Date of Award

4-27-2023

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Information Science

First Advisor

Nitin Agarwal

Abstract

The rise of social media has led to an alarming increase in harmful content, including abusive language, disrespect, and hate speech. While social media platforms have attempted to address this issue, harmful content cannot be completely eradicated before it impacts individuals or communities. In this thesis, we conduct a comparative analysis of toxicity on three social media platforms: Twitter, Parler, and Reddit. Using Reddit data, our study employs a tree structure to visualize and divide each conversation thread, allowing for an in-depth examination of the impact of toxic content on communities. We also used different machine learning algorithms to classify the toxicity of each leaf node, based on the toxicity of its parent and grandparents, as well as the average toxicity of the entire tree. Our approach can assist policymakers in detecting early signs of toxicity and diverting potentially harmful comments to less toxic directions. This study provides a comprehensive analysis of toxicity on social media platforms, allowing for a better understanding of the differences and similarities across platforms, and a deeper exploration of the impact of toxic content on individual communities. Our research offers valuable insights into the prevalence and impact of toxic content on social media platforms, and our methodology can be utilized in future studies to provide a more nuanced understanding of this complex issue.

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