Twitter reveals human mobility dynamics during the COVID-19 pandemic.
Twitter reveals human mobility dynamics during the COVID-19 pandemic.
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DOI：
10.1371/journal.pone.0241957
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发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Porter D
中科院分区：
文献类型：
--
作者：
Huang X;Li Z;Jiang Y;Li X;Porter D
当前COVID-19大流行引起全球关注，导致严重的健康、经济和社会挑战。该病毒在全球范围内的迅速传播突出表明，需要一种更加协调、较少涉及隐私、易于获得的方法来监测已被证明与病毒传播有关的人员流动。在这项研究中，我们分析了全球超过5.8亿条推文，以了解如何从全球、国家和美国各州的用户生成信息中反映出减少人类流动性的全球合作努力。考虑到移动性的多面性，我们提出了两种类型的距离：单日距离和跨日距离。为了量化某些地理区域的响应性，我们进一步提出了一个基于移动性的响应指数（MRI），它可以在一个时间窗口内捕获移动性变化的总体程度。结果表明，从Twitter数据获得的流动性模式是经得起定量反映的流动性动态。在全球范围内，2020年3月11日世卫组织宣布COVID-19为大流行之后，拟议的两个距离大大偏离了其基线。宣布后的时间间隔大大减少，表明保护措施显然影响了人们的旅行习惯。国家规模的比较显示了反应的差异，不同流行病阶段的流动模式对比就是证明。我们发现，流动性变化的触发因素与国家宣布的缓解措施相对应，证明基于Twitter的流动性意味着这些措施的有效性。在美国，COVID-19疫情对流动性的影响是明显的。然而，各州的影响差异很大。
The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.
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DOI：
10.1109/tgrs.2018.2835306
发表时间：
2018-08-01
影响因子：
8.2
作者：
Huang, Xiao;Wang, Cuizhen;Li, Zhenlong
通讯作者：
Li, Zhenlong
影响因子：
6.4
作者：
Dredze, Mark
通讯作者：
Dredze, Mark
影响因子：
3.6
作者：
Beiro, Mariano G.;Panisson, Andre;Cattuto, Ciro
通讯作者：
Cattuto, Ciro
影响因子：
56.9
作者：
Chinazzi, Matteo;Davis, Jessica T.;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
4.6
作者：
Bengtsson L;Gaudart J;Lu X;Moore S;Wetter E;Sallah K;Rebaudet S;Piarroux R
通讯作者：
Piarroux R