Large Language Models for Misinformation Detection and Intervention in Media Networks

Abstract

The rapid dissemination of information in media networks such as social media, news platforms, and video-sharing applications has reshaped public communication and knowledge acquisition. However, the same environment has accelerated the spread of misinformation and fake news, which can undermine trust, distort perceptions, and create severe consequences in sensitive domains including politics, healthcare, and finance. Early detection methods, relying on keyword-based heuristics and small-scale classifiers, proved inadequate in addressing the scale, diversity, and multimodality of modern misinformation. The emergence of Large Language Models (LLMs) provides new opportunities, as these models demonstrate strong semantic understanding, contextual reasoning, and few-shot adaptability. This paper reviews methodological advances in LLM-based misinformation detection, including direct classification, retrieval-augmented verification, network-aware detection, and generative intervention strategies. We also discuss major challenges such as hallucination, computational costs, multimodal complexity, data limitations, and privacy concerns. Finally, potential solutions are proposed, including continual dataset updates, hierarchical detection pipelines, multimodal fusion, and privacy-preserving personalization. These findings highlight both the opportunities and limitations of LLMs, underscoring the need for robust, scalable, and ethical frameworks to combat misinformation in media networks.