BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency

Twitter bot detection is an important and meaningful task. Existing text-based methods can deeply analyze user tweet content, achieving high performance. However, novel Twitter bots evade these detections by stealing genuine users' tweets and diluting malicious content with benign tweets. These novel bots are proposed to be characterized by semantic inconsistency. In addition, methods leveraging Twitter graph structure are recently emerging, showing great competitiveness. However, hardly a method has made text and graph modality deeply fused and interacted to leverage both advantages and learn the relative importance of the two modalities. In this paper, we propose a novel model named BIC that makes the text and graph modalities deeply interactive and detects tweet semantic inconsistency. Specifically, BIC contains a text propagation module, a graph propagation module to conduct bot detection respectively on text and graph structure, and a proven effective text-graph interactive module to make the two interact. Besides, BIC contains a semantic consistency detection module to learn semantic consistency information from tweets. Extensive experiments demonstrate that our framework outperforms competitive baselines on a comprehensive Twitter bot benchmark. We also prove the effectiveness of the proposed interaction and semantic consistency detection.