International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Language Matters: A Systemic Functional Linguistics-Enhanced Machine Learning Framework for Cyberbullying Detection

  • Raghad Altowairgi (College of Computer Science and Engineering, University of Jeddah) ;
  • Ala Eshamwi (Faculty College of Computer Science and Engineering, University of Jeddah) ;
  • Lobna Hsairi (Faculty College of Computer Science and Engineering, University of Jeddah)
  • 투고 : 2023.09.05
  • 발행 : 2023.09.30
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초록

Cyberbullying is a growing problem among adolescents and can have serious psychological and emotional consequences for the victims. In recent years, machine learning techniques have emerged as promising approach for detecting instances of cyberbullying in online communication. This research paper focuses on developing a machine learning models that are able to detect cyberbullying including support vector machines, naïve bayes, and random forests. The study uses a dataset of real-world examples of cyberbullying collected from Twitter and extracts features that represents the ideational metafunction, then evaluates the performance of each algorithm before and after considering the theory of systemic functional linguistics in terms of precision, recall, and F1-score. The result indicates that all three algorithms are effective at detecting cyberbullying with 92% for naïve bayes and an accuracy of 93% for both SVM and random forests. However, the study also highlights the challenges of accurately detecting cyberbullying, particularly given the nuanced and context-dependent nature of online communication. This paper concludes by discussing the implications of these findings for future research and the development of practical tool for cyberbullying prevention and intervention.

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과제정보

The authors would like to express their cordial thanks to Dr. Sawsan Aljahdali for her valuable advice.

참고문헌

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