论文标题
通过增强学习域自适应假新闻检测
Domain Adaptive Fake News Detection via Reinforcement Learning
论文作者
论文摘要
随着社交媒体是信息消费的主要力量，假新闻的加速传播已经为平台带来了新的挑战，可以区分合法新闻和假新闻。由于新闻领域的多样性和昂贵的注释成本，有效的虚假新闻检测是一项非平凡的任务。在这项工作中，我们通过将辅助信息（例如，用户注释和用户 - 新闻的交互）纳入一个新型增强学习模型，称为\ textbf {re} Inforced \ textbf {a a} daptbf \ textbf \ textbf {l textbf {l textbf} \ textbf {d} etection（real-fnd）。实时利用跨域和内域知识，尽管在不同的源域中接受了训练，但在目标域中使其在目标域中具有稳定性。对现实世界数据集的广泛实验说明了提出的模型的有效性，尤其是在目标域中可用的有限标记数据时。
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.
