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Cyberbullying Detection on Social Media Using Machine Learning

V. MAGESWARI., MCA

 (Assistant Professor, Master of Computer Applications)

G. THINAKARAN., MCA

Christ College of Engineering and Technology

Moolakulam, Oulgaret Municipality, Puducherry – 605010.

Abstract

The rapid expansion of social media has transformed online communication, allowing users to share thoughts and opinions instantly. Along with these benefits, social platforms have also become a space where cyberbullying and abusive language spread quickly, causing serious emotional and psychological harm. Because social media content is large in volume, short in length, and highly informal, identifying harmful messages manually is both time-consuming and unreliable[1][2][6].This paper presents a machine learning–based system for detecting cyberbullying in social media text. The proposed approach processes user-generated content using Natural Language Processing techniques such as tokenization, stop-word removal, and text normalization[10][11]. The cleaned text is converted into numerical features using the TF–IDF method and classified using a Random Forest algorithm to determine whether the content is bullying or non-bullying[14][15]. A Flask-based web application is developed to provide real-time prediction through a simple and user-friendly interface[12]. The results show that the system can effectively identify harmful messages from short social media posts, making it a practical tool for improving online safety and supporting automated content moderation[1][5].

Keywords

Cyberbullying detection, social media analysis, machine learning, natural language processing, TF–IDF, Random Forest, text classification.