3HAN: A Deep Neural Network for Fake News Detection
This addresses the rapid spread of fake news, providing an automated and interpretable AI solution for detection, though it is incremental as it builds on existing attention-based methods.
The paper tackled the problem of fake news detection by proposing a three-level hierarchical attention network (3HAN) that processes articles hierarchically to assign differential importance to words, sentences, and headlines, achieving an accuracy of 96.77% on a large real-world dataset.
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.