# Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies

arXiv: https://arxiv.org/abs/2503.00724 · HTML: https://mlsanity.com/paper/2503.00724/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 2 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2403.03170/) (2024)
- [RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions](https://mlsanity.com/paper/2501.00353/) (2024)
- [Multimodal Misinformation Detection using Large Vision-Language Models](https://mlsanity.com/paper/2407.14321/) (2024)
- [Joint Audio-Visual Idling Vehicle Detection with Streamlined Input Dependencies](https://mlsanity.com/paper/2410.21170/) (2024)
- [Explore the Potential of LLMs in Misinformation Detection: An Empirical Study](https://mlsanity.com/paper/2311.12699/) (2023)
- [Large Language Model Agent for Fake News Detection](https://mlsanity.com/paper/2405.01593/) (2024)
- [Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction](https://mlsanity.com/paper/2405.01884/) (2024)
- [Can Large Language Models Detect Misinformation in Scientific News Reporting?](https://mlsanity.com/paper/2402.14268/) (2024)
- [Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions](https://mlsanity.com/paper/2502.00339/) (2025)
- [Improving Academic Skills Assessment with NLP and Ensemble Learning](https://mlsanity.com/paper/2409.19013/) (2024)
- [Optimization of Transformer heart disease prediction model based on particle swarm optimization algorithm](https://mlsanity.com/paper/2412.02801/) (2024)
- [A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit](https://mlsanity.com/paper/2502.09097/) (2025)
- [Sympathy over Polarization: A Computational Discourse Analysis of Social Media Posts about the July 2024 Trump Assassination Attempt](https://mlsanity.com/paper/2501.09950/) (2025)
- [Mixed-Precision Graph Neural Quantization for Low Bit Large Language Models](https://mlsanity.com/paper/2501.18154/) (2025)

## Built on by (most cited first, 16 of 16)

- [LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions](https://mlsanity.com/paper/2509.18970/) (2025)
- [Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models](https://mlsanity.com/paper/2505.18596/) (2025)
- [User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data](https://mlsanity.com/paper/2505.06305/) (2025)
- [Goal-Aware Identification and Rectification of Misinformation in Multi-Agent Systems](https://mlsanity.com/paper/2506.00509/) (2025)
- [Research on Personalized Medical Intervention Strategy Generation System based on Group Relative Policy Optimization and Time-Series Data Fusion](https://mlsanity.com/paper/2504.18631/) (2025)
- [From Evidence to Verdict: An Agent-Based Forensic Framework for AI-Generated Image Detection](https://mlsanity.com/paper/2511.00181/) (2025)
- [Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguity](https://mlsanity.com/paper/2507.23121/) (2025)
- [The Compositional Architecture of Regret in Large Language Models](https://mlsanity.com/paper/2506.15617/) (2025)
- [SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs](https://mlsanity.com/paper/2604.22134/) (2026)
- [Human-AI Co-Creation: A Framework for Collaborative Design in Intelligent Systems](https://mlsanity.com/paper/2507.17774/) (2025)
- [An Experimental Comparison of the Most Popular Approaches to Fake News Detection](https://mlsanity.com/paper/2603.25501/) (2026)
- [LLM-Enhanced Rumor Detection via Virtual Node Induced Edge Prediction](https://mlsanity.com/paper/2602.13279/) (2026)
- [Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMs](https://mlsanity.com/paper/2604.04177/) (2026)
- [Advancing Sentiment Analysis: A Novel LSTM Framework with Multi-head Attention](https://mlsanity.com/paper/2503.08079/) (2025)
- [LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade](https://mlsanity.com/paper/2509.07274/) (2025)
- [Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection](https://mlsanity.com/paper/2608.03627/) (2026)

## Abstract (arXiv)

The proliferation of misinformation on social media has raised significant societal concerns, necessitating robust detection mechanisms. Large Language Models such as GPT-4 and LLaMA2 have been envisioned as possible tools for detecting misinformation based on their advanced natural language understanding and reasoning capabilities. This paper conducts a comparison of LLM-based approaches to detecting misinformation between text-based, multimodal, and agentic approaches. We evaluate the effectiveness of fine-tuned models, zero-shot learning, and systematic fact-checking mechanisms in detecting misinformation across different topic domains like public health, politics, and finance. We also discuss scalability, generalizability, and explainability of the models and recognize key challenges such as hallucination, adversarial attacks on misinformation, and computational resources. Our findings point towards the importance of hybrid approaches that pair structured verification protocols with adaptive learning techniques to enhance detection accuracy and explainability. The paper closes by suggesting potential avenues of future work, including real-time tracking of misinformation, federated learning, and cross-platform detection models.