An Approach for Cyberbullying Detection on Social Media

Abstract: In the modern digital age, the proliferation of social media platforms has led to the spread of negative content, especially through images containing bad words or text containing bad content. To address this problem, our project aims to develop an intelligent system designed to detect illegal content in images. Using advanced machine learning techniques, including deep neural networks such as CNNs, we aim to create powerful models that can identify and classify illegal content and enable our model to recognize patterns in images embedded with text through extensive training, tackling the critical issue of cyberbullying by building intelligent system to detect illegal messages on social media posts. We build our website using the Python-based Django framework for efficiency and ease of use. Our plan is to create a safer online environment by combining technology and a user-centric approach.

Keywords: Cyberbullying detection, Convolutional neural networks (CNNs), MobileNet, Python-based Django framework, Optical character recognition (OCR), Machine Learning. Cite: Mr. M. Kishore Babu, K. Jayasri, K. Saran, K. Adithya, K. Harsha,"An Approach for Cyberbullying Detection on Social Media", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13319.