Tags

Type your tag names separated by a space and hit enter

Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques.
J Med Internet Res. 2020 11 25; 22(11):e21504.JM

Abstract

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.

Links

PMC Free PDF

Authors+Show Affiliations

Chang A
Department of Communication, Faculty of Social Sciences, University of Macau, Macao, China. Institute of Communication and Health, Lugano University, Lugano, Switzerland.
Schulz PJ
Institute of Communication and Health, Lugano University, Lugano, Switzerland.
Tu S
Department of Radio and Television, Ming Chuan University, Taipei, Taiwan.
Liu MT
Department of Management and Marketing, Faculty of Business Administration, University of Macau, Macao, China.

MeSH

COVID-19Chemistry, AnalyticData MiningHealth CommunicationHumansSARS-CoV-2

Pub Type(s)

Journal Article

Language

eng

PubMed ID

33108306

Citation

Chang, Angela, et al. "Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques." Journal of Medical Internet Research, vol. 22, no. 11, 2020, pp. e21504.
Chang A, Schulz PJ, Tu S, et al. Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques. J Med Internet Res. 2020;22(11):e21504.
Chang, A., Schulz, P. J., Tu, S., & Liu, M. T. (2020). Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques. Journal of Medical Internet Research, 22(11), e21504. https://doi.org/10.2196/21504
Chang A, et al. Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques. J Med Internet Res. 2020 11 25;22(11):e21504. PubMed PMID: 33108306.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques. AU - Chang,Angela, AU - Schulz,Peter Johannes, AU - Tu,ShengTsung, AU - Liu,Matthew Tingchi, Y1 - 2020/11/25/ PY - 2020/06/17/received PY - 2020/10/02/accepted PY - 2020/08/28/revised PY - 2020/10/28/pubmed PY - 2020/12/22/medline PY - 2020/10/27/entrez KW - COVID-19 KW - blame KW - communication KW - culprits KW - infodemic KW - infodemic analysis KW - infodemiology KW - infoveillance KW - negativity KW - pandemic KW - placing blame KW - political grievances KW - sentiment analysis KW - social media KW - stigma SP - e21504 EP - e21504 JF - Journal of medical Internet research JO - J Med Internet Res VL - 22 IS - 11 N2 - 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. SN - 1438-8871 UR - https://www.unboundmedicine.com/medline/citation/33108306/Communicative_Blame_in_Online_Communication_of_the_COVID_19_Pandemic:_Computational_Approach_of_Stigmatizing_Cues_and_Negative_Sentiment_Gauged_With_Automated_Analytic_Techniques_ DB - PRIME DP - Unbound Medicine ER -