  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.
