3HAN: A Deep Neural Network for Fake News Detection

Autor:	Singhania, Sneha, Fernandez, Nigel, Rao, Shrisha
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computation and Language Computer Science - Social and Information Networks
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-319-70096-0_59
Popis:	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. Comment: Published as a conference paper at ICONIP 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.12014 Zobrazit plný text záznamu View this record from Arxiv