Cited by 197Spam and Phishing DetectionMobile Crowdsensing and CrowdsourcingCybercrime and Law Enforcement Studies

Serf and turf: crowdturfing for fun and profit

Wang, Gang, Wilson, Christo, Zhao, Xiaohan, Zhu, Yibo, Mohanlal, Manish, Zheng, Haitao, Zhao, Ben Y.·2012·Proceedings of the 21st international conference on World Wide Web

Abstract

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.

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Crossref · 10.1145/2187836.2187928

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  • Some providers generate fake popularity signals using automated bots, while others use realistic accounts and even crowdsourced real humans.

    “some providers generate fake popularity signals using automated bots (aka. sybils), but some others use realistic accounts and even crowdsourced real humans (aka. crowdturfing) [58, 83, 84, 93, 100].”

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References 24 · Cited by 197

Authors
Wang, G., Wilson, C., Zhao, X., Zhu, Y., Mohanlal, M., Zheng, H., & Zhao, B. Y.
Affiliations
University of California, Santa Barbara (US)
Venue
Proceedings of the 21st international conference on World Wide Web
Publisher
ACM
Published
2012-04-16
Pages
679-688
OpenAlex
W2127935984
Cited by
197
References
24
Citations by year
2026: 2 · 2025: 1 · 2024: 4 · 2023: 7 · 2022: 4 · 2021: 12 · 2020: 22 · 2019: 21 · 2018: 18 · 2017: 21 · 2016: 18 · 2015: 26 · 2014: 15 · 2013: 20 · 2012: 4
FWCI
16.35
Type
conference paper
Language
en
Open access
closed
Locations
International World Wide Web Conference (WWW) (publishedVersion)
Funders
  • National Science Foundation
  • National Research Foundation of Korea
Topics
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
  • Physical Sciences › Computer Science › Information Systems › Cybercrime and Law Enforcement Studies
Keywords
online communities, internet security, automated attacks, campaign effectiveness, web crawls

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