Authors : Victor Ojodomo Akoh; Fati Oiza Ochepa

Volume/Issue : Volume 9 - 2024, Issue 7 - July

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Google Scholar : https://tinyurl.com/4jvns5cv

Scribd : https://tinyurl.com/5bh2th6y

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL1058

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This study employed the stacking of three machine learning techniques: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression algorithms to develop a model for detecting cyberbullying using a post dataset acquired from the X Platform. The proposed model's task is to extract keywords from the post dataset and then classify them as either 1 ("cyberbullying word") or 0 ("not cyberbullying word"). The model generated an accuracy of 85.52%, and it was deployed using a simple Graphical User Interface (GUI) web application. This study recommends that the model be included on social media platforms to help reduce the growing use of cyberbullying phrases.

Keywords : Cyberbully, Machine Learning, Detection, Social Media.

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