用社会网络分析法揭露假新闻

P. Shrivastava, Mayank Sharma, Megha Kamble, Vaibhav Gore, Avenash Kumar
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引用次数: 0

摘要

在社交媒体网络上获取事实的渠道很短,再加上其呈指数级上升的趋势,也使得人们很难区分虚假信息还是真实信息。通过共享方式的快速传播具有更高的质量和指数性。避免虚假事实的传播对社交媒体网络的可信度也至关重要。因此,它不断增加的研究任务是通过其来源,内容材料或作者来检查信息的错误陈述,并将未经验证的资产从传播谣言中拯救出来。本文展示了一种基于合成智能的完全方法,用于识别通过使用社交网络实体所做的虚假陈述。不同版本的深度神经网络正被应用于评估数据集,并查看虚假信息的存在。实现设置产生了99%的分类准确率,即使数据集被测试为具有多个时代的二进制(真实或虚假)标签。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncovering Fake News by Means of Social Network Analysis
The short access to facts on social media networks in addition to its exponential upward push also made it tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for misstatement of information thru its source, content material, or author and save you the unauthenticated assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for fake information presence. The implementation setup produced most volume 99% category accuracy, even as dataset is tested for binary (real or fake) labelling with multiple epochs.
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