Empowering Detection of Malicious Social Bots and Content Spammers on Twitter by Sentiment Analysis

Abstract

The role of Twitter as a platform to share opinions has been growing in the recent years especially since it has been widely used by public personae such as politicians, personalities of the show business, and other influencers to communicate with the public. For these reasons, the use of social bots to manipulate information and influence people's opinions is also growing. In this paper, we use a supervised classification model to distinguish bots from legitimate users on Twitter. More specifically, we show the importance of sentiment features in bot-human account detection. Moreover, we evaluate our detection model by testing on Russian bot accounts who are the most recent set of social bots that appeared on Twitter to show that these techniques may be easily adapted to work on new, unseen types of social bots.