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

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.

Space-Time Dependence of Emotions on Twitter after a Natural Disaster

“…In the context of natural disasters, several studies have shown that geographically concentrated negative emotions could be identified before, during, and after a disaster with the help of Twitter data [ 14 , 24 , 34 , 44 ]. In our study, we focused on negative emotions and examined them individually and as a variable of all negative emotions in all time periods.…”

A city-wide examination of fine-grained human emotions through social media analysis

“…In such studies, sentiment analysis is generally carried out on geotagged tweets to provide an overview about the overall mood of the population at different regions or cities [ 10 ]. Other studies focused on identifying user sentiment during or after a particular event (such as a pandemic [ 11 , 12 ] or terrorism event [ 13 ]) or analyzed user sentiment at different geographical areas with the aim of investigating the mobility patterns of people within a city [ 14 ]. In a number of these studies, data about the sentiment of the tweets themselves were correlated with various metrics affecting urban living (such as job opportunities and access to public transportation) to provide insight into the effect these measures have on the happiness of the population [ 15 ].…”

“…In another example, geotagged tweets were classified based on emotional dimensions such as pleasantness and dominance, the results of which were visualized over the world map to show emotional trends across different countries [ 19 ]. Other studies used geotagged tweets to analyze the emotions of people (fear, sadness, sympathy) in response to particular events such as the London Westminster and London Bridge attacks [ 13 ] and the Paris attacks (anger and sadness) [ 20 ].…”

“…Overall, these results were mostly consistent with those reported in previous studies. For instance, prior studies found that during events of terrorism, there tends to be a spike in the display of emotions such as fear, sympathy and sadness on social media such as twitter based on the geographic proximity of the user to that event [42] or in space-time clusters [13]. A similar result was also discovered when analyzing tweets during the Paris and Brussels attacks, where emotions such as Anger, Sadness and Anxiety tending to spike on social media a couple of days after the event [20].…”

Crowd Detection in Mass Gatherings Based on Social Media Data: A Case Study of the 2014 Shanghai New Year’s Eve Stampede

“…A large quantity of social media data makes it possible to detect emergency events or the status of an assembled crowd. Leveraging social media big data for emergency response and disaster management has attracted considerable attention [ 16 , 17 , 18 ]. Social media data associated with temporal and geographical information can reveal human behavior patterns [ 19 , 20 , 21 ], and human emotion changes can be reflected by social media content [ 22 , 23 , 24 , 25 ].…”