Termite Apriori Prediction Framework for Detecting Cyber Threats in Social Networks

Today, malicious actors often use social media to send, receive, and post false, misleading, or offensive content about other people. The effects of social media bullying on its targets are similar to those of threats, gossip, and harassment in the physical world. An alarming rise in mental health issues has resulted from cyberbullying, particularly in the younger population. The effects can include suicidal thoughts and low self-esteem. Several conventional machine learning techniques have been employed to automatically identify cyberbullying on social media. Although some traditional methods of machine learning have been designed to automatically identify cyberbullying, most of them are characterized by weaknesses in feature selection and classification rates. In order to deal with such issues, a new Termite Apriori Prediction Framework (TAPF) has been proposed in this paper. The model consists of a data collection and preprocessing phase to remove noise and a hybrid feature selection phase that combines Apriori rule mining with termite optimisation to select the most discriminative features. Cyberbullying is then classified with the help of these optimized features. It has been experimentally shown that TAPF performs better than the classical models such as the Logistic Regression, Naive Bayes, Long Short-Term Memory (LSTM), and the Support Vector Machine (SVM). The proposed structure has a recall rate of 96.2, precision of 96.7, F-score of 96.3, and an overall accuracy of 96.4, which is over 2 per cent higher than the best-performing baseline (SVM). Applied to a Python environment, TAPF offers an effective and dependable mechanism of detecting cyberbullying on social media sites.