Astroturfing detection in social media: a binary n‐gram–based approach
Astroturfing detection in social media: a binary n‐gram–based approach
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DOI：
10.1002/cpe.4013
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Jian Peng;Sam Detchon;Kim-Kwang Raymond Choo;H. Ashman
中科院分区：
文献类型：
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作者：
Jian Peng;Sam Detchon;Kim-Kwang Raymond Choo;H. Ashman
Astroturfing出现在社交媒体的许多背景下，个人以许多不同的名字发布产品评论或政治评论，并且由于有意欺骗而引起关注。一个astroturfer工作的目的是使它看起来很多人持有相同的意见，促进共识的基础上astroturfer的意图。它通常是为了商业或政治利益，通常是由付费作家或意识形态驱动的作家。本文将作者归属的概念引入到astroturfing问题中，从公共社交媒体网站收集大量数据，并分析假定的个人作者，看看他们是否是同一个人。该分析包括一种二进制n-gram方法，该方法以前被证明可以有效地从同一作者的训练集上准确识别作者，而本文则展示了不同社交媒体上的作者是如何成为同一作者的。这一方法发现了许多显然由一个人操作多个账户的情况。
Astroturfing is appearing in numerous contexts in social media, with individuals posting product reviews or political commentary under a number of different names, and is of concern because of the intended deception. An astroturfer works with the aim of making it seem that a large number of people hold the same opinion, promoting a consensus based on the astroturfer's intentions. It is generally done for commercial or political advantage, often by paid writers or ideologically motivated writers. This paper brings the notion of authorship attribution to bear on the astroturfing problem, collecting quantities of data from public social media sites and analyzing the putative individual authors to see if they appear to be the same person. The analysis comprises a binary n‐gram method, which was previously shown to be effective at accurately identifying authors on a training set from the same authors, while this paper shows how authors on different social media turn out to be the same author. The method has identified numerous instances where multiple accounts are apparently being operated by a single individual.