Twitter users operated by automated programs, also known as bots, have increased their appearance recently and induced undesirable social effects. While extensive research efforts have been devoted to the task of Twitter bot detection, previous methods leverage only a small fraction of user semantic and profile information, which leads to their failure in identifying bots that exploit multi-modal user information to disguise as genuine users. Apart from that, the state-of-the-art bot detectors fail to leverage user follow relationships and the graph structure it forms. As a result, these methods fall short of capturing new generations of Twitter bots that act in groups and seem genuine individually. To address these two challenges of Twitter bot detection, we propose BotRGCN, which is short for Bot detection with Relational Graph Convolutional Networks. BotRGCN addresses the challenge of community by constructing a heterogeneous graph from follow relationships and apply relational graph convolutional networks to the Twittersphere. Apart from that, BotRGCN makes use of multi-modal user semantic and property information to avoid feature engineering and augment its ability to capture bots with diversified disguise. Extensive experiments demonstrate that BotRGCN outperforms competitive baselines on a comprehensive benchmark TwiBot-20 which provides follow relationships. BotRGCN is also proved to effectively leverage three modals of user information, namely semantic, property and neighborhood information, to boost bot detection performance.
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