Coupling sentiment and human mobility in natural disasters: a Twitter-based study of the 2014 South Napa Earthquake
Coupling sentiment and human mobility in natural disasters: a Twitter-based study of the 2014 South Napa Earthquake
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DOI：
10.1007/s11069-018-3231-1
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发表时间：
2018-06-01
期刊：
影响因子：
3.7
通讯作者：
Taylor, John E.
中科院分区：
文献类型：
--
作者：
Wang, Yan;Taylor, John E.
了解自然灾害期间的人口动态对于建设城市抵御极端事件的能力十分重要。社交媒体已成为灾害管理人员的一个重要来源，可以帮助他们确定灾害过程中情绪的动态极性，了解人员流动模式，并加强决策和灾后恢复工作。虽然有越来越多的文献中的情绪和灾害背景下的人的流动性，情绪的时空特征和情绪和流动性之间的关系随着时间的推移没有得到详细的研究。因此，本研究解决了这一研究空白，并提出了一个新的透镜，以评估人口动态在灾害耦合的情绪和流动性。我们在8周内收集了374万条带有地理标记的推文，以检查2014年加州南纳帕M6.0地震之前、期间和之后个人的情绪和流动性。研究结果表明，随着地震强度的增加，平均情绪水平下降。我们发现，类似的情绪水平往往在地理空间上聚集，这种空间自相关性在不同地震强度的地区是显着的。此外，我们研究了情绪和流动性的时间动态之间的关系。我们研究了时间序列的趋势和季节性，发现序列之间的协整。我们包括地震的影响，并建立了一个分段回归模型来描述时间序列，发现情绪的日常变化可以导致或滞后于每日变化的流动模式。本研究为评价大尺度上灾害恢复力的动态过程提供了一个新的透镜。
Understanding population dynamics during natural disasters is important to build urban resilience in preparation for extreme events. Social media has emerged as an important source for disaster managers to identify dynamic polarity of sentiments over the course of disasters, to understand human mobility patterns, and to enhance decision making and disaster recovery efforts. Although there is a growing body of literature on sentiment and human mobility in disaster contexts, the spatiotemporal characteristics of sentiment and the relationship between sentiment and mobility over time have not been investigated in detail. This study therefore addresses this research gap and proposes a new lens to evaluate population dynamics during disasters by coupling sentiment and mobility. We collected 3.74 million geotagged tweets over 8 weeks to examine individuals' sentiment and mobility before, during and after the M6.0 South Napa, California Earthquake in 2014. Our research results reveal that the average sentiment level decreases with the increasing intensity of the earthquake. We found that similar levels of sentiment tended to cluster in geographical space, and this spatial autocorrelation was significant over areas of different earthquake intensities. Moreover, we investigated the relationship between temporal dynamics of sentiment and mobility. We examined the trend and seasonality of the time series and found cointegration between the series. We included effects of the earthquake and built a segmented regression model to describe the time series finding that day-to-day changes in sentiment can either lead or lag daily changed mobility patterns. This study contributes a new lens to assess the dynamic process of disaster resilience unfolding over large spatial scales.