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The right to protest is perceived as one of the primary civil rights.Citizens participate in mass demonstrations to express themselves and exercisetheir democratic rights. However, because of the large number of participants,protests may lead to violence and destruction, and hence can be costly. Thus,it is important to predict such demonstrations in advance to safeguard againstsuch damages. Recent research has shown that about 75 percent of protests thatare regarded as legal, are planned in advance. Twitter, the prominentmicro-blogging website, has been used as a tool by protestors for planning,organizing, and announcing many of the recent protests worldwide such as thosethat led to the Arab Spring, Britain riots, and those against Mr. Trump afterthe presidential election in the U.S. In this paper, we aim to predict protestsby means of machine learning algorithms. In particular, we consider the case ofprotests against the then-president-elect Mr. Trump after the results of thepresidential election were announced in November 2016. We first identify thehashtags calling for demonstration from Trending Topics on Twitter, anddownload the corresponding tweets. We then apply four machine learningalgorithms to make predictions. Our findings indicate that Twitter can be usedas a powerful tool for predicting future protests with an average predictionaccuracy of over 75 percent (up to 100 percent). We further validate our modelby predicting the protests held in the U.S. airports after President Trump'sexecutive order banning citizens of seven Muslim countries from entering theU.S. An important contribution of our study is the inclusion of event specificfeatures for prediction purposes which helps to achieve high levels ofaccuracy.

Twitter Reveals: Using Twitter Analytics to Predict Public Protests