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Tracking and Analyzing Individual Distress Following Terrorist Attacks Using Social Media Streams

Lin, YR and Margolin, D and Wen, X (2017) Tracking and Analyzing Individual Distress Following Terrorist Attacks Using Social Media Streams. Risk Analysis, 37 (8). 1580 - 1605. ISSN 0272-4332

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Abstract

© 2017 Society for Risk Analysis Risk research has theorized a number of mechanisms that might trigger, prolong, or potentially alleviate individuals' distress following terrorist attacks. These mechanisms are difficult to examine in a single study, however, because the social conditions of terrorist attacks are difficult to simulate in laboratory experiments and appropriate preattack baselines are difficult to establish with surveys. To address this challenge, we propose the use of computational focus groups and a novel analysis framework to analyze a social media stream that archives user history and location. The approach uses time-stamped behavior to quantify an individual's preattack behavior after an attack has occurred, enabling the assessment of time-specific changes in the intensity and duration of an individual's distress, as well as the assessment of individual and social-level covariates. To exemplify the methodology, we collected over 18 million tweets from 15,509 users located in Paris on November 13, 2015, and measured the degree to which they expressed anxiety, anger, and sadness after the attacks. The analysis resulted in findings that would be difficult to observe through other methods, such as that news media exposure had competing, time-dependent effects on anxiety, and that gender dynamics are complicated by baseline behavior. Opportunities for integrating computational focus group analysis with traditional methods are discussed.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Lin, YRyurulin@pitt.eduYURULIN
Margolin, D
Wen, X
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
CorrespondentLin, Yu-Ruyurulin@pitt.eduYURULINUNSPECIFIED
Date: 1 August 2017
Date Type: Publication
Journal or Publication Title: Risk Analysis
Volume: 37
Number: 8
Page Range: 1580 - 1605
DOI or Unique Handle: 10.1111/risa.12829
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 0272-4332
Date Deposited: 26 Jun 2017 16:17
Last Modified: 18 Oct 2017 06:55
URI: http://d-scholarship.pitt.edu/id/eprint/32556

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