Dynamic Analysis and Adaptive Discriminator for Fake News Detection
This addresses the problem of fake news spreading online, which threatens society, by improving detection flexibility and performance, though it appears incremental as it builds on existing multimodal methods.
The paper tackles fake news detection by proposing a Dynamic Analysis and Adaptive Discriminator (DAAD) approach that combines knowledge-based methods using Monte Carlo Tree Search for LLM prompt optimization and semantic-based methods with four deceit patterns, achieving superior results on three real-world datasets.
In current web environment, fake news spreads rapidly across online social networks, posing serious threats to society. Existing multimodal fake news detection methods can generally be classified into knowledge-based and semantic-based approaches. However, these methods are heavily rely on human expertise and feedback, lacking flexibility. To address this challenge, we propose a Dynamic Analysis and Adaptive Discriminator (DAAD) approach for fake news detection. For knowledge-based methods, we introduce the Monte Carlo Tree Search algorithm to leverage the self-reflective capabilities of large language models (LLMs) for prompt optimization, providing richer, domain-specific details and guidance to the LLMs, while enabling more flexible integration of LLM comment on news content. For semantic-based methods, we define four typical deceit patterns: emotional exaggeration, logical inconsistency, image manipulation, and semantic inconsistency, to reveal the mechanisms behind fake news creation. To detect these patterns, we carefully design four discriminators and expand them in depth and breadth, using the soft-routing mechanism to explore optimal detection models. Experimental results on three real-world datasets demonstrate the superiority of our approach. The code will be available at: https://github.com/SuXinqi/DAAD.