Date of Award:


Document Type:


Degree Name:

Master of Science (MS)


Computer Science


Kyumin Lee


Crowdsourcing systems enable new opportunities for requesters with limited funds to accomplish various tasks using human computation. However, the power of human computation is abused by malicious requesters who create malicious campaigns to manipulate information in web systems such as social networking sites, online review sites, and search engines. To mitigate the impact and reach of these malicious campaigns to targeted sites, we propose and evaluate a machine learning based classification approach for detecting malicious campaigns in crowdsourcing platforms as a first line of defense, and build a malicious campaign blacklist service for targeted site providers, researchers and users.

Specifically, we (i) conduct a comprehensive analysis to understand the characteristics of malicious campaigns and legitimate campaigns in crowdsourcing platforms, (ii) propose various features to distinguish between malicious campaigns and legitimate campaigns, (iii) evaluate a classification approach against baselines, and (iv) build a malicious campaign blacklist service. Our experimental results show that our proposed approaches effectively detect malicious campaigns with low false negative and false positive rates.