Date of Award:

5-2016

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Advisor/Chair:

Kyumin Lee

Abstract

In online social networking sites, gaining popularity has become important. The more popular a company is, the more profits it can make. A way to measure a company's popularity is to check how many likes it has (e.g., the company's number of likes in Facebook). To instantly and artificially increase the number of likes, some companies and business people began hiring crowd workers (aka fake likers) who send likes to a targeted page and earn money. Unfortunately, little is known about characteristics of the fake likers and how to identify them. To uncover fake likers in online social networks, in this work we (i) collect profiles of fake likers and legitimate likers by using linkage and honeypot approaches, (ii) analyze characteristics of fake likers and legitimate likers, (iii) propose and develop a fake liker detection approach, and (iv) thoroughly evaluate its performance against three baseline methods and under two attack models. Our experimental results show that our cassification model significantly outperformed the baseline methods, achieving 87.1% accuracy and 0.1 false positive rate and 0.14 false negative rate.

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