Using Big Data to Study the Impact of Mass Violence: Opportunities for the Traumatic Stress Field

We describe challenges and benefits of working with Twitter data and briefly review studies that used Twitter data to explore community responses to mass violence. We then demonstrate the use of Twitter data to examine community responses to a specif ic event: the 2015 San Bernardino terrorist attack, in which 14 people were killed and 22 were wounded. In a 6‐week time frame around this attack, we evaluated the time course of community‐level negative emotion. We downloaded 1.16 million tweets, representing 25,894 users from San Bernardino, C A, and a matched control community, Stockton, CA. All tweets were coded in R using the Linguistic Inquiry and Word Count (LIWC) negative emotion dictionary. A piecewise regression technique with a discontinuity analysis was used to evaluate pre‐ and postevent trajectories of negative emotion acros s the study window. Controlling for within‐user variability, negative emotion increased by 6.2%, β = .182,SE = .014,p< .001, in San Bernardino on the day of the attack and remained elevated for 5 days; no elevation was observed in Stockton. We discuss how data ‐driven text analytic techniques are useful for exploring Twitter content generated after collective traumas and describe challenges and opportunities accompanying analyses of social media data to understand the impact of mass violence on affected populations.

Source: Journal of Traumatic Stress - October 7, 2019 Category: Psychiatry & Psychology Authors: Nickolas M. Jones, Melissa Brymer, Roxane Cohen Silver Tags: Research Article Source Type: research