Domain Adaptive Fake News Detection via Reinforcement Learning
This work addresses the challenge of domain adaptation in fake news detection for social media platforms, which is incremental as it builds on existing automated models by adding reinforcement learning and auxiliary data.
The paper tackled the problem of fake news detection across different domains by proposing a reinforcement learning-based model called REAL-FND, which incorporates auxiliary information like user comments and interactions to improve robustness when trained in a source domain and applied to a target domain with limited labeled data, showing effectiveness in experiments on real-world datasets.
With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news. Effective fake news detection is a non-trivial task due to the diverse nature of news domains and expensive annotation costs. In this work, we address the limitations of existing automated fake news detection models by incorporating auxiliary information (e.g., user comments and user-news interactions) into a novel reinforcement learning-based model called \textbf{RE}inforced \textbf{A}daptive \textbf{L}earning \textbf{F}ake \textbf{N}ews \textbf{D}etection (REAL-FND). REAL-FND exploits cross-domain and within-domain knowledge that makes it robust in a target domain, despite being trained in a different source domain. Extensive experiments on real-world datasets illustrate the effectiveness of the proposed model, especially when limited labeled data is available in the target domain.