@inproceedings{wu-etal-2025-fake,
    title = "A Fake News Detection Model Utilizing Graph Neural Networks to Capture Writing Styles",
    author = "Wu, Yen-Tsang  and
      Low, Lawrence Y. H  and
      Wang, Jenq-Haur",
    editor = "Chang, Kai-Wei  and
      Lu, Ke-Han  and
      Yang, Chih-Kai  and
      Tam, Zhi-Rui  and
      Chang, Wen-Yu  and
      Wang, Chung-Che",
    booktitle = "Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)",
    month = nov,
    year = "2025",
    address = "National Taiwan University, Taipei City, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard/2025.rocling-main.30/",
    pages = "286--295",
    ISBN = "979-8-89176-379-1",
    abstract = "本文提出 CWSMN(Capture Writing Style Multi-Graph Network),一個以圖神經網路為基礎的早期假新聞偵測方法,透過捕捉寫作風格克服傳統語意內容與傳播特徵方法在標註稀缺與跨域泛化不足下的限制。CWSMN 結合文體分析、語意嵌入與多圖融合:以 Bi-GRU 進行上下文初始化,採用 GAT 進行注意力導向的圖聚合,並以 LDA 建構主題圖,同時以輕量級前饋分類器輸出預測。於多個資料集之實驗顯示,CWSMN 對比 BERT、ALBERT 與 GraphSAINT 等強基準皆有穩定超越;在未知來源的 Source-CV 場景尤為顯著,證明其於低資源與跨領域環境之穩健泛化能力,並實現不依賴傳播的早期偵測,實驗結果證實本方法在樣本稀缺與未知來源條件下,仍能達成有效的早期偵測。"
}