Paper:
Gang Wang, Christo Wilson, Xiaohan Zhao, Yibo Zhu, Manish Mohanlal, Haitao Zheng, and Ben Y. Zhao. 2012. Serf and turf: crowdturfing for fun and profit
Discussion Leader:
Lawrence Warren
Summary:
Remarkable things can be done with the internet at your side but of course with great power comes great criminal activity. Yes it is true that crowd sourcing systems pose a unique threat to security mechanisms due to the nature of how security is approached and this paper points out the existence of malicious crowd sourcing systems. Due to the crowd sourcing systems astroturfing (refers to information dissemination
campaigns that are sponsored by an organization, but are obfuscated so as to appear spontaneous) nature, they are referred to as crowdturf systems and more specifically they are defined as systems where a customer can initiate a campaign and users receive money for completing tasks which go against accepted user policies. There are two types of crowdturfing structures described in the paper which are distributed and centralized and both of these structures need three key actors in order to operate (customers, agents, workers).
Distributed structure is organized around small groups hosted by the group leader and is resistant to external threats and are easy to dissolve and redeploy but is not popular due to the fragmented nature and lack of accountability.
Centralized structure is more like Mechanical Turk and is more streamlined and popular because of this. It is also because of the open availability of system which allows for infiltration.
Well known crowdturfing sites are running strong and is a global issue and several have adopted Paypal which extends their reach to more countries than the one they are run within.
Reflections:
This paper shows the darker side of crowd source work and the results are astonishing. Millions of dollars have been spent by companies in order to bypass spam filters in order to get more exposure and crowdturfing seems to be the new way to spread information. Sites like this are not afraid of legal backlash and have increased in popularity despite threats from law enforcement. Large information cascades have been pushed and have gone around most filters designed to catch automatized spam meanwhile it is the clicks from users which is the end goal.
Questions:
- Will machine learning need to be advanced in order to filter out spam from crowdturfing?
- Is there any fault of users when it comes to the success of the such systems?
- Why do you think Weibo and ZBJ was used as opposed to twitter and Mechanical Turk (other than research location)?
- In this growing industry are there any negative results which can be seen from a company’s point of view?
