3HAN: A Deep Neural Network for Fake News Detection
Sneha Singhania Nigel Fernandez Shrisha Rao
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.