Abstract: Cyberbullying has arisen as an unavoidable and concerning issue via virtual entertainment stages, influencing the psychological well-being and prosperity of people around the world. To resolve this issue, this study proposes a cyberbullying recognition framework utilizing the K-SVM calculation. Utilizing the force of AI, the framework means to consequently distinguish and signal occurrences of cyberbullying progressively web-based entertainment content. The improvement of the location framework starts with the assortment and naming of a thorough dataset containing instances of cyberbullying and non-cyberbullying posts or remarks. After pre-handling the text information by eliminating unessential data, changing message over completely to lowercase, and tokenizing it, significant highlights are removed utilizing the pack of-words or TF-IDF methods. These changed element vectors act as contributions for preparing the K-SVM classifier, which tries to find the ideal hyper plane for successfully recognizing cyberbullying from non-cyberbullying content. The K-SVM model's performance is evaluated using a distinct testing dataset, with metrics such as exactness, accuracy, review, F1-score, and ROC-AUC broken down to determine its feasibility in identifying cyberbullying situations. Model calibrating is led through trial and error with different K-SVM hyper boundaries and cross-approval methods to upgrade the framework's exhibition.

Keywords: Cyberbullying, Support vector machine, Machine learning, social media, Classification.

Dr.E. Mohanraj, Eniyavan N, Sidarth S, Sridharan S, "Detection of Cyber Bullying on Social Media Using Machine learning", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13109.

PDF | DOI: 10.17148/IJARCCE.2024.13109

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