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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.

This paper presents work on using continuous representations for authorship at-tribution. In contrast to previous work, which uses discrete feature representations , our model learns continuous representations for n-gram features via a neu-ral network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.

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