This research tackles the important problem of offensive data detection and classification using deep learning methods within the context of social networks. The end goal of this research is to create an artificial intelligence (AI) system that can identify inappropriate data in many forms of media, such as text, audio, and images. The system can detect potentially harmful items by using technologies such as Optical Character Recognition (OCR), Google Text to Speech (GTTS), and Natural Language Processing (NLP). Problems plaguing this area of study at the moment include small datasets and biased model results. Using conventional metrics like Accuracy, Precision, Recall, and F1-measure, the research conducts an exhaustive review to determine the effectiveness of various techniques. According to the results, deep learning models, and the Recurrent Neural Network (RNN) architecture, in particular, are very effective. By facilitating the early detection and prevention of cyberbullying, our study contributes substantial new information towards the goal of creating safer and more inclusive societies. Skillfully extracting data from social media platforms. Our models outperform prior research in identifying and classifying foul language thanks to our use of sophisticated preprocessing techniques and meticulous hyperparameter adjustment.
Journal of Theoretical and Applied Information Technology (Mon,) studied this question.