You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 

[2]: Since Twitter has emerged as one of the easiest ways of reaching people, companies started using it to advertise their products. However, creating a functional network of followers to whom to promote content is not a straightforward task. On the one side, collecting followers requires time. On the other side, companies need to establish a reputation to motivate users to follow them. A number of websites have emerged to help Twitter users create a large network of followers. These websites promise their subscribers to provide followers in exchange for a fee or limited services free of charge but in exchange for the user's Twitter account credentials. In addition, they offer to spread their clients' promotional messages in the network. In this paper, we study the phenomenon of these Twitter Account Markets, and we show how their services are often linked to abusive behavior and compromised Twitter profiles.

[3]: As web services such as Twitter, Facebook, Google, and Yahoo now dominate the daily activities of Internet users, cyber criminals have adapted their monetization strategies to engage users within these walled gardens. To facilitate access to these sites, an underground market has emerged where fraudulent accounts - automatically generated credentials used to perpetrate scams, phishing, and malware - are sold in bulk by the thousands. In order to understand this shadowy economy, we investigate the market for fraudulent Twitter accounts to monitor prices, availability, and fraud perpetrated by 27 merchants over the course of a 10-month period. We use our insights to develop a classifier to retroactively detect several million fraudulent accounts sold via this marketplace, 95 of which we disable with Twitter's help. During active months, the 27 merchants we monitor appeared responsible for registering 10-20 of all accounts later flagged for spam by Twitter, generating $127-459K for their efforts.
Thomas al [3] analyzed trafficking of fake accounts in Twitter. They bought fake profiles from 27 merchants and developed a classifier to detect these fake accounts. In a similar study, Stringhini al [2] [1] analyzed the market of Twitter followers , which, akin to Facebook like farms, provide Twitter followers for sale. Note that Twitter follower markets differ from Facebook like farms as Twitter entails a follower-followee relationship among users, while Facebook friendships imply a bidirectional relationships. Also, there is no equivalent of liking a Facebook page in the Twitter ecosystem.