本文基于Twitter和GDELT等互联网大数据，结合风险沟通、风险应对等风险治理因素，分析重大疫情下社会恐慌、焦虑和抑郁等社会情绪的演变机制。重大疫情下主要负面情绪包括恐慌、焦虑和抑郁等，这些负面情绪大规模爆发主要集中在第一波疫情初期，后期疫情反弹期间负面情绪波动幅度明显要小。本文分别从威胁感知与应对效能、社会压力与社会支持的分析框架解释恐慌和抑郁情绪的演变。世界各地负面情绪的变动既有相似性，也有多样性，这与世界各地抗疫模式的多样性以及文化特征因素密切相关。本研究的发现对于应急管理和社会心态引导具有重要参考意义。

Based on big data such as Twitter and GDELT, this paper analyzes the mechanisms of the evolution of social emotions such as social panic, anxiety and depression during a pandemic by combining risk communication, risk response and other risk management factors. The main negative emotions during a pandemic include panic, anxiety, and depression, etc. The large-scale outbreak of these negative emotions is mainly concentrated in the initial wave of the pandemic, and the fluctuation of negative emotions during the rebound of the later pandemic is significantly smaller. In this paper, we explain the evolution of panic and depression from the analytical frameworks of threat perception and coping efficacy, social stress and social support, respectively. The variability of negative emotions around the world is both similar and diverse, which is closely related to the diversity of pandemic resilience patterns around the world as well as cultural identity factors. The findings of this study have important implications for emergency management and social mentality guidance.

龚为纲、朱萌、陈浩

本文为国家社会科学基金项目“基于大数据的社会情绪风险与网络集群事件治理研究”（22BSH024）、教育部人文社会科学研究一般项目“基于大数据的西方主要社会思潮发展动态及其有效引导研究”（18YJC710016）的阶段性成果。

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