TY - JOUR
T1 - Who's To Blame for the COVID-19 pandemic? Perceptions of responsibility during the crisis using text mining and latent Dirichlet allocation
AU - Chevalier, Marianne
AU - de la Sablonnière, Roxane
AU - Harel, Simon Olivier
AU - Ratté, Sylvie
AU - Pelletier-Dumas, Mathieu
AU - Dorfman, Anna
AU - Stolle, Dietlind
AU - Lina, Jean Marc
AU - Lacourse, Éric
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/1
Y1 - 2024/1
N2 - 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.
AB - 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.
KW - Blame attribution
KW - COVID-19
KW - Dramatic social change
KW - Latent Dirichlet allocation
KW - Pandemic
KW - Scapegoating
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85187933890&partnerID=8YFLogxK
U2 - 10.1016/j.ssaho.2024.100825
DO - 10.1016/j.ssaho.2024.100825
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AN - SCOPUS:85187933890
SN - 2590-2911
VL - 9
JO - Social Sciences and Humanities Open
JF - Social Sciences and Humanities Open
M1 - 100825
ER -