@inproceedings{yang-etal-2023-rumor,
    title = "Rumor Detection on Social Media with Crowd Intelligence and {C}hat{GPT}-Assisted Networks",
    author = "Yang, Chang  and
      Zhang, Peng  and
      Qiao, Wenbo  and
      Gao, Hui  and
      Zhao, Jiaming",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.emnlp-main.347/",
    doi = "10.18653/v1/2023.emnlp-main.347",
    pages = "5705--5717",
    abstract = "In the era of widespread dissemination through social media, the task of rumor detection plays a pivotal role in establishing a trustworthy and reliable information environment. Nonetheless, existing research on rumor detection confronts several challenges: the limited expressive power of text encoding sequences, difficulties in domain knowledge coverage and effective information extraction with knowledge graph-based methods, and insufficient mining of semantic structural information. To address these issues, we propose a Crowd Intelligence and ChatGPT-Assisted Network(CICAN) for rumor classification. Specifically, we present a crowd intelligence-based semantic feature learning module to capture textual content{'}s sequential and hierarchical features. Then, we design a knowledge-based semantic structural mining module that leverages ChatGPT for knowledge enhancement. Finally, we construct an entity-sentence heterogeneous graph and design Entity-Aware Heterogeneous Attention to effectively integrate diverse structural information meta-paths. Experimental results demonstrate that CICAN achieves performance improvement in rumor detection tasks, validating the effectiveness and rationality of using large language models as auxiliary tools."
}