Quantifying social media predictors of violence during the 6 January US Capitol insurrection using Granger causality

被引:0
|
作者
Li, Qinghua [1 ,2 ]
King, Brayden G. [1 ]
Uzzi, Brian [1 ,2 ,3 ]
机构
[1] Northwestern Univ, Kellogg Sch Management, Evanston, IL 60208 USA
[2] Northwestern Univ, Northwestern Univ Inst Complex Syst N, Evanston, IL 60208 USA
[3] Northwestern Univ, McCormick Sch Engn, Evanston, IL 60208 USA
关键词
collective behaviour; social media; Capitol violence; computational social science; COLLECTIVE ACTION; MOVEMENTS; PROTEST; ATTENTION; CYCLE;
D O I
10.1098/rsif.2024.0314
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Protests involving brute force are growing in number and are viewed as a likely source of increased collective violence in industrialized nations. Yet, our scientific understanding of how violent protests are related to a leader's social media communications during protests remains nascent. Here, we analyse new data from the 6 January 'march on the US Capitol' to quantify the links between leadership, social media and levels of violence. Using data on thousands of live footage videos, Trump's tweets and rally speech, other rally speeches and #StopTheSteal tweets, we apply Granger regression methods to analyse the links between former President Trump's tweets, #StopTheSteal tweets, rally speeches and the severity and duration of outbreaks of violence and weapons use during the riot. We find that Trump's tweets predict bursts in rioters' levels and duration of violence and weapons use. Trump's tweets also predict changes in the volume and sentiments of #StopTheSteal tweets, which in turn explain additional variance in levels of violence and weapons use over the course of the riot. Our findings reveal new patterns of behaviour that link an authority figure's online behaviour during a protest and the shift from peaceful protesting to violence.
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页数:12
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