MSTBC: X Bot Detection with Multiple Social-Temporal Behavior Contrast

X bot detection aims to automatically identify malicious X bots on the X platform, playing a crucial role in protecting information and maintaining platform stability.Recently, mixture-based methods primarily simultaneously consider investigating various social features (e.g. user metadata, tweets, and social relationships) of users to differentiate humans and bots, which hold excellent performance. However, two major challenges have not been adequately addressed in current mixture-based methods: (1) Humans and bots exhibit different temporal behavior patterns, which has not been fully explored.(2) Existing mixture-based methods promote the detection by fusing diverse features but overlook the noise accumulation that arises during the fusion process.In this paper, we propose a novel X bot detection method with Multiple Social-Temporal Behavior Contrast (MSTBC), which integrates users' multiple social-temporal behaviors, including the static behavior (description content), social behavior (social structure) and temporal behavior (temporal behavior patterns).Specifically, the fine-grained temporal behaviors of users are represented as four different prompts. A temporal behavior PLM with temporal behavior prompts in MSTBC serves as the encoder to understand temporal behavior patterns.In addition, we employ multi-behavior contrast to minimize the differences of various features of users, alleviating the noise accumulation that arises during the fusion of diverse features.Experimental results demonstrate that MSTBC outperforms state-of-the-art models on four datasets. The code is available at https://anonymous.4open.science/r/MSTBC-C659.