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

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.