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.

提出了两种基于深度学习的模型，用于在多个领域的在线新闻内容中解决虚假新闻检测问题，并在 FakeNews AMT 和 Celebrity 两个数据集上评估，取得了很好的性能并超过了当前最先进的基于手工特征工程的系统。同时，进行了跨领域分析以探索系统在不同领域的适用性。