Empowering Digital Civility with an NLP Approach for Detecting (Formerly Known as Twitter) Cyberbullying through Boosted Ensembles

Abstract

As the number of social networking sites grows, so do cyber dangers. Cyberbullying is harmful behavior that uses technology to intimidate, harass, or harm someone, often on social media platforms like (formerly known as Twitter). Machine learning is the optimal approach for cyberbullying detection on to process large amounts of data, identify patterns of offensive behavior, and automate the detection process for corpus of tweets. To identify cyber threats using a trained model, the boosted ensemble (BE) technique is assessed with various machine learning algorithms such as the convolutional neural network (CNN), long short-term memory (LSTM), naive Bayes (NB), decision tree (DT), support vector machine (SVM), bidirectional LSTM (BILSTM), recurrent neural network LSTM (RNN-LSTM), multi-modal cyberbullying detection (MMCD), and random forest (RF). These classifiers are trained on the vectorized data to classify the tweets to identify cyberbullying threats. The proposed framework can detect cyberbullying cases precisely on tweets. The significance of the work lies in detecting and mitigating cyber threats in real time, and it impacts in enhancing the safety and well-being of social media users by reducing instances of cyberbullying and other cyber threats. The comparative analysis is done using metrics like accuracy, precision, recall, and F1-score, and the comparison results show that the BE technique outperforms other compared algorithms with its overall performance. Respectively, the accuracy rates of CNN, LSTM, NB, DT, SVM, RF, BILSTM, and BE are 92.5%, 93.5%, 84.6%, 88%, 89.3%, 92%, 93.75%, and 96%; precision rates of CNN, LSTM, NB, DT, SVM, RF, RNN-LSTM, and BE are 90.2%, 91.3%, 88%, 85%, 86%, 91.6%, 92.1%, and 94%; recall rates of CNN, LSTM, NB, DT, SVM, RF, BILSTM, and BE are 89.8%, 90.7%, 90%, 82%, 88.67%, 89%, 91.04%, and 93.7%; and F1-scores of CNN, LSTM, NB, DT, SVM, RF, MMCD, and BE are 90.6%, 91.8%, 85%, 84.56% 87.2%, 90%, 84.6%, and 94.89%.