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arXiv · 2306.12014

3HAN: A Deep Neural Network for Fake News Detection

Sneha Singhania·Nigel Fernandez·Shrisha Rao
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Abstract

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.

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Sneha Singhania, Nigel Fernandez, Shrisha Rao. 2023-06-21. 3HAN: A Deep Neural Network for Fake News Detection. https://doi.org/10.1007/978-3-319-70096-0_59

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