2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) On Predicting Social Unrest Using Social Media Rostyslav Korolov∗, Di Luy, Jingjing Wangz, Guangyu Zhouz, Claire Bonialx, Clare Vossx, Lance Kaplanx, William Wallace∗, Jiawei Hanz and Heng Jiy ∗Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute fkorolr,
[email protected] yDepartment of Computer Science Rensselaer Polytechnic Institute flud2,
[email protected] zDepartment of Computer Science, University of Illinois at Urbana-Champaign fjwang112, gzhou6,
[email protected] xU.S. Army Research Laboratory fclaire.n.bonial.civ, clare.r.voss.civ,
[email protected] Abstract—We study the possibility of predicting a social This paper provides a more detailed review of related protest (planned, or unplanned) based on social media messaging. research in both social science and data analysis followed by a We consider the process called mobilization, described in the discussion of the theoretical foundations of our methodology literature as the precursor of participation. Mobilization includes and then a description of our empirical testing of the pertinent four stages: being sympathetic to the cause, being aware of the theoretical constructs. We conclude by reporting results and movement, motivation to take part and ability to participate. setting forth directions for future work. We suggest that expressions of mobilization in communications of individuals may be used to predict the approaching protest. We have utilized several Natural Language Processing techniques A. Related Work to create a methodology to identify mobilization in social media communication. This study is related to three research areas: social and political science on antecedents of protest behavior, research Results of experimentation with Twitter data collected before concerning forecasting events and behaviors using social media and during the 2015 Baltimore events and the information data, extracting information from social media.