Investigating the Potential of Aggregated Tweets as Surrogate Data for Forecasting Civil Protests

Abstract

Online Micro-blogging Social Media websites like Twitter are being used as a real-time platform for information sharing and communication during planning and mobilization of civil unrest events. We conduct a study of more than 1.5 million English Tweets spanning 5 months on the topic of Immigration and found evidences of Twitter being used as a platform for planning and mobilization of protests and civil disobedience related demonstrations. We believe that Twitter data can be used as a surrogate and open-source precursor for forecasting civil unrest and investigate Machine Learning based techniques for building a prediction model. We present our solution approach consisting of various components such as named entity recognition (temporal, spatial location, people expressions extraction), semantic enrichment of events related tweets (crowd-buzz & commentary and mobilization & planning) location-time-topic correlation miner. We conduct a series of experiments on a real-world and large dataset and investigate the application of trend analysis. We conduct two case studies on civil unrest related events and demonstrate the effectiveness of our approach.