Online conversations: A study of their toxicity

Alkhabaz, Ridha Monir A.

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

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

Language

eng

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

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