Journal article
Whose Lives Matter? Mass Shootings and Social Media Discourses of Sympathy and Policy, 2012-2014
Journal of computer-mediated communication, Vol.24(4), pp.182-202
07/01/2019
DOI: 10.1093/jcmc/zmz009
Abstract
This study focuses on the outpouring of sympathy in response to mass shootings and the contestation over gun policy on Twitter from 2012 to 2014 and relates these discourses to features of mass shooting events. We use two approaches to Twitter text analysis-hashtag grouping and supervised machine learning (ML)-to triangulate an understanding of intensity and duration of thoughts and prayers, gun control, and gun rights discourses. We conduct parallel time series analyses to predict their temporal patterns in response to features of mass shootings. Our analyses reveal that while the total number of victims and child deaths consistently predicted public grieving and calls for gun control, public shootings consistently predicted the defense of gun rights. Further, the race of victims and perpetrators affected the levels of public mourning and policy debates, with the loss of black lives and the violence inflicted by white shooters generating less sympathy and policy discourses.
Details
- Title: Subtitle
- Whose Lives Matter? Mass Shootings and Social Media Discourses of Sympathy and Policy, 2012-2014
- Creators
- Yini Zhang - University of Wisconsin–MadisonDhavan Shah - University of Wisconsin–MadisonJordan Foley - University of Wisconsin–MadisonAman Abhishek - University of Wisconsin–MadisonJosephine Lukito - University of Wisconsin–MadisonJiyoun Suk - University of Wisconsin–MadisonSang Jung Kim - Univ Wisconsin, Sch Journalism & Mass Commun, Madison, WI 53706 USAZhongkai Sun - Univ Wisconsin, Dept Elect & Comp Engn, Madison, WI 53706 USAJon Pevehouse - University of Wisconsin–MadisonChristine Garlough - University of Wisconsin–Madison
- Resource Type
- Journal article
- Publication Details
- Journal of computer-mediated communication, Vol.24(4), pp.182-202
- Publisher
- Oxford Univ Press
- DOI
- 10.1093/jcmc/zmz009
- ISSN
- 1083-6101
- eISSN
- 1083-6101
- Number of pages
- 21
- Grant note
- NRF-2016S1A3A2925033 / National Research Foundation of Korea Grant - Korean Government; National Research Foundation of Korea
- Language
- English
- Date published
- 07/01/2019
- Academic Unit
- School of Journalism and Mass Communication; Center for Social Science Innovation
- Record Identifier
- 9984459637202771
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