Communicative blame in online communication of the COVID-19 pandemic: Computational approach of stigmatizing cues and negative sentiment gauged with automated analytic techniques

Angela Chang, Peter Johannes Schulz, Sheng Tsung Tu, Matthew Tingchi Liu

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Background: Information about a new coronavirus emerged in 2019 and rapidly spread around the world, gaining significant public attention and attracting negative bias. The use of stigmatizing language for the purpose of blaming sparked a debate. Objective: This study aims to identify social stigma and negative sentiment toward the blameworthy agents in social communities. Methods: We enabled a tailored text-mining platform to identify data in their natural settings by retrieving and filtering online sources, and constructed vocabularies and learning word representations from natural language processing for deductive analysis along with the research theme. The data sources comprised of ten news websites, eleven discussion forums, one social network, and two principal media sharing networks in Taiwan. A synthesis of news and social networking analytics was present from December 30, 2019, to March 31, 2020. Results: We collated over 1.07 million Chinese texts. Almost two-thirds of the texts on COVID-19 came from news services (n=683,887, 63.68%), followed by Facebook (n=297,823, 27.73%), discussion forums (n=62,119, 5.78%), and Instagram and YouTube (n=30,154, 2.81%). Our data showed that online news served as a hotbed for negativity and for driving emotional social posts. Online information regarding COVID-19 associated it with China-and a specific city within China through references to the “Wuhan pneumonia”-potentially encouraging xenophobia. The adoption of this problematic moniker had a high frequency, despite the World Health Organization guideline to avoid biased perceptions and ethnic discrimination. Social stigma is disclosed through negatively valenced responses, which are associated with the most blamed targets. Conclusions: Our sample is sufficiently representative of a community because it contains a broad range of mainstream online media. Stigmatizing language linked to the COVID-19 pandemic shows a lack of civic responsibility that encourages bias, hostility, and discrimination. Frequently used stigmatizing terms were deemed offensive, and they might have contributed to recent backlashes against China by directing blame and encouraging xenophobia. The implications ranging from health risk communication to stigma mitigation and xenophobia concerns amid the COVID-19 outbreak are emphasized. Understanding the nomenclature and biased terms employed in relation to the COVID-19 outbreak is paramount. We propose solidarity with communication professionals in combating the COVID-19 outbreak and the infodemic. Finding solutions to curb the spread of virus bias, stigma, and discrimination is imperative.

Original languageEnglish
Article numbere21504
JournalJournal of Medical Internet Research
Issue number11
StatePublished - Nov 2020

Bibliographical note

Publisher Copyright:
© Angela Chang, Peter Johannes Schulz, ShengTsung Tu, Matthew Tingchi Liu. Originally published in the Journal of Medical Internet Research (, 25.11.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.


  • Blame
  • COVID-19
  • Communication
  • Culprits
  • Infodemic
  • Infodemic analysis
  • Infodemiology
  • Infoveillance
  • Negativity
  • Pandemic
  • Placing blame
  • Political grievances
  • Sentiment analysis
  • Social media
  • Stigma


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