In this paper, we show how to automate this analysis by using machine learning in a realistic setting.
we show how to classify trolls according to their political role-left, news feed, right-by using features extracted from social media, i.e., Twitter, in two scenarios: (i) in a traditional supervised learning scenario, where labels for trolls are available, and (ii) in a distant supervision scenario, where labels for trolls are not available, and we rely on more-commonly-available labels for news outlets mentioned by the trolls.