Boumechaal, S. and Sharoff, S. orcid.org/0000-0002-4877-0210 (2024) Attitudes, Communicative Functions, and Lexicogrammatical features of Anti-Vaccine Discourse on {Telegram}. Applied Corpus Linguistics, 4 (2). 100095.
Abstract
This paper reports the process of collecting a corpus with examples of anti-vaccine discourse and the results of its linguistic analysis. The overall aim of the project is to help public health authorities to improve their communication campaigns by better understanding the conditions for misinformation spreading via social media. More specifically, this paper analyses linguistic properties of a corpus of prominent misinformation channels in Telegram as compared against a more general COVID corpus as well as against a general purpose English corpus. For this paper, the quantitative analysis relies on corpus querying to identify the most recurrent discourse patterns related to COVID vaccines. We use the appraisal framework to analyse the patterns with respect to the attitudes conveyed in the messages. We have also applied an automatic AI classifier to predict communicative functions of these texts. This allows us to examine them more closely through the use of simple lexicogrammatical features following Biber, as well as their ideational processes following Halliday. The findings show that common collocations in the Telegram corpus containing misinformation draw on three attitudes: fear, insecurity, and mistrust in COVID vaccines which are discursively constructed to promote vaccine hesitancy among social media users. Furthermore, the misinformation messages tend to occur more often in such communicative functions as promotional texts, news reporting, and text expressed as presenting reference information.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2024, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. This is an author produced version of an article published in Applied Corpus Linguistics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Attitudes; Communicative Functions; Misinformation on Telegram |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Arts, Humanities and Cultures (Leeds) > School of Languages Cultures & Societies (Leeds) > Translation Studies (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/R511717/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Jan 2024 11:27 |
Last Modified: | 30 May 2024 09:08 |
Published Version: | https://www.sciencedirect.com/science/article/pii/... |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.acorp.2024.100095 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206956 |
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Filename: covid19-ling-paper-souad-finala_Serge Sharoff.pdf
