A Deep Learning Approach for Semantic Analysis of COVID-19-Related Stigma on Social Media | |
Liu, Lin2,3; Cao, Zhidong2,3; Zhao, Pengfei2,3; Paul Jen-Hwa Hu1; Zeng, Dajun2,3; Luo, Yin2,3 | |
刊名 | IEEE Transactions on Computational Social Systems |
2023 | |
页码 | 246-254 |
英文摘要 | Abstract— 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 learningbased 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. |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/51725] |
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.University of Utah 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Institute of Automation, Chinese Academy of Sciences |
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,2023:246-254. |
APA | Liu, Lin,Cao, Zhidong,Zhao, Pengfei,Paul Jen-Hwa Hu,Zeng, Dajun,&Luo, Yin.(2023).A Deep Learning Approach for Semantic Analysis of COVID-19-Related Stigma on Social Media.IEEE Transactions on Computational Social Systems,246-254. |
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 (2023):246-254. |
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