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Citation |
Lin YR, Margolin D, Wen X. Risk Anal. 2017; 37(8): 1580-1605. |
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Affiliation |
School of Information Sciences, University of Pittsburgh, PA, USA. |
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Copyright |
(Copyright © 2017, Society for Risk Analysis, Publisher John Wiley and Sons) |
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DOI |
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PMID |
28556273 |
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Abstract |
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. Language: en |
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Keywords |
Big data; disaster response; emergency management; human behaviors; risk communication; social media; terrorism |