Background
Coronavirus disease (COVID-19) caused by a new form of coronavirus (SARS-CoV-2) first appeared in China end of 2019 and quickly spread to all counties of the world. To slow down the spread of the virus and to limit the pressure on the health care systems, different regulations and recommendations have been implemented by authorities, comprising amongst others the closure of all entertainment venues and social distancing. These measures have received mixed reactions, particularly from young individuals, with many not following available advice. Drawing on the information in social media discussion forums, the present study explores the reasons why people ignore the orders and recommendations of the authorities and why the authorities are unable to produce a shared sense of inclusion concerning protective measures against the coronavirus outbreak.
Methods
Three open-access social media forums (Reddit, Twitter, and YouTube comments) were systematically searched with respect to coronavirus/COVID-19-related beliefs, attitudes, and behaviours of individuals. The data was retrieved in the first three weeks of March 2020. Qualitative document analysis and qualitative content analysis were used as the methodical approach. The data was reviewed by all authors and jointly interpreted to minimise inconsistencies.
Results
In the analysis, 22 threads were used from the aforementioned websites. Six main themes were identified: information pollution on social media, the need to know the unusual threat that spreads rapidly, the impacts of the social environment, the role of government’s representatives and politicians, aids without concrete economic steps to satisfy them, and self-criticism by parents related to the behaviours and attitudes of their children.
Conclusion
In uncertain crises, transparency in the presentation of information and government policies emerge as influential determinants in creating social susceptibility and solidarity. The results further reveal the potential advantages and opportunities of using social media data in scientific investigations.