Dissertation/Thesis Abstract

Towards Network False Identity Detection in Online Social Networks
by Vallapu, Sai Krishna, M.S., Southern Illinois University at Edwardsville, 2016, 66; 10246101
Abstract (Summary)

In this research, we focus on identifying false identities in social networks. We performed a detailed study on different string matching techniques to identify user profiles with real or fake identity. In this thesis, we focus on a specific case study on sex offenders. Sex offenders are not supposed to be online on social networking sites in few states. To identify the existence of offenders in social networks, we ran experiments to compare datasets downloaded from Facebook and offender registries. To identify the most suitable string matching technique to solve this particular problem, we performed experiments on various methods and utilized the most appropriate technique, the Jaro-Winkler algorithm. The major contribution of our research is a weight based scoring function that is capable of identifying user records with full or partial data revealed in social networks. Based on our data samples created using metadata information of Facebook, we were able to identify the sex offender profiles with real identity and seventy percent of the sex offenders with partial information.

Indexing (document details)
Advisor: Gamage, Thoshitha
Commitee: McKenney, Mark, Yu, Xudong William
School: Southern Illinois University at Edwardsville
Department: Computer Science
School Location: United States -- Illinois
Source: MAI 56/03M(E), Masters Abstracts International
Subjects: Criminology, Web Studies, Computer science
Publication Number: 10246101
ISBN: 978-1-369-50587-0
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