Successful navigation of the Covid-19 pandemic is predicated on public cooperation with safety measures and appropriate perception of risk, in which emotion and attention play important roles. Signatures of public emotion and attention are present in social media data, thus natural language analysis of this text enables near-to-real-time monitoring of indicators of public risk perception. We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from \(\sim 20\) million unique Covid-19-related tweets from 12 countries posted between 10th March and 14th June 2020. We find evidence of psychophysical numbing: Twitter users increasingly fixate on mortality, but in a decreasingly emotional and increasingly analytic tone. Semantic network analysis based on word co-occurrences reveals changes in the emotional framing of Covid-19 casualties that are consistent with this hypothesis. We also find that the average attention afforded to national Covid-19 mortality rates is modelled accurately with the Weber–Fechner and power law functions of sensory perception. Our parameter estimates for these models are consistent with estimates from psychological experiments, and indicate that users in this dataset exhibit differential sensitivity by country to the national Covid-19 death rates. Our work illustrates the potential utility of social media for monitoring public risk perception and guiding public communication during crisis scenarios.

成功应对 Covid-19 大流行取决于公众对安全措施的合作以及对风险的适当认知，其中情绪和注意力发挥着重要作用。社交媒体数据中存在公众情绪和注意力的特征，因此对文本的自然语言分析可以近乎实时地监测公众风险感知指标。我们将大流行病进展的关键流行病学指标与公众对大流行病的看法指标进行了比较，这些指标是根据 2020 年 3 月 10 日至 6 月 14 日期间发布的来自 12 个国家的\(\sim 20\)百万条独特的 Covid-19 相关推文构建的。我们找到心理物理学麻木的证据：推特用户越来越关注死亡率，但情绪化程度越来越低，分析性语气越来越强。基于单词共现的语义网络分析揭示了 Covid-19 伤亡者情绪框架的变化，这与这一假设是一致的。我们还发现，对全国 Covid-19 死亡率的平均关注度是通过感官知觉的韦伯-费希纳函数和幂律函数准确建模的。我们对这些模型的参数估计与心理实验的估计一致，并表明该数据集中的用户对国家 Covid-19 死亡率表现出不同的敏感性。我们的工作说明了社交媒体在危机情况下监测公众风险认知和指导公众沟通的潜在效用。