CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit
It addresses misinformation detection for social media platforms, offering a more accurate and scalable solution, though it appears incremental as it builds on existing retrieval and network methods.
The paper tackled fake news detection on Reddit by combining language models with a political knowledge base and social network analysis, achieving an 11% increase in F1-score over state-of-the-art methods on a dataset of over 200,000 posts.
Fake news threatens democracy and exacerbates the polarization and divisions in society; therefore, accurately detecting online misinformation is the foundation of addressing this issue. We present CrediRAG, the first fake news detection model that combines language models with access to a rich external political knowledge base with a dense social network to detect fake news across social media at scale. CrediRAG uses a news retriever to initially assign a misinformation score to each post based on the source credibility of similar news articles to the post title content. CrediRAG then improves the initial retrieval estimations through a novel weighted post-to-post network connected based on shared commenters and weighted by the average stance of all shared commenters across every pair of posts. We achieve 11% increase in the F1-score in detecting misinformative posts over state-of-the-art methods. Extensive experiments conducted on curated real-world Reddit data of over 200,000 posts demonstrate the superior performance of CrediRAG on existing baselines. Thus, our approach offers a more accurate and scalable solution to combat the spread of fake news across social media platforms.