@inproceedings{han-etal-2026-exploring,
    title = "Exploring and Distilling Multi-Dimensional Clues for Interpretable Social Bot Detection",
    author = "Han, Yi  and
      Lu, Haiqi  and
      Liao, Lizi  and
      Zhou, Shuhan  and
      Liu, Yuanxing  and
      Zhang, Weinan  and
      Liu, Ting",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1338/",
    pages = "28971--28992",
    ISBN = "979-8-89176-390-6",
    abstract = "Social bot accounts have long been disseminating disinformation and engaging in malicious activities on social media platforms. Detecting these social bots has become a critical and urgent task, essential for maintaining a healthy online ecosystem. Existing social bot detection research usually provides detection results directly without corresponding supportive explanations, making it difficult to assess the extent to which such predictions are trustworthy. This is a key concern for online moderation. In this work, we explore the detection interpretation and summarize a four-dimensional clue framework from individual and social perspectives. We propose CDRBot, which primarily employs outcome-reward reinforcement learning to train inspectors to generate faithful, grounded, and readable clues from the *User Information*, *Semantic Features*, *Interactive Situation*, and *Behavioral Pattern*. These clues are then integrated to make final predictions. Experimental results demonstrate that our approach outperforms other baselines in detection performance. The generated clues are faithful, grounded, and readable, and can significantly enhance the performance of large language models in social bot detection."
}