MSc Thesis Proposal Announcement of Muhammad Moeed Khalid:"Online Sexual Predator detection"

 

Date: Friday October 7, 2022 

Time:  10:00am – 11:30am 

Location: Essex Hall, Room 122

Reminder: Two-part attendance required: Part I (QR code) and Part II (sign in sheet)

 

Online sexual abuse is a very concerning yet severely overlooked vice of modern society. With more children being on the Internet and with the ever-increasing advent of web-applications such as online chatrooms and multiplayer games, preying on vulnerable users has become more accessible for predators. In recent years, there has been work on detecting online sexual predators using Machine Learning techniques. Such work has trained on severely imbalanced datasets, and imbalance is handled via manual trimming of over-represented labels. In this work, we propose a sampling approach that first tackles the problem of imbalance and then improves the effectiveness of the underlying classifiers. Our evaluation of the proposed sampling approach on PAN benchmark dataset shows performance improvements on several classification metrics, compared to prior methods that otherwise require hands-crafted sampling of the data. 

 

 

Keywords: Natural Language processing, Machine Learning, Deep Learning, Data Imbalance, Classification  

MSc Thesis Committee:  

Internal Reader:         Dr. Dima Alhadidi 

External Reader:        Dr. Jagdish Pathak 

Advisors:                    Dr. Alioune Ngom / Dr. Hossein Fani