Disintermediation and disinformation as a political strategy: use of AI to analyse fake news as Trump's rhetorical resource on Twitter

The communicative effects of disintermediation caused by social media promote the expansion of personalist and emotional political discourses that reach the audience directly and evade the traditional journalistic filter. This phenomenon leads to new political communication tactics, but also exposes citizens to potentially fraudulent, contaminated or polarised content. In this context, framed in post-truth, the term 'fake news' gains relevance as a way of referring to disinformation and as a political and performative argument that can be weaponised. This research aims to analyse such use in the discourse of the former president Donald Trump during his presidential term (2017-2021), focussing on Twitter as the main platform in his political communication strategy online. To analyse this, we resort to a methodological triangulation of content, discourse, and sentiment analysis, with the latter combining both lexicon and artificial intelligence (AI) techniques through machine learning on the basis of deep learning and natural language processing, which is applied to his messages published with the term 'fake news' (N = 768). The analysis of the sample, provided here in an open dataset, employs self-developed software that allows each unit of analysis to be filtered and coded around its predominant themes, sentiments, and words. The main results confirm that Trump's attribution of 'fake news' focusses on three main topics: the media (53%), politics (40%) and his cabinet (33%). It also shows how the former president resorts to a personalist agenda, focussed on the defence of his proposals and his team (80%) by delegitimizing his opponents and the press, with a negative tone (72%) loaded with derogatory terms, confirming a weaponised strategy of the term 'fake news' as a political argument of disinformation and disintermediation.