  In the dynamic and rapidly evolving world of social media, detecting
anomalous users has become a crucial task to address malicious activities such
as misinformation and cyberbullying. As the increasing number of anomalous
users improves the ability to mimic normal users and evade detection, existing
methods only focusing on bot detection are ineffective in terms of capturing
subtle distinctions between users. To address these challenges, we proposed
SeGA, preference-aware self-contrastive learning for anomalous user detection,
which leverages heterogeneous entities and their relations in the Twittersphere
to detect anomalous users with different malicious strategies. SeGA utilizes
the knowledge of large language models to summarize user preferences via posts.
In addition, integrating user preferences with prompts as pseudo-labels for
preference-aware self-contrastive learning enables the model to learn
multifaceted aspects for describing the behaviors of users. Extensive
experiments on the proposed TwBNT benchmark demonstrate that SeGA significantly
outperforms the state-of-the-art methods (+3.5\% ~ 27.6\%) and empirically
validate the effectiveness of the model design and pre-training strategies. Our
code and data are publicly available at https://github.com/ying0409/SeGA.
