Detection of Fraudulent Tweets: An Empirical Investigation Using Network Analysis and Deep Learning Technique

Jaewan Lim, Zhihui Liu, Lina Zhou
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引用次数: 1

Abstract

Social media has become a powerful and efficient platform for information diffusion. The increasing pervasiveness of social media use, however, has brought about the problems of fraudulent accounts that are intended to diffuse misinformation or malicious contents. Twitter recently released comprehensive archives of fraudulent tweets that are possibly connected to a propaganda effort of Internet Research Agency (IRA) on the 2016 U.S. presidential election. To understand information diffusion in fraudulent networks, we analyze structural properties of the IRA retweet network, and develop deep neural network models to detect fraudulent tweets. The structure analysis reveals key characteristics of the fraudulent network. The experiment results demonstrate the superior performance of the deep learning technique to a traditional classification method in detecting fraudulent tweets. The findings have potential implications for curbing online misinformation.
虚假推文的检测:使用网络分析和深度学习技术的实证调查
社交媒体已经成为一个强大而高效的信息传播平台。然而,社交媒体使用的日益普及带来了旨在传播错误信息或恶意内容的欺诈账户的问题。推特最近公开了可能与互联网研究机构(IRA)对2016年美国总统选举的宣传有关的虚假推文的综合档案。为了了解欺诈网络中的信息扩散,我们分析了IRA转发网络的结构特性,并开发了深度神经网络模型来检测欺诈推文。结构分析揭示了诈骗网络的主要特征。实验结果表明,深度学习技术在检测欺诈性推文方面优于传统的分类方法。这一发现对遏制网络虚假信息具有潜在的意义。
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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