Serf and turf: crowdturfing for fun and profit

By G. Wang, Christo Wilson, Xiaohan Zhao, Yibo Zhu, M. Mohanlal, Haitao Zheng, Ben Y. Zhao. 2011

250 citations recorded by the source corpus.

Popular Internet services in recent years have shown that remarkable things can be achieved by harnessing the power of the masses using crowd-sourcing systems. However, crowd-sourcing systems can also pose a real challenge to existing security mechanisms deployed to protect Internet services. Many of these security techniques rely on the assumption that malicious activity is generated automatically by automated programs. Thus they would perform poorly or be easily bypassed when attacks are generated by real users working in a crowd-sourcing system. Through measurements, we have found surprising evidence showing that not only do malicious crowd-sourcing systems exist, but they are rapidly growing in both user base and total revenue. We describe in this paper a significant effort to study and understand these "crowdturfing" systems in today's Internet. We use detailed crawls to extract data about the size and operational structure of these crowdturfing systems. We analyze details of campaigns offered and performed in these sites, and evaluate their end-to-end effectiveness by running active, benign campaigns of our own. Finally, we study and compare the source of workers on crowdturfing sites in different countries. Our results suggest that campaigns on these systems are highly effective at reaching users, and their continuing growth poses a concrete threat to online communities both in the US and elsewhere.

Subfields: Computer Science Applications, Information Systems, Management of Technology and Innovation, Evaluation & Benchmarks

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Citations in the corpus

Automatic Detection of Generated Text is Easiest when Humans are Fooled (2019, background): “, 2018), influences elections (Allcott and Gentzkow, 2017), and undermines user trust (Wang et al., 2012; Song et al., 2015).”
Automated Crowdturfing Attacks and Defenses in Online Review Systems (2017, background): “To boost their reputation or to damage that of a competitor, businesses can solicit fake reviews that express an overly positive or negative opinion about a business [7, 30, 74].”
Adapting Social Spam Infrastructure for Political Censorship (2012, background): “Despite the complexity of the attack, we show how Twitter’s relevance-based search helped mitigate the attack’s impact on users searching for information regarding the Russian election.”
Human and Automatic Detection of Generated Text (2019, background): “Aside from news, crowdturfing of fake reviews on websites such as Amazon and Yelp significantly undermines user trust (Wang et al., 2012; Song et al., 2015).”
CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks (2015, background): “[33] collect data from crowdturfing sites based in China, Zhubajie and Sandaha, and analyze their structures, scale, and the amount of money involved in it.”
EVILCOHORT: Detecting Communities of Malicious Accounts on Online Services (2015, background): “On the other hand, E VIL C OHORT can detect an account (or an IP address) as malicious even if the account does not post any content on the online service.”
Man vs. Machine: Practical Adversarial Detection of Malicious Crowdsourcing Workers (2014, methodology): “Today, crowdturfing campaigns often spam web services such as social networks, online review sites, and instantmessaging networks [48].”
Trafficking Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse (2013, methodology): “Prior to conducting our study, we worked with Twitter and our institutions to set down guidelines for interacting with merchants.”
Strength in Numbers: Robust Tamper Detection in Crowd Computations (2015, background): “Recent studies show that there are thriving underground “blackmarket” services, where human users or bots can be “hired” to create fake identities [27,39].”
Uncovering social network sybils in the wild (2011, background): “OSN profiles can also be commoditized by “cloning” them onto other Web sites [Wang et al. 2012].”
Understanding latent interactions in online social networks (2010, background): “Sybils are responsible for generating much of the spam on today’s OSNs [Gao et al. 2010; Grier et al. 2010; Yang et al. 2011; Thomas et al. 2011; Wang et al. 2012].”