Yue Ning, Sathappan Muthiah, Naren Ramakrishnan, David Mares


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.

Yue Ning, Sathappan Muthiah, Naren Ramakrishnan, Huzefa Rangwala, David Mares: When do Crowds Turn Violent? Uncovering Triggers from Media. ASONAM 2018: 77-82


Naren Ramakrishnan

Yue Ning

Publication Details

Date of publication:
October 25, 2018
Advances in Social Network Analysis and Mining
Page number(s):