Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak
Abstract
:1. Introduction
2. Materials and Methods
2.1. Twitter Data
2.2. Coding Scheme
2.3. Content Analysis
3. Results
3.1. Specific Topics of COVID-19 Stigma
3.1.1. Mark
3.1.2. Group Labeling
3.1.3. Responsibility
3.1.4. Peril
3.2. Misinformation, Conspiracy Theories, and COVID-19 Stigma
4. Discussion
4.1. Practical Implications
4.2. Limitations and Future Research
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Coding Variables | Examples | Intercoder Reliability | Percentages (N = 7000) |
---|---|---|---|
Mark | 3.47% | ||
1. Flu-like symptoms | “The more I read about the coronavirus, the more I freak out when I hear someone cough or sneeze in public.” | 0.81 | 1.44% |
2. Personal protective equipment | “A Chinese girl was kicked off my train because she was wearing a facemask.” | 0.81 | 1.67% |
3. Asian Origin | “Asians should be banned from the US due to their ‘coronavirus privilege.’” | 0.82 | 2.11% |
4. Healthcare providers and essential workers | “I had a customer complained today bc he didn’t want to sit next to a nurse as they would get him sick.” | 0.77 | 0.17% |
Group Labeling | 1.19% | ||
1. Wuhan/China/Asian virus | “Actually, I prefer calling it Wuhan virus, more fitting.” | 0.90 | 0.86% |
2. Trump virus | “Yes #trumpvirus is additionally deadly because he is in the WH [White House]. But don’t forget you are endangered because the GOP allow his actions.” | 0.91 | 0.33% |
Responsibility | 1.77% | ||
1. Different eating habits | “@xxx you can’t blame anyone but the restaurant [who served] and the people who ate bat soup in Wuhan, China.” | 0.88 | 0.30% |
2. Travelling | “Coronavirus is running rampant in Europe right now and all these white girls on Instagram still be taking vacations to the beach.” | 0.89 | 0.91% |
3. Violating precautions | “@xxx this is so irresponsible. ‘India is magical so coronavirus can’t infect us!’ ?! No data supports this theory. COVID-19 is not the flu. Failure to take the threat seriously and delaying social distancing practices will result in faster spread.” | 0.75 | 0.59% |
Peril | 19.94% | ||
1. Health | “The #coronaoutbreak will kill many people and temporarily disable others.” | 0.84 | 9.34% |
2. Normal life | “Oh my god I just watched the news on tv the babies the coronavirus is progressing at my place they are making the decision to close all the schools and the public for 14 days it’s really scary I’m scared for my family and my mom.” | 0.84 | 6.84% |
3. Economy | “Things coronavirus will affect: house prices will crash, stock markets crash, unemployment will increase.” | 0.85 | 3.93% |
4. Healthcare system | “In a way the relatively low mortality rate makes the #coronavirus more of a problem as it spreads more widely and causes more strain on hospitals.” | 0.85 | 1.13% |
Misinformation | “7 million Americans get the flu annually and 61,000 die yet we have a vaccine. only 496 cases of Wuhan virus and 23 deaths. you, anti-trump celebs, the media, and Dems are weaponizing this for political gain and it’s not just irresponsible it’s criminal.” | 0.96 | 4.21% |
Conspiracy Theories | “The aliens are coming they started the coronavirus they are trying to kill us.” | 0.94 | 2.00% |
Stigma Message Content | Misinformation | Conspiracy Theories | ||
---|---|---|---|---|
Absent (n = 6705, %) | Present (n = 295, %) | Absent (n = 6860, %) | Present (n = 140, %) | |
Mark | 3.54a | 1.69a | 3.41a | 6.43a |
Group Labeling | 1.13a | 2.37a | 1.08a | 6.43b |
Responsibility | 1.76a | 2.03a | 1.66a | 7.14b |
Peril | 20.34b | 10.85a | 20.22b | 6.43a |
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Li, Y.; Twersky, S.; Ignace, K.; Zhao, M.; Purandare, R.; Bennett-Jones, B.; Weaver, S.R. Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak. Int. J. Environ. Res. Public Health 2020, 17, 6847. https://doi.org/10.3390/ijerph17186847
Li Y, Twersky S, Ignace K, Zhao M, Purandare R, Bennett-Jones B, Weaver SR. Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak. International Journal of Environmental Research and Public Health. 2020; 17(18):6847. https://doi.org/10.3390/ijerph17186847
Chicago/Turabian StyleLi, Yachao, Sylvia Twersky, Kelsey Ignace, Mei Zhao, Radhika Purandare, Breeda Bennett-Jones, and Scott R. Weaver. 2020. "Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak" International Journal of Environmental Research and Public Health 17, no. 18: 6847. https://doi.org/10.3390/ijerph17186847