Xu Paiheng, Dredze Mark, Broniatowski David A
Malone Center for Engineering in Healthcare, Center for Language and Speech Processing, Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States.
Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC, United States.
J Med Internet Res. 2020 Dec 3;22(12):e21499. doi: 10.2196/21499.
Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and "flattens the curve" so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues.
The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week.
We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic.
We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic.
We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.
社交距离是应对新冠疫情的重要组成部分。尽量减少社交互动和出行可降低感染传播速度并“拉平曲线”，从而使医疗系统更有能力治疗感染者。然而，随着疫情持续，公众将如何应对这些政策仍不明确。
本研究的目的是展示推特社交流动性指数，这是一种从推特数据得出的社交距离和出行的衡量指标。我们使用公开的带有地理位置信息的推特数据来衡量用户在给定一周内的出行情况。
我们收集了2019年1月1日至2020年4月27日在美国带有地理标签的469,669,925条推文。我们分析了自新冠疫情开始以来，在城市和州层面上总共3,768,959名推特用户的汇总流动性差异。
我们发现，实施社交距离政策后，美国的出行量大幅下降（61.83%）。然而，各州之间的差异很大，从38.54%到76.80%不等。截至4月初尚未发布全州范围社交距离命令的八个州在出行减少方面排名靠后：阿肯色州（45）、爱荷华州（37）、内布拉斯加州（35）、北达科他州（22）、南卡罗来纳州（38）、南达科他州（46）、俄克拉何马州（50）、犹他州（14）和怀俄明州（53）。我们正在互联网上展示我们的研究结果，并将在疫情期间继续更新我们的分析。
我们观察到，较早采用社交距离政策的州出行减少幅度较大，而没有此类政策的州变化较小。这些结果在一定程度上也与基于其他流动性数据的结果一致。因此，带有地理位置信息的推文是利用公共资源跟踪社交距离做法的有效方式，这种跟踪作为正在进行的疫情应对规划的一部分可能会很有用。
