False News Discourse Online: Corpus-Assisted Discourse Analyses

Abstract

This thesis examines the discourse characteristics of online false news articles using a mixed-method corpus-assisted discourse approach. The study employs three corpus-assisted analytical methods: move analysis (MA), key semantic domains analysis (KSDA), and multidimensional analysis (MDA) to analyse online false news articles in terms of discourse structure, discursive news values, and stylistic patterns respectively, with the goal of understanding how false news influences its audience. The corpus includes 137 verifiably online false news articles (totalling 106,673 words) on climate change, vaccination, and COVID-19. It also includes a comparative corpus consisting of 548 news articles (totalling 350,798 words) from reliable and reputable broadsheets, tabloids, web-based publications, and blogs covering the same topics. The MA findings indicate that the primary goal of false news articles is challenging mainstream narratives. This aim is reflected in the discourse structure of false news articles, which combines elements of expository, narrative, and argumentative genres. The KSDA uncovers that false news discourse emphasises meanings constructing unique news values, such as scepticism, corroboration, causality, mysticism, and historicity. The MDA showed that false news shares stylistic similarities with traditional and reliable news sources, especially broadsheets. Both false news and broadsheets exhibit informational, involved, narrative, and expository styles. However, false news differs from all other news types in that it does not tend to use an explicitly advocating style. Overall, the study reveals that these similarities and distinctive characteristics contribute to the persuasive and viral impact of false news discourse. The study highlights the significant threat posed by false news, noting that false news can gain credibility and traction by imitating credible journalism and that false news can be difficult to detect by detection algorithms based on surface-level stylistic features. Moreover, the study emphasises that the key factor for the linguistic variations between false news and true news is false news’s primary goal of challenging mainstream narratives. Recognizing the contradictory nature of false news compared to established narratives and the potential for audience low credulity, false news writers strategically design their discourse to appeal to audiences in a manner distinct from true news. This finding underscores the pervasive issues of scepticism and information crisis in society. The thesis concludes with a reflection on the corpus and the mixed-method approach, as well as recommendations for future research and practical implications.