A Novel Method for Detecting Fake news: Deep Learning Based on Propagation Path Concept

F. Torgheh, M. Keyvanpour, B. Masoumi, S. V. Shojaedini
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引用次数: 2

Abstract

In the modern world, social media are extensively used for the purpose of communication, business and education. Although ease of use and simple accessibility to social media has expanded their applications, but unfortunately, they are associated with potential dangers which may negatively influence users. As main item, the publication of fake news can negatively affect various aspects of life (political, social, economic, etc.), therefore researchers have studied various methods to address the fake news detection. One way to check and detect fake news is to use the available features in news propagation path, news publisher and users. In this paper, an attempt has been made to investigate fake news detection based on these features and a proposed deep neural network model.
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一种新的假新闻检测方法:基于传播路径概念的深度学习
在现代世界,社交媒体被广泛用于交流、商业和教育。虽然社交媒体的易用性和简单可访问性扩大了它们的应用范围,但不幸的是,它们与可能对用户产生负面影响的潜在危险相关联。作为主要项目,假新闻的发布会对生活的各个方面(政治,社会,经济等)产生负面影响,因此研究人员研究了各种方法来解决假新闻检测问题。检查和检测假新闻的一种方法是利用新闻传播路径、新闻发布者和用户的可用特征。本文尝试基于这些特征和提出的深度神经网络模型来研究假新闻检测。
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