Detecting Changed-Hands Online Review Accounts
Geli Fei, Shuai Wang, Bing Liu, Leman Akoglu

TL;DR
This paper introduces a novel method for detecting online review accounts that have changed ownership, by analyzing content and style changes, effectively identifying accounts involved in spam activities.
Contribution
It presents a new detection algorithm for changed-hands accounts that leverages fine-grained feature selection and pinpoints change points within account histories.
Findings
High detection accuracy demonstrated in experiments
Effective identification of change points within account histories
Outperforms existing spam detection methods
Abstract
A reputable social media or review account can be a good cover for spamming activities. It has become prevalent that spammers buy/sell such accounts openly on the Web. We call these sold/bought accounts the changed-hands (CH) accounts. They are hard to detect by existing spam detection algorithms as their spamming activities are under the disguise of clean histories. In this paper, we first propose the problem of detecting CH accounts, and then design an effective detection algorithm which exploits changes in content and writing styles of individual accounts, and a proposed novel feature selection method that works at a fine-grained level within each individual account. The proposed method not only determines if an account has changed hands, but also pinpoints the change point. Experimental results with online review accounts demonstrate the high effectiveness of our approach.
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Taxonomy
TopicsSpam and Phishing Detection · Text and Document Classification Technologies · Authorship Attribution and Profiling
