Matthews, J., 2024. Dynamics of COVID-19 Blame Attribution: A Corpus-Based Analysis of Readers’ Comments in Response to UK Online News. Communication and the Public. (In Press)
Full text available as:
|
PDF (OPEN ACCESS ARTICLE)
matthews-2024-dynamics-of-covid-19-blame-attribution-a-corpus-based-analysis-of-readers-comments-in-response-to-uk.pdf - Published Version Available under License Creative Commons Attribution Non-commercial. 1MB | |
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
DOI: 10.1177/20570473241258815
Abstract
This study adopts a longitudinal approach to analyse the attribution of blame in online comments for the emergence, continuation and consequences of COVID-19. It uses an innovative approach to distil a specialised corpus of readers’ comments in response to UK online news articles about COVID-19, before applying corpus linguistic techniques to identify the principal actors attributed as blame agents. The research found that both internal (the government and the prime minister) and external actors (China and the World Health Organization) were identified as blame agents in comments. The analysis also indicates the presence of blame attribution towards people, their own actions and behaviours, which, in part, may be a consequence of government and public health messaging that emphasised individual responsibility to reduce transmission of the virus. This is distinctive, with significance for public understanding of COVID-19 and for future pandemic communication planning.
Item Type: | Article |
---|---|
ISSN: | 2057-0473 |
Group: | Faculty of Media & Communication |
ID Code: | 39954 |
Deposited By: | Symplectic RT2 |
Deposited On: | 11 Jun 2024 06:50 |
Last Modified: | 11 Jun 2024 06:50 |
Downloads
Downloads per month over past year
Repository Staff Only - |