ABSTRACT
The prevalence of fake news on social media platforms has become a serious matter as it can manipulate public opinions and decision-making. This type of deliberate disinformation creates confusion and makes it challenging for Internet users to differentiate between verified and fake content. The purpose of this chapter is to showcase how a comparative corpus-based approach can be effectively used to reveal distinctive language features of fake news. Specifically, a corpus of fake news spread on Twitter between 2016 and 2017 by the Russian Internet Research Agency (IRA) is compared to two other corpora. The first corpus includes verified news tweets produced by the international news agency Associated Press (AP), while the second corpus contains more sensationalist news tweets disseminated by the British middle-range tabloid the Daily Mail. By utilising the technique of keyword analysis, distinct lexico-grammatical, and diverse standard and non-standard graphemic features that are distinctive to fake news are uncovered. Ultimately, this chapter endeavours to increase our understanding of the particularities and peculiarities of the language of fake news with the view to foster critical (social) media literacy.
