Adaptive Mixture of Domain-aware Experts for Detecting Social Bots
Qianqian Lü, Shilong Li, Kun Li, Wei Zhou, Liangjun Zang · 2024
Social bot detection has received widely attention from academic and industrial communities. However, existing bot detection methods are far from perfect. To mimic genuine users on social networks, advanced social bots are often active in multiple domains and have mixed characteristics of multiple domains. It is unreasonable to classify a bot with only one domain. To effectively extract and fuse features from multiple domains, we propose a novel method for Domain-aware Social Bot Detection (DSBD). Specifically, we first use a prompt-based method for zero-shot domain classification to obtain accurate domain distribution for any user. We then aggregate multiple domain expert representations through a domain gate, and finally use the fused representation to classify. Experimental results show that our approach consistently outperforms all baselines and that our fusion strategy perform well in various settings especially zero-shot situation.