@inproceedings{xu-etal-2026-trust-towards,
    title = "{TRUST}: Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification",
    author = "Xu, Ruixuan  and
      Hu, Mengting  and
      Wang, Zhunheng  and
      Jiang, Ming  and
      Ying, Rui  and
      Zhang, Zhen  and
      Gao, Hang  and
      Liu, Shuaipeng  and
      Cheng, Renhong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1575/",
    pages = "31477--31491",
    ISBN = "979-8-89176-395-1",
    abstract = "Social bots threaten online platforms by mimicking human behavior and forming deceptive connections, enabling the dissemination of misinformation while evading detection. Existing graph-based detection models leverage graph neural networks (GNNs) to capture relational structures and multimodal user features. However, such models are vulnerable to deceptive message propagation, where bots deliberately interact with legitimate users. These interactions create heterophilous edges{--}connections between nodes with different labels (i.e. human and bot){--}which undermine the homophily assumption that connected users typically share similar characteristics. In this work, we propose a novel framework to mitigate deceptive message propagation through node-level uncertainty estimation and graph structure purification. The framework comprises three key components: (1) Node uncertainty estimation employs evidential deep learning with an error-sensitive uncertainty loss to obtain calibrated node-wise uncertainty; (2) Uncertainty-guided pseudo-label generation assigns pseudo-labels to low-uncertainty nodes using a dynamic threshold; (3) Graph structure purification selectively disconnects heterophilous edges identified between differently labeled nodes. Extensive experiments on three benchmark datasets and six GNN backbones demonstrate that our framework consistently enhances detection performance and serves as an effective general-purpose enhancement module for social bot detection."
}