Detecting cyber bullying in the social media using deep learning and ensemble algorithms

Abstract

Cyberbullying, a pervasive issue in the digital age, poses a serious threat to individuals' well-being, necessitating advanced technologies for effective detection and mitigation. This research focuses on the detection of cyberbullying within the context of the Bangla language, employing a comprehensive approach that integrates deep learning and traditional machine learning algorithms. The dataset used for this study is specifically curated for Bangla, ensuring the model's applicability in diverse linguistic scenarios. In our methodology, we leverage well-established machine learning models, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, Gaussian Naive Bayes (GNB), Quadratic Discriminant Analysis (QDA), Ridge Classifier (RC), and Passive Aggressive Classifier (PA). Additionally, we incorporate sophisticated deep learning models, Bidirectional Long Short-Term Memory (BLSTM) and Recurrent Neural Network (RNN), to enhance the detection capabilities. The evaluation process involves employing Bagging Classifiers to assess the performance of each model, considering metrics such as accuracy, precision, recall, F1 score, and the Area Under the Receiver Operating Characteristic (ROC) Curve. We visualize the results through confusion matrices and ROC curves, providing a comprehensive analysis of each model's effectiveness. The Bidirectional Long Short-Term Memory (BLSTM) emerges as the frontrunner among the algorithms, exhibiting the highest scores 99.80% accurate across key metrics.