Generate First, Then Sample: Enhancing Fake News Detection with LLM-Augmented Reinforced Sampling

Abstract

The spread of fake news on online platforms has long been a pressing concern. Considering this, extensive efforts have been made to develop fake news detectors. However, a major drawback of these models is their relatively low performance—lagging by more than 20%—in identifying *fake* news compared to *real* news, making them less suitable for practical deployment. This gap is likely due to an imbalance in the dataset and the model’s inadequate understanding of data distribution on the targeted platform. In this work, we focus on improving the model’s effectiveness in detecting *fake* news. To achieve this, we **first** adopt an LLM to **generate** fake news in three different styles, which are later incorporated into the training set to augment the representation of fake news. **Then**, we apply Reinforcement Learning to dynamically **sample** fake news, allowing the model to learn the optimal real-to-fake news ratio for training an effective fake news detector on the targeted platform. This approach allows our model to perform effectively even with a limited amount of annotated news data and consistently improve detection accuracy across different platforms. Experimental results demonstrate that our approach achieves state-of-the-art performance on two benchmark datasets, improving *fake* news detection performance by 24.02% and 11.06% respectively.