Today social media has become the primary source for news. Via social media platforms, fake news travel at unprecedented speeds, reach global audiences and put users and communities at great risk. Therefore, it is extremely important to detect fake news as early as possible. Recently, deep learning based approaches have shown improved performance in fake news detection. However, the training of such models requires a large amount of labeled data, but manual annotation is time-consuming and expensive. Moreover, due to the dynamic nature of news, annotated samples may become outdated quickly and cannot represent the news articles on newly emerged events. Therefore, how to obtain fresh and high-quality labeled samples is the major challenge in employing deep learning models for fake news detection. In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. The annotator can automatically assign weak labels for unlabeled news based on users' reports. The reinforced selector using reinforcement learning techniques chooses high-quality samples from the weakly labeled data and filters out those low-quality ones that may degrade the detector's prediction performance. The fake news detector aims to identify fake news based on the news content. We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports. Extensive experiments on this dataset show that the proposed WeFEND model achieves the best performance compared with the state-of-the-art methods.

中文翻译：

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通过强化学习对假新闻检测进行弱监督

如今，社交媒体已成为新闻的主要来源。通过社交媒体平台，假新闻以前所未有的速度传播，覆盖全球受众，并使用户和社区面临巨大风险。因此，尽早发现假新闻极为重要。最近，基于深度学习的方法在假新闻检测方面表现出了改进的性能。然而，此类模型的训练需要大量标注数据，而人工标注耗时且成本高昂。此外，由于新闻的动态性，注释样本可能会很快过时，无法代表有关新出现事件的新闻文章。因此，如何获取新鲜、高质量的标记样本是采用深度学习模型进行假新闻检测的主要挑战。为了应对这一挑战，我们提出了一个强化的弱监督假新闻检测框架，即 WeFEND，它可以利用用户的报告作为弱监督来扩大假新闻检测的训练数据量。所提出的框架由三个主要组件组成：注释器、增强选择器和假新闻检测器。注释者可以根据用户的报告为未标记的新闻自动分配弱标签。使用强化学习技术的强化选择器从弱标记数据中选择高质量样本，并过滤掉那些可能会降低检测器预测性能的低质量样本。假新闻检测器旨在根据新闻内容识别假新闻。我们在通过微信公众号和相关用户报告发布的大量新闻文章中测试了所提出的框架。对该数据集的大量实验表明，与最先进的方法相比，所提出的 WeFEND 模型实现了最佳性能。