Date of Award:

5-2017

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Kyumin Lee

Committee

Kyumin Lee

Committee

Curtis Dyreson

Committee

Haitao Wang

Abstract

Crowdsourcing sites such as Mechanical Turk and Crowdflower provide a marketplace where requesters create tasks and recruit workers, who may perform certain tasks in order to get financial compensation. Anyone in the world can be a requester and/or a worker as long as he/she has the Internet connection. Crowdsourcing creates a new way to solve various tasks by using “human computation power”. However, crowdsourcing has been misused by malicious requesters and unethical workers for account generation, search engine optimization, content and link generation, ad posting and spam mailing, and social network linking. It creates new threats to the Web system. The consequences of the malicious tasks are receiving spam emails and spam messages from online social networks, polluted social links by fake link farming, and irrelevant search results because of manipulated web page link structure. Eventually, these have degraded the information trustworthiness on the Web. To solve this problem, we build a predictor that detects whether campaign/task in crowdsourcing sites is malicious or not so that malicious campaigns/tasks can be removed by crowdsourcing site providers as soon as they are created. In particular, we (i) analyze characteristics of malicious and legitimate campaigns; (ii) extract commonly available features from four crowdsourcing sites; and (iii) build predictors and evaluate their performance. Our experimental results show that our predictors are more effective and robust compared with several baselines. In the end, we design and build a malicious campaign blacklist service, which provides users with various information.

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