The rapid spread of fake news is a serious problem calling for AI solutions.
We employ a deep learning based automated detector through a three level
hierarchical attention network (3HAN) for fast, accurate detection of fake
news. 3HAN has three levels, one each for words, sentences, and the headline,
and constructs a news vector: an effective representation of an input news
article, by processing an article in an hierarchical bottom-up manner. The
headline is known to be a distinguishing feature of fake news, and
furthermore, relatively few words and sentences in an article are more
important than the rest. 3HAN gives a differential importance to parts of an
article, on account of its three layers of attention. By experiments on a
large real-world data set, we observe the effectiveness of 3HAN with an
accuracy of 96.77\%. Unlike some other deep learning models, 3HAN provides an
understandable output through the attention weights given to different parts
of an article, which can be visualized through a heatmap to enable further
manual fact checking.