COVID-19–Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis
By S. Islam, Tonmoy Sarkar, Sazzad Hossain Khan, A. Kamal, S. M. Hasan, Alamgir Kabir, Dalia Yeasmin, Mohammad A. Islam, K. Chowdhury, K. S. Anwar and 2 more. American Journal of Tropical Medicine and Hygiene, 2020
902 citations recorded by the source corpus.
Abstract. Infodemics, often including rumors, stigma, and conspiracy theories, have been common during the COVID-19 pandemic. Monitoring social media data has been identified as the best method for tracking rumors in real time and as a possible way to dispel misinformation and reduce stigma. However, the detection, assessment, and response to rumors, stigma, and conspiracy theories in real time are a challenge. Therefore, we followed and examined COVID-19–related rumors, stigma, and conspiracy theories circulating on online platforms, including fact-checking agency websites, Facebook, Twitter, and online newspapers, and their impacts on public health. Information was extracted between December 31, 2019 and April 5, 2020, and descriptively analyzed. We performed a content analysis of the news articles to compare and contrast data collected from other sources. We identified 2,311 reports of rumors, stigma, and conspiracy theories in 25 languages from 87 countries. Claims were related to illness, transmission and mortality (24%), control measures (21%), treatment and cure (19%), cause of disease including the origin (15%), violence (1%), and miscellaneous (20%). Of the 2,276 reports for which text ratings were available, 1,856 claims were false (82%). Misinformation fueled by rumors, stigma, and conspiracy theories can have potentially serious implications on the individual and community if prioritized over evidence-based guidelines. Health agencies must track misinformation associated with the COVID-19 in real time, and engage local communities and government stakeholders to debunk misinformation.
Subfields: Communication, Epidemiology, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/f543627aedd386cdc8314c6a564d34cb7d4f3c8e
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Citations in the corpus
- Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023, background): “Examples of such efects include mortality and hospital admissions [20, 48], public fear and anxiety [79, 107], eroded trust in health institutions [87], and exacerbated racial discrimination and stigma [41, 48].”