Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jun 17, 2020
Date Accepted: Oct 2, 2020
Date Submitted to PubMed: Oct 27, 2020
Blaming Devices in Online Communication of the COVID-19 pandemic: Stigmatizing cues and negative sentiment gauged with automated analytic techniques
ABSTRACT
Background:
Background:
Information about a new coronavirus, which emerged in 2019, is rapidly spreading around the globe, gaining significant public attention and attracting much sentiment and negative discussion.
Objective:
Objective:
The study aimed to identify statements about the blameworthy agents by assessing online media attributions of blame by expressing sentiment and stigmatization. Using stigmatizing language for the purpose of blaming sparked a debate. The study looks at stigmatizing and sentiment as devices for blaming in Taiwan in 2020, when the world was haunted by COVID-19.
Methods:
Methods:
We extracted related online news articles and posts from social networks, media sharing networks, and online discussion forums between December 30, 2019, and March 31, 2020, in Taiwan. A codebook was developed based on stigmatizing language and sentiment.
Results:
Results:
After removing irrelevant discussions, we collated over 1.07 million Chinese texts from 11 online news sites, 10 discussion forums, Facebook, YouTube, and Instagram to conduct an automated content analysis. Online news served as a hotbed for negativity and for driving emotional social posts. Stigma is disclosed through negatively valenced responses rather than positive ones toward figures in mainland China. The adoption of the problematic moniker, “Wuhan pneumonia,” had a very high frequency, despite the WHO guideline to avoid biased perceptions and ethnic discrimination. Understanding the nomenclature and biased terms employed in relation to the coronavirus outbreak is paramount. We propose solidarity with communication professionals in combating the coronavirus outbreak and the infodemic. Finding solutions to curb the spread of virus bias, stigma, and discrimination is imperative.
Conclusions:
Conclusions:
Our sample is sufficiently representative of a community since it contains a broad range of mainstream online news and social media. Stigmatizing language linking the pandemic by online media influencers shows a lack of civic responsibility, encouraging bias, hostility, and discrimination. Stigmatizing terms frequently used were clearly deemed offensive, and they might have contributed to recent backlashes against China by encouraging and directing blaming. The implications, which range from risk communication to stigma mitigation and xenophobia concerns amid the COVID-19 outbreak, are emphasized. Clinical Trial: n/a
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