Emotional Bots: Content-based Spammer Detection on Social Media

Abstract

Recent research indicates that a considerable amount of content on social media is generated by automated accounts. The automata present sophisticated behaviormimicking humans-aiming at evading traditional detection methods. In this paper, we present a supervised approach to detect automated accounts on Twitter using mainly contentbased features. We performed our experiments using four datasets that contain tweets from almost 20K malicious and benign accounts. Our methodology is lightweight and employs users' metadata, content and sentiment features. It performs well on unseen data (0.95 F1-score) reaching 95% precision and recall. This work also demonstrates that sentiment characteristics can add value to social spambot detection algorithms when combined with known features.

Twitter Bots in Cyber-Physical-Social Systems: Detection and Estimation Based on the SEIR Model

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TwiFly: A Data Analysis Framework for Twitter

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