Investigating Appraisal and the Language of Evaluation in Fake News Corpora

Abstract

The present corpus study, which is grounded in Appraisal Theory, investigates evaluative language use in fake news in English. The primary aim is to find out how and why, if at all, evaluative meanings are construed differently in fake news compared to genuine news. The secondary aim is to explore potential differences between types of fake news based on contextual factors. The data are from two carefully-designed corpora containing both fake and genuine news: a single-authored corpus and a multi-authored corpus. Both corpora contain false information that is meant to deceive, but they also differ from each other in terms of register, genre and the motivational goals of the authors. Through qualitative and quantitative analyses, we show that there are systematic differences in the occurrence of Appraisal expressions across fake and genuine news, with Appraisal being more common in the former. However, the exact nature of the affective, dialogic and modal expression of fake news is influenced by contextual factors that, so far, have largely been ignored in fake news research. Therefore, the study has important implications for the development of fake news detection systems based on data sources of different kinds, a task which is in grave need of the input of corpus linguists.