mlsanityData to 14 Oct 2026

Explore the Potential of LLMs in Misinformation Detection: An Empirical Study

arXiv 2311.12699 ↗First posted 21 Nov 2023cs.CL cs.AI cs.CYcited by 12

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Abstract (arXiv)

Large Language Models (LLMs) have garnered significant attention for their powerful ability in natural language understanding and reasoning. In this paper, we present a comprehensive empirical study to explore the performance of LLMs on misinformation detection tasks. This study stands as the pioneering investigation into the understanding capabilities of multiple LLMs regarding both content and propagation across social media platforms. Our empirical studies on eight misinformation detection datasets show that LLM-based detectors can achieve comparable performance in text-based misinformation detection but exhibit notably constrained capabilities in comprehending propagation structure compared to existing models in propagation-based misinformation detection. Our experiments further demonstrate that LLMs exhibit great potential to enhance existing misinformation detection models. These findings highlight the potential ability of LLMs to detect misinformation.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 23 of its 60 references that are in the map. First posted on arXiv 21 Nov 2023.

Builds on

23 of 60 references.

References · 23

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11 more
  1. 2020FANG: Leveraging Social Context for Fake News Detection Using Graph Representationcited by 99
  2. 2021User Preference-aware Fake News Detectioncited by 79
  3. 2023Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detectioncited by 72
  4. 2023Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attackscited by 70
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 2; 2026: 5.

12 papers.

  1. 2024DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detectioncited by 41
  2. 2024Securing Large Language Models: Threats, Vulnerabilities and Responsible Practicescited by 37
  3. 2024What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detectioncited by 25
  4. 2025Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategiescited by 16
  5. 2024Comparing GPT-4 and Open-Source Language Models in Misinformation Mitigationcited by 9
  6. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  7. 2024Can LLMs be Fooled? Investigating Vulnerabilities in LLMscited by 5
  8. 2026Are LLMs More Skeptical of Entertainment News?cited by 0
  9. 2026An Information-theoretic Propagation Denoising and Fusion Framework for Fake News Detectioncited by 0
  10. 2026Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerabilitycited by 0
  11. 2026From Generation to Detection: Exploration of Discourse Driven Scenario based LLM Generated Fake Newscited by 0
  12. 2026SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detectioncited by 0