Who's To Blame for the COVID-19 pandemic? Perceptions of responsibility during the crisis using text mining and latent Dirichlet allocation

Abstract

The spread of the contagious COVID-19 virus was quickly followed by an outbreak of explanations and discourses trying to make sense of the crisis. The goal of this paper is to track the changing dynamics of blame attribution and scapegoating in the Canadian population as the COVID-19 pandemic unfolds, with a particular emphasis on the influence of evolving public health measures. The study uses data from a longitudinal survey conducted with a representative sample of 3617 Canadians between April 2020 and May 2021 following a longitudinal design. Latent Dirichlet allocation (LDA), a computational approach to analyze text, was applied to data coming from an open-ended question on who or what should be held responsible for the COVID-19 pandemic. Nine topics were identified, six of which were recurring overtime. Canadians mostly blame distant collectives in the early months of the pandemic, especially China and wet markets. Over time, they increasingly blame local collectives, such as individuals who do not comply with sanitary measures. Blame attribution evolves with the proximity of the threat and the risk of international spread.

Keywords

• Blame attribution
• Scapegoating
• Dramatic social change
• Pandemic
• COVID-19
• Text mining