A Deep CNN Model for Cyberbullying Detection in Online Social Media

Abstract

Cyberbullying has become a significant issue in the digital age, affecting millions of users on social media platforms. Traditional approaches, such as keyword-based filtering and conventional machine learning models, often fail to accurately detect cyberbullying due to their inability to understand contextual meanings and linguistic variations like sarcasm and slang. This project proposes a Deep Convolutional Neural Network (CNN) model for automated cyberbullying detection in online social media text. The model leverages deep learning techniques to extract semantic features from text data, improving the detection of harmful and abusive language. The proposed system involves data preprocessing, feature extraction using word embeddings, and classification using a CNN architecture trained on labeled datasets.