ArticleJMIR infodemiology2026
Performance of Large Language Models in the Cognitive Analysis of Misinformation: Evaluation Study.
Article in JMIR infodemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPublic discourse is significantly impacted by the rapid spread of misinformation on social media platforms. Human moderators, while capable of performing well, face many challenges due to scalability. While large language models (LLMs) show great potential across various language tasks, their capacity for cognitive and contextual analysis, in detecting and interpreting misinformation, remains less evaluated.
objectiveThis study evaluates the effectiveness of LLMs in detecting and interpreting misinformation compared to human annotators, focusing on tasks requiring cognitive analysis and complex judgment. Additionally, we analyze the influence of different prompt engineering strategies on model performance and discuss ethical considerations for using LLMs in content moderation systems.
methodsWe evaluated 4 OpenAI models against a panel of human annotators using a subset of posts from the MuMiN dataset. Each model and human annotator responded to structured questions on misinformation, following an established cognitive framework. Both human annotators and LLMs also provided scores indicating how confident they were in their responses. Various prompting strategies were used in this research, including: 0-shot, few-shot, and chain-of-thought, with performance evaluated through precision, recall, F
resultsGPT-4 Turbo with chain-of-thought prompting achieved the highest performance of all LLMs for detecting misinformation, with an accuracy of 67.2% and an F
conclusionsLLMs show significant potential for automating misinformation detection. Their limitations in understanding and interpreting these posts highlight the current necessity of human oversight. A hybrid framework combining LLMs for preliminary screening with human moderators for more complex evaluation presents a promising future direction. Future research could prioritize the fine-tuning of LLMs using datasets that emphasize cognitive and emotional linguistic features, alongside the development of advanced prompting techniques.
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