Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies
It addresses the societal problem of misinformation detection for social media users and platforms, but is incremental as it compares existing methods without introducing new paradigms.
This paper compared LLM-based approaches for detecting misinformation on social media, evaluating methods like fine-tuning and zero-shot learning across topics such as public health and politics, and found that hybrid approaches combining structured verification with adaptive learning enhance accuracy and explainability.
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.