University of Illinois Urbana-Champaign

Online conversations: A study of their toxicity

Alkhabaz, Ridha Monir A.

Content Files
ALKHABAZ-THESIS-2024.pdf

Permalink

https://hdl.handle.net/2142/125603

Description

Title
Online conversations: A study of their toxicity
Author(s)
Alkhabaz, Ridha Monir A.
Issue Date
2024-07-16
Director of Research (if dissertation) or Advisor (if thesis)
Sundaram, Hari
Department of Study
Siebel Computing &DataScience
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • toxicity
  • online conversations
  • graph neural networks
  • turn-taking paths, terminating conversational structures
  • network science
Abstract
Social media platforms are essential spaces for modern human communication. There is a dire need to make these spaces most welcoming and engaging to their participants. A potential threat to this need is the propagation of toxic content in online spaces. Hence, it becomes crucial for social media platforms to detect early signs of a toxic conversation. In this work, we tackle the problem of toxicity prediction by proposing a definition for conversational structures. This definition empowers us to provide a new framework for toxicity prediction. Thus, we examine more than 1.18 million X (made by 4.4 million users), formerly known as Twitter, threads to provide a few key insights about the current state of online conversations. Our results indicated that most of the X threads do not exhibit a conversational structure. Also, our newly defined structures are distributed differently than previously thought of online conversations. Additionally, our definitions give a meaningful sign for models to start predicting the future toxicity of online conversations. We also showcase that message-passing graph neural networks outperform state-of-the-art gradient-boosting trees for toxicity prediction. Most importantly, we find that once we observe the first two terminating conversational structures, we can predict the future toxicity of online thread with ≈ 88 % accuracy. We hope our findings will help social media platforms better curate content in their spaces and promote more conversations in online spaces.
Graduation Semester
2024-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/125603
Copyright and License Information
Copyright 2024 Ridha Alkhabaz

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois