Pilati Federico, Gallotti Riccardo, Sacco Pier Luigi
Università IULM, Milan, Italy.
Bruno Kessler Foundation (FBK), Trento, Italy.
Front Sociol. 2023 Jan 17;7:1093354. doi: 10.3389/fsoc.2022.1093354. eCollection 2022.
In this brief report we followed the evolution of the COVID-19 Infodemic Risk Index during 2020 and clarified its connection with the epidemic waves, focusing specifically on their co-evolution in Europe, South America, and South-eastern Asia. Using 640 million tweets collected by the Infodemic Observatory and the open access dataset published by Our World in Data regarding COVID-19 worldwide reported cases, we analyze the COVID-19 infodemic vs. pandemic co-evolution from January 2020 to December 2020. We find that a characteristic pattern emerges at the global scale: a decrease in misinformation on Twitter as the number of COVID-19 confirmed cases increases. Similar local variations highlight how this pattern could be influenced both by the strong content moderation policy enforced by Twitter after the first pandemic wave and by the phenomenon of selective exposure that drives users to pick the most visible and reliable news sources available.
在本简要报告中,我们追踪了2020年期间新冠疫情信息疫情风险指数的演变,并阐明了其与疫情浪潮的联系,特别关注了其在欧洲、南美洲和东南亚的共同演变。利用信息疫情观察站收集的6.4亿条推文以及“我们的数据世界”发布的关于全球新冠疫情报告病例的开放获取数据集,我们分析了2020年1月至2020年12月期间新冠疫情信息疫情与大流行的共同演变。我们发现,在全球范围内出现了一种特征模式:随着新冠确诊病例数的增加,推特上的错误信息减少。类似的局部变化突出了这种模式如何既受到推特在第一波疫情后实施的严格内容审核政策的影响,也受到促使用户选择最显眼和可靠新闻来源的选择性曝光现象的影响。