Abstract

Abstract: As the number of likes of a Facebook page provides a measure of its seeming popularity and profitability, an underground market of services has emerged that aim to boost page likes. In this talk, we aim to shed light on the “like farms” ecosystem, presenting three sets of results. First, we report on a honeypot-based measurement study: we analyze likes garnered using, respectively, Facebook ads and farms, and highlight 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 much stealthier approach. We then take a look at existing graph-based fraud detection algorithms (including those currently deployed by Facebook), showing that stealthy farms successfully evade detection by spreading likes over longer timespans and by liking many popular pages to mimic normal users. Finally, we analyze features extracted from timeline posts. We find that like farm accounts tend to more often re-share content, use fewer words and poorer vocabulary, target fewer topics, and generate more (often duplicate) comments and likes compared to normal users. Using these timeline-based features, we experiment with machine learning algorithms to detect like farms accounts, obtaining appreciably high accuracy (as high as 99% precision and 97% recall).

Bio: Emiliano De Cristofaro is a Senior Lecturer at University College London (UCL). Prior to joining UCL in 2013, he was a research scientist at PARC (a Xerox company). In 2011, he received a PhD in Networked Systems from the University of California, Irvine, advised (mostly while running on the beach) by Gene Tsudik. His research interests include privacy technologies, applied cryptography, privacy and security measurements. He has served as program co-chair of the Privacy Enhancing Technologies Symposium (PETS) in 2013 and 2014, and of the Workshop on Genome Privacy and Security (GenoPri 2015). His ugly, yet up-to-date, homepage is available at https://emilianodc.com