Detection of Fake News in Romanian: LLM-Based Approaches to COVID-19 Misinformation

Abstract

The spread of misinformation during the COVID-19 pandemic raised widespread concerns about public health communication and media reliability. In this study, we focus on these issues as they manifested in Romanian-language media and employ Large Language Models (LLMs) to classify misinformation, with a particular focus on super-narratives—broad thematic categories that capture recurring patterns and ideological framings commonly found in pandemic-related fake news, such as anti-vaccination discourse, conspiracy theories, or geopolitical blame. While some of the categories reflect global trends, others are shaped by the Romanian cultural and political context. We introduce a novel dataset of fake news centered on COVID-19 misinformation in the Romanian geopolitical context, comprising both annotated and unannotated articles. We experimented with multiple LLMs using zero-shot, few-shot, supervised, and semi-supervised learning strategies, achieving the best results with an LLaMA 3.1 8B model and semi-supervised learning, which yielded an F1-score of 78.81%. Experimental evaluations compared this approach to traditional Machine Learning classifiers augmented with morphosyntactic features. Results show that semi-supervised learning substantially improved classification results in both binary and multi-class settings. Our findings highlight the effectiveness of semi-supervised adaptation in low-resource, domain-specific contexts, as well as the necessity of enabling real-time misinformation tracking and enhancing transparency through claim-level explainability and fact-based counterarguments.