Identification of Cyberbullies on Social Media
Keywords:
Cyberbullying detection, Decision trees, K-Nearest neighbors, Machine learning, Naive Bayes, Random forest, Social media, SVM, Text classificationAbstract
Cyberbullying on social media platforms has become a pervasive issue, causing significant emotional and psychological harm to users. This paper presents a web-based system designed to detect cyberbullying in social media posts using machine learning techniques. The system employs five supervised algorithms Support Vector Machine (SVM), Random Forest, Decision Tree, Naive Bayes, and K-Nearest Neighbors (KNN) to classify posts as bullying or non-bullying based on textual content. Enhanced text preprocessing, including normalization and feature extraction, improves detection accuracy. The system features a user-friendly web interface for users to submit posts and administrators to monitor and remove harmful content. Experimental results show that SVM achieves the highest accuracy at 92.5%, demonstrating its effectiveness for real-time cyberbullying detection. This work contributes to safer online environments by automating the identification of harmful posts.
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