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Utilizing Machine Learning for Detecting Cyber Bullying in Social Media

Author

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  • Saba Yousha

    (Department of Computer Information & Engineering, Mehran University of Engineering and Technology Jamshoro SINDH, Pakistan)

Abstract

The widespread dominance of the Internet and Electronic Media has made Social Media platforms a primary mode of communication. Unfortunately, these platforms have also become breeding grounds for harmful behavior, notably "Cyber Bullying," which involves using technology to inflict disrespect and harm on others. Despite various efforts by researchers to address this issue, the detection of such behavior remains crucial in combating this menace. This study aims to emphasize an effective approach for detecting cyberbullying on Social Media platforms. The findings indicate that the SVM (Support Vector Machine) classifier outperforms other classifiers in this context.We acquired tweet data from Twitter and used significant machine learning techniques to classify and forecast whether tweets are "offensive" or "non-offensive" and after that, using the Support Vector Machine'sAlgorithm, a machine learning-modelis prepared to detect Cyber Bullying on Social Media Platform.This research provide promising resultsto useML techniques for detection of Cyber Bullying.

Suggested Citation

  • Saba Yousha, 2023. "Utilizing Machine Learning for Detecting Cyber Bullying in Social Media," International Journal of Innovations in Science & Technology, 50sea, vol. 5(4), pages 760-772, December.
    • Handle: RePEc:abq:ijist1:v:5:y:2023:i:4:p:760-772
    as

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    File URL: https://journal.50sea.com/index.php/IJIST/article/view/598/1216
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    File URL: https://journal.50sea.com/index.php/IJIST/article/view/598
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    References listed on IDEAS

    as
    1. Amgad Muneer & Suliman Mohamed Fati, 2020. "A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter," Future Internet, MDPI, vol. 12(11), pages 1-20, October.
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