Title: Applying Text Mining to Protest Stories as Voice against Media Censorship,
Abstract: Data driven activism attempts to collect, analyze and visualize data to
foster social change. However, during media censorship it is often impossible
to collect such data. Here we demonstrate that data from personal stories can
also help us to gain insights about protests and activism which can work as a
voice for the activists. We analyze protest story data by extracting location
network from the stories and perform emotion mining to get insight about the
protest.