Astroturfing detection in social media: a binary n‐gram–based approach. (14th October 2016)

• Record Type: Journal Article
• Authors: Peng, Jian; Detchon, Sam; Choo, Kim‐Kwang Raymond; Ashman, Helen
• Other Names: Lengauer Christian guestEditor.; Bolten Matthias guestEditor.; Falgout Robert guestEditor.; Schenk Olaf guestEditor.; Zhou Xiaobo guestEditor.; Zhao Laiping guestEditor.
• Is Part Of: Concurrency and computation. Volume 29:Number 17(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-10-14
• Subjects: astroturfing -- authorship attribution -- n‐gram -- social media astroturfing -- user profiling; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4013
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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.