解读公众情绪:基于微博数据的后疫情时代的时空动态

IF 2.5 3区 心理学 Q2 PSYCHOLOGY, MULTIDISCIPLINARY
Yi Liu, Xiaohan Yan, Tiezhong Liu, Yan Chen
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引用次数: 0

摘要

大流行后时代,长期接触公共卫生危机对全球精神卫生构成重大威胁。为了解决这个问题,我们开发了一个概念模型来分析公众情绪的时空分布,使用2022年北京酒吧爆发(6月9日至8月18日)的微博数据。该模型集成了基于词汇的情绪分析、空间自相关测试和内容分析,以全面了解不同阶段和地区的情绪反应。研究结果揭示了一个跨越紧急、传染和解决阶段的多峰情绪周期,在紧急区域、周边地区和受感染个体访问的地区具有显著的情绪聚集性。通过编码,我们确定了24个主要类别和90个子类别,提炼成9个核心主题,说明了影响因素、公众情绪和在线行为之间的相互作用。积极的公众情绪(如希望、感激、乐观)与流行病的改善和政策的执行有关,推动了支持预防措施和抵制错误信息等行为。负面情绪(如愤怒、焦虑、悲伤)源于疾病的严重爆发、控制不足和对自由的限制,导致批评和要求问责。这项研究将大数据分析与行为科学结合起来,为公众情绪和行为的演变提供了重要的见解。通过强调时空模式和情感动态,它为政府和卫生组织提供了可操作的指导,以设计有针对性的干预措施,培养复原力,并以精确和同情的方式更好地管理未来的社会危机。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding Public Emotions: Spatiotemporal Dynamics in the Post-Pandemic Era Through Weibo Data.

Prolonged exposure to public health crises in the post-pandemic era poses significant threats to global mental health. To address this, we developed a conceptual model to analyse the spatiotemporal distribution of public emotions, using Weibo data from the 2022 Beijing bar outbreak (9 June-18 August). The model integrates lexicon-based emotion analysis, spatial autocorrelation tests, and content analysis to provide a comprehensive understanding of emotional responses across stages and regions. The findings reveal a multi-peak emotional cycle spanning emergency, contagion, and resolution stages, with significant emotional clustering in emergency zones, surrounding areas, and regions visited by infected individuals. Through coding, we identified 24 main-categories and 90 sub-categories, distilled into nine core themes that illustrate the interplay between influencing factors, public emotions, and online behaviours. Positive public emotions (e.g., hopefulness, gratitude, optimism) were linked to pandemic improvements and policy implementation, driving behaviours such as supporting prevention measures and resisting misinformation. Negative emotions (e.g., anger, anxiety, sadness) stemmed from severe outbreaks, insufficient controls, and restrictions on freedoms, leading to criticism and calls for accountability. This study bridges big data analytics with behavioural science, offering critical insights into evolving public emotions and behaviours. By highlighting spatiotemporal patterns and emotional dynamics, it provides actionable guidance for governments and health organizations to design targeted interventions, foster resilience, and better manage future social crises with precision and empathy.

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来源期刊
Behavioral Sciences
Behavioral Sciences Social Sciences-Development
CiteScore
2.60
自引率
7.70%
发文量
429
审稿时长
11 weeks
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