Artificial Intelligence and Fake News: Linguistic Analysis, Gender Bias, and Ethics in Digital Media Communication
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Abstract: Often amplifying gender bias and consolidating stereotypes through inappropriate language, the digital landscape is a privileged space for the manipulation and dissemination of false or misleading content. Moreover, the deployment of Artificial Intelligence (AI) systems in content generation, moderation, and dissemination not only appears to reproduce pre-existing biases but also to intensify them, thereby contributing to the consolidation of narratives that perpetuate stereotypes and facilitate the circulation of disinformation.
Adopting a mixed-method approach, this exploratory study combines corpus-based linguistic and cross-linguistic analysis with technical and theoretical perspectives on digital communication and AI ethics, positioning itself within the contemporary debate on the capacity of generative technologies to amplify bias and stereotypes. Within this framework, the research integrates three analytical dimensions: (1) a quantitative analysis measuring the frequency and normalized distribution of gender-marked and non-inclusive terms; (2) a sentiment analysis to examine how emotional polarity (positive, neutral, negative) interacts with lexical bias and framing; and (3) a qualitative semantic and pragmatic examination of discursive features, including contrastive constructions, voice and agency patterns, and semantic role attribution. The study is based on a corpus of 12 articles focusing on the two most-searched topics in the United Kingdom and the United States, selected using Google Trends 2024. The corpus includes articles from The New York Times, The Washington Post, The Guardian, and Daily Mirror, newspapers in which a declared use of Artificial Intelligence technologies was identified. The findings reveal that, despite differences among newspapers, algorithmically mediated language is not neutral, and the use of contrastive expressions or the choice of passive constructions tends to reinforce discriminatory dynamics.
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