论文标题
社会疏远的信念和人类流动性：Twitter的证据
Social Distancing Beliefs and Human Mobility: Evidence from Twitter
论文作者
论文摘要
我们构建了一个新的数据库，其中包含数十万个与Twitter上的COVID-19大流行有关的地理标签消息。我们通过分析包含“待在家里”，“保持安全”，“戴口罩”，“洗手手”和“社交距离”等推文来捕捉社会疏远的每日社会疏远指数，以捕捉社会疏远的信念。我们发现，T-1的社会距离的Twitter指数的增加与第t天的流动性下降有关。我们还发现，州秩序，降水量和温度的增加，有助于降低人类流动性。共和党国家也不太可能执行社会疏远。在社交网络上分享的信念既可以揭示个人的行为，又可以影响他人的行为。我们的发现表明，政策制定者可以使用地理标记的Twitter数据（与移动性数据结合使用）来更好地了解个人自愿的社会疏远行动。
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 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.
