Unveiling Fake Accounts at the Time of Registration: An Unsupervised Approach

Abstract

Online social networks (OSNs) are plagued by fake accounts. Existing fake account detection methods either require a manually labeled training set, which is time-consuming and costly, or rely on rich information of OSN accounts, e.g., content and behaviors, which incurs significant delay in detecting fake accounts. In this work, we propose UFA (Unveiling Fake Accounts) to detect fake accounts immediately after they are registered in an unsupervised fashion. First, through a measurement study on the registration patterns on a real-world registration dataset, we observe that fake accounts tend to cluster on outlier registration patterns, e.g., IP and phone numbers. Then, we design an unsupervised learning algorithm to learn weights for all registration accounts and their features that reveal outlier registration patterns. Next, we construct a registration graph to capture the correlation between registration accounts, and utilize a community detection method to detect fake accounts via analyzing the registration graph structure. We evaluate UFA using real-world WeChat datasets. Our results demonstrate that UFA achieves a precision 94% with a recall ~80%, while a supervised variant requires 600K manual labels to obtain the comparable performance. Moreover, UFA has been deployed by WeChat to detect fake accounts for more than one year. UFA detects 500K fake accounts per day with a precision ~93% on average, via manual verification by the WeChat security team.