Aiding the Detection of Fake Accounts in Large Scale Social Online Services Qiang Cao † Michael Sirivianos ‡ Xiaowei Yang Tiago Pregueiro Duke University Telefonica Research Duke University Tuenti, Telefonica Digital
[email protected] [email protected] [email protected] [email protected] Abstract customer’s resources by making him pay for online ad Users increasingly rely on the trustworthiness of the clicks or impressions from or to fake profiles. Fake ac- information exposed on Online Social Networks (OSNs). counts can also be used to acquire users’ private contact In addition, OSN providersbase their business models on lists [17]. Sybils can use the “+1” button to manipulate the marketability of this information. However, OSNs Google search results [11] or to pollute location crowd- suffer from abuse in the form of the creation of fake ac- sourcing results [8]. Furthermore, fake accounts can be counts, which do not correspond to real humans. Fakes used to access personal user information [27] and per- can introduce spam, manipulate online rating, or exploit form large-scale crawls over social graphs [42]. knowledge extracted from the network. OSN operators The challenge. Due to the multitude of the reasons currently expend significant resources to detect, manu- behind their creation ( 2.1), real OSN Sybils manifest ally verify, and shut down fake accounts. Tuenti, the numerous and diverse§ profile features and activity pat- largest OSN in Spain, dedicates 14 full-time employees terns. Thus, automated Sybil detection (e.g., Machine- in that task alone, incurring a significant monetary cost. Learning-based) does not yield the desirable accuracy Such a task has yet to be successfully automated because ( 2.2).