This research evaluates the effectiveness of Large Language Models (LLMs) like GPT-4 and LLaMA2 in detecting misinformation across different approaches.

• Text-based detection: Analyzes LLMs' capabilities to identify false claims from text alone
• Multimodal analysis: Examines how LLMs perform when processing both text and visual content
• Agentic approaches: Explores how LLM-powered agents can verify information through active inquiry

Security implications: As misinformation threatens social cohesion and institutional trust, this research provides crucial insights into how AI systems can be deployed as defensive tools in our digital information ecosystem.

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