Fake news detection using discourse segment structure analysis

2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Pub Date : 2020-01-01 DOI:10.1109/Confluence47617.2020.9058106

Anmol Uppal, Vipul Sachdeva, Seema Sharma

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引用次数: 15

Abstract

Online news platforms greatly influence our society and culture in both positive and negative ways. As online media becomes more dependent for source of information, a lot of fake news is posted online, that widespread with people following it without any prior or complete information of event authenticity. Such misinformation has the potential to manipulate public opinions. The exponential growth of fake news propagation have become a great threat to public for news trustworthiness. It has become a compelling issue for which discovering, examining and dealing with fake news has increased in demand. However, with the limited availability of literature on the issue of uncovering fake news, a number of potential methodologies and techniques remains unexplored. The primary aim of this paper is to review existing methodologies, to propose and implement a method for automated deception detection. The proposed methodology uses deep learning in discourse-level structure analysis to formulate the structure that differentiates fake and real news. The baseline model achieved 74% accuracy.

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基于语段结构分析的假新闻检测

网络新闻平台对我们的社会和文化产生了积极和消极的影响。随着网络媒体对信息来源的依赖程度越来越高，大量的假新闻在网上发布，人们在没有事先或完整的事件真实性信息的情况下广泛关注。这种错误信息有可能操纵公众舆论。虚假新闻传播呈指数级增长，已成为公众对新闻可信度的巨大威胁。发现、检查和处理假新闻的需求日益增加，这已成为一个引人注目的问题。然而，由于关于揭露假新闻问题的文献有限，许多潜在的方法和技术仍未得到探索。本文的主要目的是回顾现有的方法，提出并实现一种自动欺骗检测方法。所提出的方法在话语级结构分析中使用深度学习来制定区分假新闻和真实新闻的结构。基线模型达到了74%的准确率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)

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