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1. A Deep Learning Approach for Semantic
Analysis of COVID
Social Media
Abstract
The rapid spread of the pandemic of coronavirus
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
stigmatized contents on online social network (OSN) platforms in the early
stage of COVID-19. Our method performs a semantic
analysis to unveil essential spatial
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 di
of stigmatized words that pertain to people in Wuhan and their dietary
behaviors, respectively. Netizens’ participations and stigmatizations in the
A Deep Learning Approach for Semantic
Analysis of COVID-19-Related Stigma on
The rapid spread of the pandemic of coronavirus disease of 2019 (COVID
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
he disastrous epidemic. We propose a deep learning-based method to detect
stigmatized contents on online social network (OSN) platforms in the early
19. Our method performs a semantic-based quantitative
analysis to unveil essential spatial-temporal characteristics of COVID
stigmatization for timely alerts and risk mitigation. Empirical evaluations are
carried out to examine our method’s predictive utilities. The visualization
occurrence network using Gephi indicate two di
of stigmatized words that pertain to people in Wuhan and their dietary
behaviors, respectively. Netizens’ participations and stigmatizations in the
A Deep Learning Approach for Semantic
Related Stigma on
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
based method to detect
stigmatized contents on online social network (OSN) platforms in the early
based quantitative
emporal 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
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

2. 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.