IAE Paris - Université Paris 1 Panthéon-Sorbonne, Paris, France.
CES Sorbonne - Université Paris 1 Panthéon-Sorbonne, Paris, France.
PLoS One. 2021 Mar 3;16(3):e0246949. doi: 10.1371/journal.pone.0246949. eCollection 2021.
We construct a novel database containing hundreds of thousands geotagged messages related to the COVID-19 pandemic sent on Twitter. We create a daily index of social distancing-at the state level-to capture social distancing beliefs by analyzing the number of tweets containing keywords such as "stay home", "stay safe", "wear mask", "wash hands" and "social distancing". We find that an increase in the Twitter index of social distancing on day t-1 is associated with a decrease in mobility on day t. We also find that state orders, an increase in the number of COVID-19 cases, precipitation and temperature contribute to reducing human mobility. Republican states are also less likely to enforce social distancing. Beliefs shared on social networks could both reveal the behavior of individuals and influence the behavior of others. Our findings suggest that policy makers can use geotagged Twitter data-in conjunction with mobility data-to better understand individual voluntary social distancing actions.
我们构建了一个包含数十万条与 COVID-19 大流行相关的地理标记消息的新型数据库，这些消息是在 Twitter 上发布的。我们通过分析包含“stay home”、“stay safe”、“wear mask”、“wash hands”和“social distancing”等关键词的推文数量，创建了一个每日社交隔离指数，以捕捉社交隔离信念。我们发现，t-1 日 Twitter 社交隔离指数的增加与 t 日流动性的下降有关。我们还发现，州政府命令、COVID-19 病例数的增加、降水和温度有助于减少人类流动性。共和党州也不太可能执行社交隔离措施。社交网络上分享的信念既可以揭示个人的行为，也可以影响他人的行为。我们的研究结果表明，政策制定者可以使用地理标记的 Twitter 数据——结合流动性数据——来更好地了解个人自愿的社交隔离行为。
