Mc-DNN: Fake News Detection Using Multi-Channel Deep Neural Networks

IF 4.1 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI:10.4018/ijswis.295553

Jitendra V. Tembhurne, Md. Moin Almin, Tausif Diwan

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引用次数: 25

Abstract

With the advancement of technology, social media has become a major source of digital news due to its global exposure. This has led to an increase in spreading fake news and misinformation online. Humans cannot differentiate fake news from real news because they can be easily influenced. A lot of research work has been conducted for detecting fake news using Artificial Intelligence and Machine Learning. A large number of deep learning models and their architectural variants have been investigated and many websites are utilizing these models directly or indirectly to detect fake news. However, state-of-the-arts demonstrate the limited accuracy in distinguishing fake news from the original news. We propose a multi-channel deep learning model namely Mc-DNN, leveraging and processing the news headlines and news articles along different channels for differentiating fake or real news. We achieve the highest accuracy of 99.23% on ISOT Fake News Dataset and 94.68% on Fake News Data for Mc-DNN. Thus, we highly recommend the use of Mc-DNN for fake news detection.

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Mc-DNN:使用多通道深度神经网络检测假新闻

随着科技的进步，社交媒体因其全球曝光而成为数字新闻的主要来源。这导致了虚假新闻和错误信息在网上传播的增加。人类无法区分假新闻和真实新闻，因为它们很容易受到影响。在利用人工智能和机器学习检测假新闻方面已经进行了大量的研究工作。人们已经研究了大量的深度学习模型及其架构变体，许多网站正在直接或间接地利用这些模型来检测假新闻。然而，最先进的技术表明，区分假新闻和真实新闻的准确性有限。我们提出了一个多渠道深度学习模型，即Mc-DNN，利用和处理不同渠道的新闻标题和新闻文章，以区分假新闻和真实新闻。我们在ISOT假新闻数据集上达到了99.23%的最高准确率，在Mc-DNN假新闻数据集上达到了94.68%的最高准确率。因此，我们强烈推荐使用Mc-DNN进行假新闻检测。

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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来源期刊

International Journal on Semantic Web and Information Systems 工程技术-计算机：人工智能

CiteScore

6.20

自引率

12.50%

发文量

审稿时长

20 months

期刊介绍： The International Journal on Semantic Web and Information Systems (IJSWIS) promotes a knowledge transfer channel where academics, practitioners, and researchers can discuss, analyze, criticize, synthesize, communicate, elaborate, and simplify the more-than-promising technology of the semantic Web in the context of information systems. The journal aims to establish value-adding knowledge transfer and personal development channels in three distinctive areas: academia, industry, and government.