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Online conversations: A study of their toxicity
Alkhabaz, Ridha Monir A.
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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
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Graduate Dissertations and Theses at Illinois PRIMARY
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