A cross-lingual transfer learning method for online COVID-19-related hate speech detection

doi:10.1016/j.eswa.2023.121031

CORC > 自动化研究所 > 中国科学院自动化研究所 > 舆论大数据科学与技术应用联合实验室

	A cross-lingual transfer learning method for online COVID-19-related hate speech detection
	Liu, Lin 1,2; Xu, Duo 5; Zhao, Pengfei 1,2; Zeng, Daniel Dajun 1,2; Hu, Paul Jen-Hwa 3; Zhang, Qingpeng 4; Luo, Yin 1,2; Cao, Zhidong 1,2
刊名	EXPERT SYSTEMS WITH APPLICATIONS
	2023-12-30
卷号	234 页码:11
关键词	COVID-19 Deep learning Cross-lingual Hate speech detection Natural language processing
ISSN号	0957-4174
DOI	10.1016/j.eswa.2023.121031
通讯作者	Cao, Zhidong(zhidong.cao@ia.ac.cn)
英文摘要	During the COVID-19 pandemic, online social media platforms such as Twitter facilitate the exchange of information among people. However, the prevalence of "infodemic"such as online hate speech has exacerbated social rifts, discrimination, prejudice and even hate crimes. Timely and effective detection of the hate speech will help create a healthy public opinion environment. Most of the current COVID-19-related hate speech research focuses on a single language, such as English. In this paper, we introduce a cross-lingual transfer learning method, aiming to contribute to hate speech detection in low-resource languages. We propose a deep learning based model to classify hate speech with a pre-trained language model for multilingual text embedding. Data augmentation and cross-lingual contrastive learning are then utilized to further improve the performance of cross-lingual knowledge transfer. To evaluate our method, we collected three publicly available annotated COVID-19-related hate speech datasets on Twitter, i.e., two in English and one in German. Furthermore, a Chinese dataset based on Weibo is constructed to expand multilingual data. The experimental results across three languages illustrate the effectiveness of our method for cross-lingual hate speech detection. Test F1-scores of our method for English, Chinese, German as transfer target languages can reach up to 0.728, 0.799 and 0.612 respectively, which are on average better than other baselines.
资助项目	New Generation Artificial Intelligence Development Plan of China[2021ZD0111205] ; National Natural Science Foundation of China[72025404] ; National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[72074209] ; Beijing Natural Science Foundation, China[L192012] ; Beijing Nova Program, China[Z201100006820085]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:001059475500001
资助机构	New Generation Artificial Intelligence Development Plan of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation, China ; Beijing Nova Program, China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53203]
专题	舆论大数据科学与技术应用联合实验室
通讯作者	Cao, Zhidong
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Univ Utah, David Eccles Sch Business, Salt Lake City, UT USA 4.City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China 5.Beihang Univ, Sch Math Sci, Beijing 100191, Peoples R China
推荐引用方式 GB/T 7714	Liu, Lin,Xu, Duo,Zhao, Pengfei,et al. A cross-lingual transfer learning method for online COVID-19-related hate speech detection[J]. EXPERT SYSTEMS WITH APPLICATIONS,2023,234:11.
APA	Liu, Lin.,Xu, Duo.,Zhao, Pengfei.,Zeng, Daniel Dajun.,Hu, Paul Jen-Hwa.,...&Cao, Zhidong.(2023).A cross-lingual transfer learning method for online COVID-19-related hate speech detection.EXPERT SYSTEMS WITH APPLICATIONS,234,11.
MLA	Liu, Lin,et al."A cross-lingual transfer learning method for online COVID-19-related hate speech detection".EXPERT SYSTEMS WITH APPLICATIONS 234(2023):11.