Astroturfing Detection in Social Media: Using Binary n-Gram Analysis for Authorship Attribution

• Published in: 2016 IEEE Trustcom/BigDataSE/ISPA pp. 121 - 128
• Main Authors: Jian Peng, Kim-Kwang Choo, Raymond, Ashman, Helen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2016
• Subjects: Astroturfing; Authorship Attribution; Context; Feature extraction; N-gram; Plagiarism; Social network services; Support vector machines; Syntactics; User Profiling; Writing
• ISSN: 2324-9013
• DOI: 10.1109/TrustCom.2016.0054

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 analysing 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.