title: Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media.
abstract: Online social media activity can often be a precursor to disruptive events such as protests, strikes, and "occupy" movements. We have observed that such civil unrest can galvanize supporters through social networks and help recruit activists to their cause. Understanding the dynamics of social network cascades and extrapolating their future growth will enable an analyst to detect or forecast major societal events. Existing work has primarily used structural and temporal properties of cascades to predict their future behavior. But factors like societal pressure, alignment of individual interests with broader causes, and perception of expected benefits also affect protest participation in social media. Here we develop an analysis framework using a differential game theoretic approach to characterize the cost of participating in a cascade, and demonstrate how we can combine such cost features with classical properties to forecast the future behavior of cascades. Using data from Twitter, we illustrate the effectiveness of our models on the "Brazilian Spring" and Venezuelan protests that occurred in June 2013 and November 2013, respectively. We demonstrate how our framework captures both qualitative and quantitative aspects of how these uprisings manifest through the lens of tweet volume on Twitter social media.
authors.affiliation: Discovery Analytics Center, Virginia Tech, Arlington, VA, United States of America; Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, VA, United States of America.
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Goode
authors.affiliation: Discovery Analytics Center, Virginia Tech, Arlington, VA, United States of America; Dept. of Computer Science, Virginia Tech, Blacksburg, VA, United States of America.
authors.forename: Siddharth
authors.initials: S
authors.lastname: Krishnan
authors.affiliation: Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, VA, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Roan
authors.affiliation: Discovery Analytics Center, Virginia Tech, Arlington, VA, United States of America; Dept. of Computer Science, Virginia Tech, Blacksburg, VA, United States of America.
authors.forename: Naren
authors.initials: N
authors.lastname: Ramakrishnan
mesh_terms: D001938:Brazil; D006801:Humans; D020407:Internet; D008962:Models, Theoretical; D061108:Social Media; D014687:Venezuela