Attitudes, communicative functions, and lexicogrammatical features of anti-vaccine discourse on Telegram

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AT32DE82F" target="_blank" >RIV/00216208:11320/25:T32DE82F - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2666799124000121" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2666799124000121</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.acorp.2024.100095" target="_blank" >10.1016/j.acorp.2024.100095</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Attitudes, communicative functions, and lexicogrammatical features of anti-vaccine discourse on Telegram

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Attitudes, communicative functions, and lexicogrammatical features of anti-vaccine discourse on Telegram

Popis výsledku anglicky

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.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Corpus Linguistics

ISSN

2666-7991

e-ISSN

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Svazek periodika

4

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

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EID výsledku v databázi Scopus

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