Measuring, Characterizing, and Detecting Facebook Like Farms

作者：Muhammad Ikram, Lucky Onwuzurike, Shehroze Farooqi, Emiliano De Cristofaro, Arik Friedman, Guillaume Jourjon, Mohamed Ali Kâafar, Muhammad Shafiq · 发表于：ACM Transactions on Privacy and Security · 年份：2017 · DOI：10.1145/3121134 · 被引用次数：28 · 研究领域：Spam and Phishing Detection、Misinformation and Its Impacts、Cybercrime and Law Enforcement Studies

Online social networks offer convenient ways to reach out to large audiences. In particular, Facebook pages are increasingly used by businesses, brands, and organizations to connect with multitudes of users worldwide. As the number of likes of a page has become a de-facto measure of its popularity and profitability, an underground market of services artificially inflating page likes (“like farms ”) has emerged alongside Facebook’s official targeted advertising platform. Nonetheless, besides a few media reports, there is little work that systematically analyzes Facebook pages’ promotion methods. Aiming to fill this gap, we present a honeypot-based comparative measurement study of page likes garnered via Facebook advertising and from popular like farms. First, we analyze likes based on demographic, temporal, and social characteristics and find that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users’ behavior. Next, we look at fraud detection algorithms currently deployed by Facebook and show that they do not work well to detect stealthy farms that spread likes over longer timespans and like popular pages to mimic regular users. To overcome their limitations, we investigate the feasibility of timeline-based detection of like farm accounts, focusing on characterizing content generated by Facebook accounts on their timelines as an indicator of genuine versus fake so...