This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2394) Infodemiology and Infoveillance (1278) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1573) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1015) Outbreak and Pandemic Preparedness and Management (1679) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1484)

Published on in Vol 22, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21504, first published .
Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques

Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques

Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques

Authors of this article:

Angela Chang1, 2 Author Orcid Image ;   Peter Johannes Schulz2 Author Orcid Image ;   ShengTsung Tu3 Author Orcid Image ;   Matthew Tingchi Liu4 Author Orcid Image
Article Authors Cited by (26) Tweetations (4) Metrics

Journals

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  21. Liu M, Sun Y. Understanding Blame in the Context of Childhood Obesity. Health Communication 2024;39(9):1684 View
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  26. Zhu J, Shan L, Limongi R. Netographic narratives of user-generated travelogues on tourist destination image of Thailand. PLOS ONE 2024;19(5):e0301582 View