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Detection of Cyberbullying in Social Media Texts Using Explainable Artificial Intelligence.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Detection of Cyberbullying in Social Media Texts Using Explainable Artificial Intelligence./
Author:	Islam, Mohammad Rafsun.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	89 p.
Notes:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
Subject:	Web studies. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30565940
ISBN:	9798380269872

Detection of Cyberbullying in Social Media Texts Using Explainable Artificial Intelligence.
Islam, Mohammad Rafsun.

Detection of Cyberbullying in Social Media Texts Using Explainable Artificial Intelligence. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 89 p.

Source: Masters Abstracts International, Volume: 85-03.

Thesis (M.Sc.)--Queen's University (Canada), 2023.

This item must not be sold to any third party vendors.

The widespread use of social media has opened the door to new forms of harassment and abuse, such as cyberbullying, that have a serious impact on individuals’ psychological health, especially children and teenagers. Therefore, research communities have recently paid attention to developing detection approaches using Natural Language Processing (NLP) combined with machine learning algorithms to identify instances of cyberbullying in social media texts such as comments, posts, and messages. Those approaches have successfully classified the social media text as either cyberbullying or non-cyberbullying. However, they are unable to determine the type of cyberbullying and the reasons why victims may be targeted based on certain characteristics. The aim of this thesis is to develop a novel detection approach that can identify the type of cyberbullying based on characteristics such as gender, religion, age, and ethnicity. This thesis has accomplished this objective by utilizing an Explainable Artificial Intelligence (XAI) technology called Local Interpretable Model-agnostic Explanations (LIME) to justify and explain the classification of text as cyberbullying. LIME enables machine learning models to capture and highlight the most influential words that affect the decision to classify a text as cyberbullying. Those influential words are utilized to re-label and update the training data. The machine learning models are then re-trained using the updated data. To evaluate theperformance of the proposed approach, a simulation experiment has been conducted on a large dataset containing comments and posts from Twitter. Simulation results show that: 1) LIME provides reliable and convincing justifications and explanations for classifying a text as cyberbullying; 2) LIME enables machine learning to identify the type of cyberbullying based on characteristics such as gender, religion, age, and ethnicity; and 3) LIME improves the performance of the machine learning models in terms of classification accuracy.

ISBN: 9798380269872Subjects--Topical Terms:

1148502
Web studies.

Detection of Cyberbullying in Social Media Texts Using Explainable Artificial Intelligence.
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520 $a The widespread use of social media has opened the door to new forms of harassment and abuse, such as cyberbullying, that have a serious impact on individuals’ psychological health, especially children and teenagers. Therefore, research communities have recently paid attention to developing detection approaches using Natural Language Processing (NLP) combined with machine learning algorithms to identify instances of cyberbullying in social media texts such as comments, posts, and messages. Those approaches have successfully classified the social media text as either cyberbullying or non-cyberbullying. However, they are unable to determine the type of cyberbullying and the reasons why victims may be targeted based on certain characteristics. The aim of this thesis is to develop a novel detection approach that can identify the type of cyberbullying based on characteristics such as gender, religion, age, and ethnicity. This thesis has accomplished this objective by utilizing an Explainable Artificial Intelligence (XAI) technology called Local Interpretable Model-agnostic Explanations (LIME) to justify and explain the classification of text as cyberbullying. LIME enables machine learning models to capture and highlight the most influential words that affect the decision to classify a text as cyberbullying. Those influential words are utilized to re-label and update the training data. The machine learning models are then re-trained using the updated data. To evaluate theperformance of the proposed approach, a simulation experiment has been conducted on a large dataset containing comments and posts from Twitter. Simulation results show that: 1) LIME provides reliable and convincing justifications and explanations for classifying a text as cyberbullying; 2) LIME enables machine learning to identify the type of cyberbullying based on characteristics such as gender, religion, age, and ethnicity; and 3) LIME improves the performance of the machine learning models in terms of classification accuracy.
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