Time period: February – September 2021
Description: In the context of the current COVID-19 health crisis, health-related misinformation has been spread to back-up what are mainly political considerations, including on the use of protective facemasks. Our study brings together social sciences and computer science expertise to unpack the #NoMask discourses and conversations by using both network analysis approaches on big data retrieved from Twitter and qualitative analyses on sub-sets of relevant social media data. We aim to better understand the role of Twitter in that interesting area where the dissemination of medical misinformation becomes capitalized by the political narrative linking the social discontent caused by the socio-economic impacts of the pandemic to specific political ideologies. Our analyses show that there is not a unique “NoMask movement”, nor a defined online community. Rather, we can identify a range of relatively niche, loosely connected and heterogeneous actors independently pushing diverse (but converging and compatible) discourses. Conversations occurring under the #NoMask umbrella are overall limited, as twitters using relevant hashtags are not talking to each other; nonetheless, they are successfully engaging a larger audience.
Research Team: Anita Lavorgna (PI), Les Carr, Ashton Kingdon (University of Southampton)
Contact: Anita Lavorgna (anita.lavorgna@unibo.it)