Measuring, Characterizing, and Detecting Facebook Like Farms
This addresses the issue of fraudulent popularity metrics on social media for businesses and platforms, offering an incremental improvement in detection methods.
The study tackled the problem of detecting artificially inflated Facebook page likes from like farms by comparing them to likes from Facebook advertising, finding that stealthy farms mimic regular users and existing detection algorithms fail against them. The researchers developed a classifier using lexical and non-lexical features from account timelines, achieving over 99% precision and 93% recall in detecting like farm accounts.
Social networks offer convenient ways to seamlessly 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, aka like farms, has emerged alongside Facebook's official targeted advertising platform. Nonetheless, 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 which 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 social activity. We analyze a range of features, grouped into two main categories: lexical and non-lexical. We find that like farm accounts tend to re-share content, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. Using relevant lexical and non-lexical features, we build a classifier to detect like farms accounts that achieves precision higher than 99% and 93% recall.