Natural Language Processing (NLP) System for Cyberbullying Identification in Thai Language

In modern life, the easy access to the internet and social media has become almost essential. Although online social media channels are good means of communication, their frequency of cyberbullying and harassment creates problems as well. On these sites, cyberbullying takes many forms that cause targeted victims to experience anxiety, guilt, and despair. With an eye on demographic factors and personal experiences of victimizing, this study explores the degree and impressions of cyberbullying within a Thai sample community. By means of a thorough questionnaire survey, important new perspectives on respondents' awareness of cyberbullying, its underlying causes, and coping mechanisms for such events were obtained. Analyzing demographic data in tandem with cyberbullying experiences found a clear correlation between social media use length and the likelihood of running into cyberbullying. Moreover, the work suggests the creation of a customized natural language processing system specifically designed to identify Thai language cyberbullying events, therefore attaining an interesting accuracy rate of 84.23%. The study also points out the possible market chances for commercializing this method, implying it may be a profitable investment. These results provide a fresh understanding of language processing strategies for efficient content analysis on several internet platforms. Received: 16 September 2024 |Revised: 12 November 2024 | Accepted: 20 December 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Bello Musa Yakubu: Conceptualization, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization, Funding acquisition. Worapong Bumrungsri: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Pattarasinee Bhattarakosol: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – review & editing, Supervision, Project administration, Funding acquisition.

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Publication Details

Journal
Journal of Computational and Cognitive Engineering
Published
2025-02-13
DOI
https://doi.org/10.47852/bonviewjcce52024355
Citations
1
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
2.48

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article

Natural Language Processing (NLP) System for Cyberbullying Identification in Thai Language

Bello Musa Yakubu, Pattarasinee Bhattarakosol, Worapong Bumrungsri
1 citations
Journal of Computational and Cognitive Engineering
Hate Speech and Cyberbullying Detection
2.48
article

Natural Language Processing (NLP) System for Cyberbullying Identification in Thai Language

Bello Musa Yakubu, Pattarasinee Bhattarakosol, Worapong Bumrungsri
article en
1 citations

Abstract

In modern life, the easy access to the internet and social media has become almost essential. Although online social media channels are good means of communication, their frequency of cyberbullying and harassment creates problems as well. On these sites, cyberbullying takes many forms that cause targeted victims to experience anxiety, guilt, and despair. With an eye on demographic factors and personal experiences of victimizing, this study explores the degree and impressions of cyberbullying within a Thai sample community. By means of a thorough questionnaire survey, important new perspectives on respondents' awareness of cyberbullying, its underlying causes, and coping mechanisms for such events were obtained. Analyzing demographic data in tandem with cyberbullying experiences found a clear correlation between social media use length and the likelihood of running into cyberbullying. Moreover, the work suggests the creation of a customized natural language processing system specifically designed to identify Thai language cyberbullying events, therefore attaining an interesting accuracy rate of 84.23%. The study also points out the possible market chances for commercializing this method, implying it may be a profitable investment. These results provide a fresh understanding of language processing strategies for efficient content analysis on several internet platforms. Received: 16 September 2024 |Revised: 12 November 2024 | Accepted: 20 December 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Bello Musa Yakubu: Conceptualization, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization, Funding acquisition. Worapong Bumrungsri: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Pattarasinee Bhattarakosol: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – review & editing, Supervision, Project administration, Funding acquisition.

Journal of Computational and Cognitive EngineeringVol. 4(2)
Chulalongkorn University (TH)
Chulalongkorn University, Faculty of Science, Chulalongkorn University
Openalex Percentile: Top 12%
Hate Speech and Cyberbullying Detection
2.48
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