An Unreliable news is any piece of information which is false or misleading, deliberately spread to promote political, ideological and financial agendas. Recently the problem of unreliable news has got a lot of attention as the number instances of using news and social media outlets for propaganda have increased rapidly. This poses a serious threat to society, which calls for technology to automatically and reliably identify unreliable news sources. This paper is an effort made in this direction to build systems for detecting unreliable news articles. In this paper, various NLP algorithms were built and evaluated on Unreliable News Data 2017 dataset. Variants of hierarchical attention networks (HAN) are presented for encoding and classifying news articles which achieve the best results of 0.944 ROC-AUC. Finally, Attention layer weights are visualized to understand and give insight into the decisions made by HANs. The results obtained are very promising and encouraging to deploy and use these systems in the real world to mitigate the problem of unreliable news.

本文旨在建立一个能够自动可靠辨别可靠性的消息来源的技术系统。通过不断改进的自然语言处理算法，本文最终设计出一种基于层次注意网络的模型，此模型在Unreliable News Data 2017数据集上取得了0.944的ROC-AUC，引领了机器检测不可信消息的新发展方向。

信息时代名为“关注”的不可靠新闻检测技术