國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Detection of Cyberbullying in Social Media Texts Using Explainable Artificial Intelligence./
作者:	Islam, Mohammad Rafsun.
面頁冊數:	1 online resource (89 pages)
附註:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
標題:	Support vector machines. -
電子資源:	click for full text (PQDT)
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. - 1 online resource (89 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380269872Subjects--Topical Terms:

641425
Support vector machines.
Index Terms--Genre/Form:

554714
Electronic books.

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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