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    题名: Can Generative AI Eliminate Speech Harms? A Study on Detection of Abusive and Hate Speech during the COVID-19 Pandemic
    作者: 楊亨利;李博逸
    Yang, Heng-Li;Li, Bo-Yi;Wang, Chen-Shu;Chen, Hong-Yan
    贡献者: 資管博六
    关键词: Abusive and hate speech;Machine learning;Language model
    日期: 2023-10
    上传时间: 2024-07-17
    摘要: This study investigates the issue of abusive and hateful language arising from the COVID-19 pandemic, this research employs machine learning techniques to establish a system capable of detecting and rephrasing abusive and hateful language. To begin, a dataset and dictionary specific to abusive and hateful language in Chinese. Subsequently, a two-stage detection model is proposed, with the BERT model yielding the most optimal outcomes. The first stage attains an accuracy of 94.42%, while the second stage achieves an accuracy of 81.48%.
    關聯: 2023 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), IEEE
    数据类型: conference
    DOI 連結: https://doi.org/10.1109/ICCE-Asia59966.2023.10326404
    DOI: 10.1109/ICCE-Asia59966.2023.10326404
    显示于类别:[資訊管理學系] 會議論文

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