论文标题
一种自动检测假新闻的深度学习方法
A Deep Learning Approach for Automatic Detection of Fake News
论文作者
论文摘要
假新闻检测是新闻领域中非常重要且重要的任务。到目前为止，在政治领域，这个具有挑战性的问题已经看到，但是在多域平台中确定它可能会更具挑战性。在本文中，我们提出了两个有效的模型，基于深度学习，以解决多个领域的在线新闻内容中的假新闻检测问题。我们在两个最近发布的数据集上评估了我们的技术，即fakenews amt和名人进行假新闻检测。拟议的系统产生令人鼓舞的性能，表现优于当前基于手工的特征工程的最先进系统，两种型号的显着利润分别为3.08％和9.3％。为了利用可用于相关任务的数据集，我们执行跨域分析（即在Fakenews AMT上接受培训的模型，对名人进行了测试，反之亦然），以探索整个域中系统的适用性。
Fake news detection is a very prominent and essential task in the field of journalism. This challenging problem is seen so far in the field of politics, but it could be even more challenging when it is to be determined in the multi-domain platform. In this paper, we propose two effective models based on deep learning for solving fake news detection problem in online news contents of multiple domains. We evaluate our techniques on the two recently released datasets, namely FakeNews AMT and Celebrity for fake news detection. The proposed systems yield encouraging performance, outperforming the current handcrafted feature engineering based state-of-the-art system with a significant margin of 3.08% and 9.3% by the two models, respectively. In order to exploit the datasets, available for the related tasks, we perform cross-domain analysis (i.e. model trained on FakeNews AMT and tested on Celebrity and vice versa) to explore the applicability of our systems across the domains.
