Iperstoria (Jun 2024)
Face Masks and User-Generated Discourse in the Covid-19 Era
Abstract
Ensuing the World Health Organization’s (WHO) announcement on 11 March 2020 that Covid-19 had become a global pandemic, many governments worldwide introduced wearing masks as one of the primary measures to abide by in order to limit the spread of the virus. Since then, face masks have become one of the main symbols of the pandemic. Although their effectiveness in reducing the spread of Covid-19 infection has been backed by scientific evidence, wearing face masks has triggered a significant debate, mostly on social media (Baker, Concannon and So 2022; Al-Ramahi et al. 2021). User-generated discourse has expanded dramatically during the pandemic due to the enhanced online interaction possibilities. In particular, mask aversion is still perceived and represented online as an antisocial norm that has emerged during the current Covid-19 pandemic (Kim 2022). This study examines the reactions of Facebook and Twitter users to a recent new Coronavirus alert raised by New York City in response to rising cases, recommending, though not requiring, people to wear masks in public indoor settings. The comments posted were analysed using the basic methodology of Computer-Mediated Discourse Analysis (CMDA), which allows the identification of patterns in interactive message content (Herring 2010), and interpreted through a Critical Discourse Analysis lens to investigate the reasons of Internet users for and against wearing masks as a mitigation measure against Covid-19 spread. A quantitative and qualitative research approach was employed to analyse conversational and behavioural data in the social media discourse framed by the two factions supporting or contrasting mask wearing (Lang, Erickson and Jing-Schmidt 2021; Franz et al. 2019; Martin and White 2005). In addition, this study attempted to assess the variance in response to the same content posted on different platforms. The results show that social media can be a valuable source of data mining that could help decision-makers better understand the public discourse around crucial public health issues like wearing masks to curb the Coronavirus pandemic and effectively address public perception by adopting more suitable policies.
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