Cyberbullying Detection on Social Media using AI
A. R. Jariya Begum · International Journal for Research in Applied Science and Engineering Technology · 2025
Cyberbullying on social media platforms has emerged as a critical societal challenge, causing significant psychological and emotional harm to individuals, particularly among vulnerable populations. Early detection and prevention are crucial to mitigating its impact. This paper presents a Natural Language Processing (NLP)- based approach for the automated detection of cyberbullying in user-generated content on social media. We propose a system that preprocesses textual data through techniques such as tokenization, stemming, and stopword removal, and subsequently transforms it using feature extraction methods like TFIDF and word embeddings. A supervised machine learning model, trained on annotated datasets, classifies online comments into cyberbullying and non- cyberbullying categories. Our experimental results demonstrate that NLP techniques, when combined with appropriate machine learning algorithms such as Logistic Regression and Support Vector Machines (SVM), achieve high accuracy in identifying harmful content. The proposed framework offers a scalable solution to assist social media platforms in monitoring user behavior, ensuring safer online environments, and fostering positive digital interactions. Future work includes enhancing detection capabilities through deep learning methods and expanding the system to multilingual contexts.