Domain Adaptive Fake News Detection via Reinforcement Learning
arXiv:2202.08159 · doi:10.1145/3485447.3512258
Abstract
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.
arXiv commentAccepted to The ACM Web Conference (WWW) 2022
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Cited by in corpus (4)
- Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
- Robust Domain Misinformation Detection via Multi-modal Feature Alignment
- A Macro- and Micro-Hierarchical Transfer Learning Framework for Cross-Domain Fake News Detection
- Exposing and Explaining Fake News On-the-Fly