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政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 > Item 140.119/152360

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/152360

Title:	Can Generative AI Eliminate Speech Harms? A Study on Detection of Abusive and Hate Speech during the COVID-19 Pandemic
Authors:	楊亨利;李博逸 Yang, Heng-Li;Li, Bo-Yi;Wang, Chen-Shu;Chen, Hong-Yan
Contributors:	資管博六
Keywords:	Abusive and hate speech;Machine learning;Language model
Date:	2023-10
Issue Date:	2024-07-17
Abstract:	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%.
Relation:	2023 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), IEEE
Data Type:	conference
DOI 連結:	https://doi.org/10.1109/ICCE-Asia59966.2023.10326404
DOI:	10.1109/ICCE-Asia59966.2023.10326404
Appears in Collections:	[資訊管理學系] 會議論文

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