text: As businesses increasingly rely on social networking sites to engage with their customers, it is crucial to understand and counter reputation manipulation activities, including fraudulently boosting the number of Facebook page likes using like farms. To this end, several fraud detection algorithms have been proposed and some deployed by Facebook that use graph co-clustering to distinguish between genuine likes and those generated by farm-controlled profiles. However, as we show in this paper, these tools do not work well with stealthy farms whose users spread likes over longer timespans and like popular pages, aiming to mimic regular users. We present an empirical analysis of the graph-based detection tools used by Facebook and highlight their shortcomings against more sophisticated farms. Next, we focus on characterizing content generated by social networks accounts on their timelines, as an indicator of genuine versus fake social activity. We analyze a wide range of features extracted from timeline posts, which we group into two main classes: lexical and non-lexical. We postulate and verify that like farm accounts tend to often re-share content, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. We extract relevant lexical and non-lexical features and and use them to build a classifier to detect like farms accounts, achieving significantly higher accuracy, namely, at least 99 precision and 93 recall.
  @cite_32: Facebook pages offer an easy way to reach out to a very large audience as they can easily be promoted using Facebook's advertising platform. Recently, the number of likes of a Facebook page has become a measure of its popularity and profitability, and an underground market of services boosting page likes, aka like farms, has emerged. Some reports have suggested that like farms use a network of profiles that also like other pages to elude fraud protection algorithms, however, to the best of our knowledge, there has been no systematic analysis of Facebook pages' promotion methods. This paper presents a comparative measurement study of page likes garnered via Facebook ads and by a few like farms. We deploy a set of honeypot pages, promote them using both methods, and analyze garnered likes based on likers' demographic, temporal, and social characteristics. We highlight a few interesting findings, including 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.
summary: Finally, we stress that our prior work @cite_32 only presents an exploratory measurement study of like farms, based on the characteristics of the accounts that liked a few honeypot pages. Specifically, @cite_32 analyzes the geographic and demographic distribution of garnered likes, the temporal patterns observed for each campaign, as well as the social graph induced by the likers. Whereas, in this paper, we take a significant step further: although we re-use the honeypot campaigns to build a corpus of like farm users, (i) we demonstrate that temporal and social graph analysis can only be used to detect naive farms, and (ii) we introduce a timeline-based classifier that achieves a remarkably high degree of accuracy.