A Deep Learning Approach for Semantic Analysis of COVID-19-Related Stigma on Social Media

doi:10.1109/TCSS.2022.3145404

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 互联网大数据与安全信息学研究中心

	A Deep Learning Approach for Semantic Analysis of COVID-19-Related Stigma on Social Media
	Liu, Lin 1,3; Cao, Zhidong 1,3; Zhao, Pengfei 1,3; Hu, Paul Jen-Hwa 2; Zeng, Daniel Dajun 1,3; Luo, Yin 1,3
刊名	IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
	2022-02-16
页码	9
关键词	COVID-19 Social networking (online) Coronaviruses Blogs Pandemics Deep learning Statistical analysis Coronavirus disease of 2019 (COVID-19) deep learning public sentiment analysis social media social stigma
ISSN号	2329-924X
DOI	10.1109/TCSS.2022.3145404
通讯作者	Cao, Zhidong(zhidong.cao@ia.ac.cn)
英文摘要	The rapid spread of the pandemic of coronavirus disease of 2019 (COVID-19) has created an unprecedented, global health disaster. During the outburst period, the paucity of knowledge and research aggravated devastating panic and fears that lead to social stigma and created serious obstacles to contain the disastrous epidemic. We propose a deep learning-based method to detect stigmatized contents on online social network (OSN) platforms in the early stage of COVID-19. Our method performs a semantic-based quantitative analysis to unveil essential spatial-temporal characteristics of COVID-19 stigmatization for timely alerts and risk mitigation. Empirical evaluations are carried out to examine our method's predictive utilities. The visualization results of the co-occurrence network using Gephi indicate two distinct groups of stigmatized words that pertain to people in Wuhan and their dietary behaviors, respectively. Netizens' participations and stigmatizations in the Hubei region, where the COVID-19 broke out, are twice (p < 0.05) and four (p <0.01) times more frequent and intense than those in other parts of China, respectively. Also, the number of COVID-19 patients is correlated with COVID-19-related stigma significantly (correlation coefficient = 0.838, p <0.01). The responses to individual users' posts have the power law distribution, while posts by official media appear to attract more responses (e.g., likes, replies, and forward). Our method can help platforms and government agencies manage public health disasters through effective identification and detailed analyses of social stigma on social media.
资助项目	National Natural Science Foundation of China[72025404] ; National Natural Science Foundation of China[71621002] ; New Generation Artificial Intelligence Development Plan of China (2015-2030)[2021ZD0111205] ; Beijing Natural Science Foundation[L192012] ; Beijing Nova Program[Z201100006820085]
WOS研究方向	Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000757943800001
资助机构	National Natural Science Foundation of China ; New Generation Artificial Intelligence Development Plan of China (2015-2030) ; Beijing Natural Science Foundation ; Beijing Nova Program
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47885]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者	Cao, Zhidong
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 2.Univ Utah, David Eccles Sch Business, Salt Lake City, UT 84112 USA 3.Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Liu, Lin,Cao, Zhidong,Zhao, Pengfei,et al. A Deep Learning Approach for Semantic Analysis of COVID-19-Related Stigma on Social Media[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2022:9.
APA	Liu, Lin,Cao, Zhidong,Zhao, Pengfei,Hu, Paul Jen-Hwa,Zeng, Daniel Dajun,&Luo, Yin.(2022).A Deep Learning Approach for Semantic Analysis of COVID-19-Related Stigma on Social Media.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,9.
MLA	Liu, Lin,et al."A Deep Learning Approach for Semantic Analysis of COVID-19-Related Stigma on Social Media".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2022):9.