Fake news in elections: leveraging large language models using semantic analyses to extract insights from academic research

Abstract

This paper examines the delicate balance between fake news and freedom of speech by analyzing academic research at the intersection of “fake news” and “election.” Using 568 publications from the Web of Science database, the research applies data analytics and natural language processing (NLP) methods to uncover geographical patterns, thematic trends and sentiment in the scholarly discourse. Named Entity Recognition identifies the most studied countries and platforms, while topic modeling reveals recurring themes such as political misinformation, media influence and the role of artificial intelligence in fake news detection. The originality of this work lies in its integration of bibliometric analysis with advanced NLP-driven semantic, large language models and sentiment approaches, offering a multidimensional view of how fake news is framed in academic contexts. Unlike prior studies, it quantifies emerging trends using Compound Annual Growth Rate (CAGR), identifies propagation mechanisms like “sharing” and “engagement,” and bridges computational and social science perspectives. Findings show a strong research focus on misinformation during elections since 2016, with Brazil, Spain and the United States as leading contributors, and Twitter® and Facebook® as the most frequently studied platforms. Our research contributes to a deeper understanding of how misinformation and freedom of speech are debated in scholarly literature and underscores the need for interdisciplinary approaches to safeguard democratic integrity in the digital era.