Interest-Aware Social Bot Detection with Contrastive Hard Sample Mining

Social bots frequently engage in malicious activities like spreading misinformation and phishing on major social media platforms, significantly impacting the fairness and security of these platforms. Therefore, detecting social bots has become a very critical task. However, we observe two challenges for bot detection methods: neglected discrepancies under various interests (e.g., politics, entertainment) and challenging cases in the real world (e.g., carefully camouflaged bots, individualized genuine users). To tackle these issues, we propose BotCHMIA, a novel interest-aware social bot detection method enhanced with challenging cases. Specifically, to enhance feature representations by various user interests, we propose an interest-aware feature collaboration that utilizes a series of expert networks and an interest adapter to acquire user interest-specific information and fuse it with task-specific feature representations extracted by a bot detection projection. Additionally, we estimate sample hardness during the training process based on the model’s classification confidence and improve existing supervised contrastive loss with randomly selected challenging cases, namely hard samples, to enhance the discriminability of user feature representations. We conduct extensive experiments on two real social bot datasets, and the results demonstrate the practical benefits gained from our proposed detection method.