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伦敦连续恐怖袭击后负面情绪的时空监测。

Space-Time Surveillance of Negative Emotions After Consecutive Terrorist Attacks in London.

机构信息

Department of Geosciences, Georgia State University, Atlanta, GA 30303, USA.

Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA.

出版信息

Int J Environ Res Public Health. 2020 Jun 4;17(11):4000. doi: 10.3390/ijerph17114000.

Abstract

Terrorist attacks pose significant threats to mental health. There is dearth information about the impact of consecutive terrorist attacks on space-time concentrations of emotional reactions. This study collected (1) Twitter data following the two terrorist attacks in London in March and June of 2017, respectively, and (2) deprivation data at small areal levels in the United Kingdom. The space-time permutation model was used to detect the significant clusters of negative emotions, including fear, sadness, and anger in tweets. Logistic regression models were used to examine the social deprivation of communities associated with negative tweeting. The results reported two significant clusters after the March attack, one was in London, ten days after the attack, and the other was far from the attack site between Manchester and Birmingham, three days after the attack. Attention to the reoccurring attack in June diminished quickly. The socially deprived communities experienced double disadvantage-sending fewer tweets but expressing more negative emotions than their counterparts. The findings suggest that terrorism can affect public emotions far and broad. There is a potential for surveillance to rapidly identify geographically concentrated emotions after consecutive or prolonged disasters using social media data.

摘要

恐怖袭击对心理健康构成重大威胁。关于连续恐怖袭击对情绪反应时空集中的影响,信息匮乏。本研究收集了(1)2017 年 3 月和 6 月伦敦两次恐怖袭击后的 Twitter 数据,以及(2)英国小面积水平的剥夺数据。时空置换模型用于检测推文中恐惧、悲伤和愤怒等负面情绪的显著集群。逻辑回归模型用于检验与负面推文相关的社区的社会剥夺情况。结果报告了 3 月袭击后的两个显著集群,一个在伦敦,袭击发生后 10 天,另一个在曼彻斯特和伯明翰之间远离袭击地点,袭击发生后 3 天。对 6 月再次袭击的关注度迅速下降。社会剥夺社区遭受了双重劣势——发送的推文较少,但表达的负面情绪比其对应社区更多。研究结果表明,恐怖主义可以在很远和很广的范围内影响公众情绪。使用社交媒体数据,对连续或长期灾害后的地理集中情绪进行快速监测,具有潜在可能性。

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