Cyberbullying is a complicated digital social media challenge that has intensified with the rapid spread of social media platforms. It takes many categories, including hate speech, verbal abuse. Due to the large volume of text generated daily, traditional methods based on rules or human review are no longer sufficient for accurate and effective detection of this phenomenon. The objective of this research is to evaluate the performance of a fine-tuned BERT model in multi-category cyberbullying detection and compare them with several traditional machine learning algorithms, including Support Vector Machine (SVM), logistic regression, and random forests. In this research we use a multi-category cyberbullying dataset, and the BERT model evaluated using performance metrics such as accuracy, precision, recall, and F1 score, The experimental showed that the BERT model achieved the best result overall performance metrics, where an accuracy is 0.8474, outperforming the conventional machine learning models such as SVM, RF and LR, the result of this research demonstrated the efficiency and ability of BERT in detection of multiclass cyberbullying, Furthermore this research illuminates the significant potential of transformer-based models in handling complex contextual and semantic information within social media cyberbullying texts.
Keywords
BERT for CyberbullyingCyberbullying DetectionDeep learning. Machine Learning ModelsTransformer Models.
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