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Fake News Detection Based on 3-HAN Architecture Using Deep Learning Techniques
Abstract
Fake news spreading is a common scenario we see nowadays in this modern era of social media and smart phones. According to the New York Times, fake news is described as “a made-up story with the intention of misleading, frequently with financial gain as the motive.” The problem is complex, given its varied interpretations across the globe. Hence, an effective system is needed to detect whether the given news is fake or real, and while doing a literature survey, I came across various research works that have been done to prevent the spread of fake news. In this article, we propose an approach that leverages machine learning algorithms and natural language processing to identify false or misleading information in news articles. Machine learning provides an effective solution to our problem, and in general, all systems use machine learning techniques in some way or another. In this article, the method of attention is introduced in NLP, and a new approach is introduced combined with machine learning algorithms. The approach is using a 3-layered HAN architecture, which is based on the three particles of a news article, that is, words, sentences, and paragraphs. In this project, the efficiency of this architecture is shown in the currently used models.
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