  The right to protest is perceived as one of the primary civil rights.
Citizens participate in mass demonstrations to express themselves and exercise
their 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 against
such damages. Recent research has shown that about 75 percent of protests that
are regarded as legal, are planned in advance. Twitter, the prominent
micro-blogging website, has been used as a tool by protestors for planning,
organizing, and announcing many of the recent protests worldwide such as those
that led to the Arab Spring, Britain riots, and those against Mr. Trump after
the presidential election in the U.S. In this paper, we aim to predict protests
by means of machine learning algorithms. In particular, we consider the case of
protests against the then-president-elect Mr. Trump after the results of the
presidential election were announced in November 2016. We first identify the
hashtags calling for demonstration from Trending Topics on Twitter, and
download the corresponding tweets. We then apply four machine learning
algorithms to make predictions. Our findings indicate that Twitter can be used
as a powerful tool for predicting future protests with an average prediction
accuracy of over 75 percent (up to 100 percent). We further validate our model
by predicting the protests held in the U.S. airports after President Trump's
executive order banning citizens of seven Muslim countries from entering the
U.S. An important contribution of our study is the inclusion of event specific
features for prediction purposes which helps to achieve high levels of
accuracy.
