On Predicting Social Unrest Using Social Media

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. on actual protests taken from news media show a correlation over time between volume of Twitter communications related to There is an abundance of research on protest in the fields of mobilization and occurrences of protest at certain geographical social and political sciences, using various approaches to the locations. We conclude with discussion of possible theoretical subject. Studies have been done at the macro level: looking explanations and practical applications of these results. at the influence of the political and economic situation in general on protest moods [1] and viewing protests as organized I. INTRODUCTION social movements [6]. From the perspective of an individual, scholarly research has been concerned with recruitment and Human society has a long history of social protests in var- activism [7], [8] and factors and cognitive processes affecting ious forms [1]. The use of digital communications including individual participation in protests [9], [10], [11]. Such factors social media has become a salient feature of protests in recent have also been used for modeling protest participation, e.g. in years. Scholars have established the link between individual’s [9], [10], [12], and in a recent study combining social science social media use and political involvement [2]. For example, with computational simulation of dynamics of participants scholarly studies have considered the role of social media [13]. Recent reviews of literature concerning individual protest in recruitment for protest [3], dissemination of situational participation can be found in [9], [10]. information during protest [4], [5] and relation between social media use and activism [2]. If digital communications can be Since the online social media gained their worldwide pop- used to facilitate the social protest, then can it be possible to ularity, a wide stream of research has emerged on extraction use these communications to predict the protest as well? This and structuring of the available information [14]. Among the study provides a positive answer to this question. various social media websites, Twitter is especially popular among scholars due to the availability of large numbers of data We hypothesize that there is a relationship between the that can be collected while the event is unfolding - a naturally volume of particular types of social media communications and occurring experiment. Approaches to extracting information actual occurrences of protests. The challenge in this approach from Twitter are described, for example, in [15] and [16]. lies in identifying the relevant messages in a very large stream Scholarly works taking advantage of these technologies have of social media. To tackle this challenge we developed a been concerned with public response to extreme events [17], methodology that intergrates research findings from the social [18], [19], modeling human behavior [20], [21], [22], and and political sciences with recent advances in natural language forecasting actions [21], [22], attitudes [23] or even election processing and information extraction. We have applied this results [24]. methodology to the Twitter data gathered before and during 2015 Baltimore protests and found empirical support for our Research on the role of social media in protests has hypothesis. emerged after the Arab Spring, when its contribution to the IEEE/ACM ASONAM 2016, August 18-21, 2016, San Francisco, CA, USA 978-1-5090-2846-7/16/$31.00 c 2016 IEEE 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) emergence of revolutions in Arab countries became apparent. As mobilization must precede protest participation, being Studies were undertaken on the role of social media in rev- able to identify individuals at different stages and to quanti- olutions [4], [25], information dissemination during protests tatively measure the progress of mobilization enables one to [2], [5], and activism and support of social movements [3], make inferences about the upcoming protest. [23], [26]. Despite the general abundance of research concerning B. Mobilization in Social Media social media and protest, studies focused on the prediction of a In order to utilize social media data we need a method protest using data from social media are scarce. In one of these to detect and measure mobilization. Assuming we can label papers [27], the authors compare the prediction of a protest individual messages according to mobilization stages (or lack with the prediction of election results. A study described in thereof), what do we expect to observe? [28] presents a solution, relying on the constructed vocabulary. Recent research [29] addresses the problem using influence Typically, research on social phenomena, such as protest, cascades as a predictor of protest. This study, however, also employs surveys conducted after the fact. While it has been relies on a constructed vocabulary. Authors of [30] exploit found that mobilization precedes individual participation in a previous activity of a social media user to predict whether protest [11], we do not know, based upon empirical research, her next message will be related to protest. The relationship how mobilization develops over time in order to result in a between social media activity and protests during the Arab protest. Spring was established in [31], using hashtags to filter relevant Consider a closed community, in which all individuals ini- messages. Our approach also uses geographical and topical tially sympathetic to the cause of a potential protest are known. grouping of messages and our results confirm main finding of As individuals are mobilizing, we would expect to observe these studies, which is that social media activity can be used a growing number of people at later stages of mobilization to forecast a protest. The notable difference in our approach, compared to those who don’t progress beyond sympathy. This which we consider our main contribution, is that we ground consideration, however, is not relevant in social media because our method of finding relevant messages on social science [9], we can only identify an individual’s mobilization status if [11], thus linking observations based on the data to underlying this individual voluntarily chooses to communicate it. Thus, social and cognitive processes. unlike the post-hoc survey study, for a longitudinal study using social media data it is unreasonable to assume a static set of II. THEORETICAL CONSIDERATIONS initial sympathizers (recruitment pool) for two reasons. First, different users may be vocal at each observation, and second, A. Introduction to Mobilization new sympathizers may emerge as a result of increased social Social science literature [9], [11] defines action mobiliza- media activity. This is due to the fact that communications are tion, which we refer to simply as ‘mobilization’ throughout not targeted just at sympathizers, but also at those who are this paper, as a four-stage cognitive process resulting in an neutral towards the issue (e.g. due to being unaware of it). In individual’s decision to participate in a collective action, such [21], [22] it has been shown that social media activity may as to protest. These stages are as follows. contribute to spreading of the behavior even among those who do not communicate about it, a phenomenon that we have no Sympathy to the cause reason to rule out in our case. Every potential protest has a cause - the reason why it happens, which normally emerges

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