When do crowds turn violent? uncovering triggers from media

Yue Ning, Sathappan Muthiah, Naren Ramakrishnan, Huzefa Rangwala, David Mares

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Mass gatherings often underlie civil disobedience activities and as such run the risk of turning violent, causing damage to both property and people. While civil unrest is a rather common phenomenon, only a small subset of them involve crowds turning violent. How can we distinguish which events are likely to lead to violence? Using articles gathered from thousands of online news sources, we study a two-level multi-instance learning formulation, CrowdForecaster, tailored to forecast violent crowd behavior, specifically violent protests. Using data from five countries in Latin America, we demonstrate not just the predictive utility of our approach, but also its effectiveness in discovering triggering factors, especially in uncovering how and when crowd behavior begets violence.

Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
EditorsAndrea Tagarelli, Chandan Reddy, Ulrik Brandes
Pages77-82
Number of pages6
ISBN (Electronic)9781538660515
DOIs
StatePublished - 24 Oct 2018
Event10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain
Duration: 28 Aug 201831 Aug 2018

Publication series

NameProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018

Conference

Conference10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Country/TerritorySpain
CityBarcelona
Period28/08/1831/08/18

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