Requiem for online harassers: Identifying racism from political tweets

During the last five years, the amount of users of online social networks has increased exponentially. With the growing of users, social problems also arise. Due to the nature of these platforms, specifically Twitter, users can express their ideas in the way they prefer no matter if it is racist or not. As the Twitter CEO says, one of the most difficult things for them is to detect and ban people who harass others. Researches have addressed this issue in recent years. However, it is needed a wider range of strategies to detect racist users and content. In this work, we collect tweets produced by the ego networks of the two former 2016 US Presidential Candidates: Hillary Clinton and Donald Trump, grouped in four datasets. After deleting spammers, we get 84,371 unique users labeled by using two different metrics: Sentiment Word Count and Racist Score. Both of them let us not only to identify users as racists, but also to detect the level of negativism by analyzing their most recent 200 tweets, increasing the effectiveness of the method. Using it, we find the most negative and racist user and the most positive and non-racist user from all datasets. Taking advantage of the topological properties of the ego networks we analyzed, we also verify that our results satisfy the sociologist theory of homophily; where the followers of each candidate represent their homophilous. For a nation as the United States of America, detecting online harassers might help to decrease racism and cyberbullying, social problems that affect their society. A world without online harassers is an utopia, but this is one step to achieve it.