In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

Niloofar Safi Samghabadi

will defend her dissertation

Automatic Detection of Nastiness and Early Signs of Cyberbullying Incidents on Social Media

Abstract

Although social media has made it easy for people to connect on a virtually unlimited basis, it has also opened doors to people who misuse it to bully others. Nowadays, abusive behavior and cyberbullying are considered as major issues in cyberspace that can seriously affect the mental and physical health of victims. However, due to the growing number of social media users, manual moderation of online content is impractical. Available automatic systems for hate speech and cyberbullying detection fail to make opportune predictions, which makes them ineffective for prevention. In this thesis, we aim at advancing new technology that will help to protect vulnerable online users. As a first approximation to this goal, we develop computational methods to automatically identify extremely aggressive texts. We further expand these methods and propose a Natural Language Processing system that constantly monitors online conversations, and triggers an alert when a possible case of cyberbullying is happening. We design a new evaluation framework and show that our system is able to provide timely and accurate cyberbullying predictions, based on limited evidence. In this research, we are more concerned about teens, since they are the most vulnerable group of users under online attacks. We introduce new data resources for both tasks of abusive language and cyberbullying detection from social media platforms that are specifically popular among youth.

Date: Thursday, April 30, 2020
Time: 1:30 - 2:30 PM
Place: Online Presentation - Zoom meeting
Register in advance for this meeting: https://zoom.us/meeting/register/tJMrcO2orz8rGtNDIkXCcSXN-kq0xhlz9aEF
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Advisor: Dr. Thamar Solorio

Faculty, students, and the general public are invited.