techniques, we analyse large-scale textual data to identify recurring themes, emotional patterns, and how these evolve over time. Text data was preprocessed and analysed using BERTopic for topic modelling and a transformer-based model for emotion classification across 28 categories using the RoBERTa-base GoEmotions model. Results show that climate-related discourse is structured around a small number of core themes, primarily separating action-oriented discussions like advocacy and policy from informational and reflective content. Emotional analysis reveals that negative emotions such as fear and sadness are more prominent in posts, while comments often introduce a wider range of responses, including care, encouragement, and neutral reactions. These findings suggest that online climate discourse is shaped not only by the topics being discussed, but also by how users engage with and respond to one another. This work provides an initial framework for understanding climate-related anxiety through large-scale social media analysis and highlights opportunities for improving future models, expanding platform coverage, and incorporating youth-centred perspectives.
发表机构
- Sprout Climate Association（Sprout气候协会）
机构由 AI 辅助整理，请以论文原文为准。
