Comparative analysis on deep neural network models for detection of cyberbullying on Social Media - Balakrishna, Sivadi; Gopi, Yerrakula; Solanki, Vijender Kumar (Ingeniería Solidaria, 2022)
Introduction: Social media usage has been increased and it consists of both positive and negative effects. By considering the misuse of social media platforms through various cyberbullying methods like stalking and harassment, there should be preventive methods to control these and avoid mental stress. Problem: These extra words will expand the size of the vocabulary and influence the performance of the algo-rithm.Objective: To detect cyberbullying in social media. Methodology: In this paper, we come up with variant deep learning models like Long Short Term Memory (LSTM), Bi-Directional Long Short Term Memory (BI-LSTM), Recurrent Neural Networks (RNN), Bi-Directional Recurrent Neural Networks (BI-RNN), Gated Recurrent Unit (GRU), and Bi-Directional Gated Recurrent Unit (BI-GRU) to detect cyberbullying in social media. Results: The proposed mechanism has been performed, analyzed and implemented on Twitter data with Accuracy, Precision, Recall, and F-Score as measures. The deep learning models such as LSTM, BI-LSTM, RNN, BI-RNN, GRU, and BI-GRU are applied on Twitter to public comments data and performance was observed for these models, obtaining an improved accuracy of 90.4%. Conclusions: The results indicate that the proposed mechanism is efficient when compared with the state of the art schemes.Originality:Applying deep learning models to perform comparative analysis on social media data is the first approach to detecting cyberbullying.Restrictions:These models are applied only on textual data comments. Own work have not concentrated on multimedia data such as Audio, Video, and Images.