DOI
https://doi.org/10.25772/81FG-GP42
Defense Date
2014
Document Type
Thesis
Degree Name
Master of Science
Department
Computer Science
First Advisor
Carol Fung
Abstract
Twitter generates the majority of its revenue from advertising. Third parties pay to have their products advertised on Twitter through: tweets, accounts and trends. However, spammers can use Sybil accounts (fake accounts) [21] to advertise and avoid paying for it. Sybil accounts are highly active on Twitter performing advertising campaigns to serve their clients [5]. They aggressively try to reach a large audience to maximize their influence. These accounts have similar behavior if controlled by the same master. Most of their spam tweets include a shortened URL to trick users into clicking on it. Also, since they share resources with each other, they tend to tweet similar trending topics to attract a larger audience. However, some Sybil accounts do not spam aggressively to avoid being detected [22], rendering it difficult for traditional spam detectors to be effective in detecting low spamming Sybil accounts. In this paper, I investigate additional criteria to measure the similarity between accounts on Twitter. I propose an algorithm to define the correlation among accounts by investigating their tweeting habits and content. Given known labeled accounts by spam detectors, this approach can detect hidden accounts that are closely related to labeled accounts but are not detected by traditional spam detection approaches.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-19-2014