Cyberbullying detection on multi-modal data using pre-trained deep learning architectures

Introduction: The present article is the product of the research "Cyberbullying Detection on Multi-Modal Data Using Pre-Trained Deep Learning Architectures.", developed at Pondicherry Engineering College in the year 2020. Problem: Identification of cyberbullying activities on multi-modal data of social media Objective: To propose a model that can identify cyberbullying activity for text and image data. Methodology: This paper has extracted the features of using two pre-trained architectures for text data and image data, in order to identify cyberbullying activities on multi-modal data, concatenated text features and image features, before supplying them as inputs to the classifier. Results: An analysis has been performed on the proposed approach implemented on multi-modal data with Recall, and F1-Score as measures. Grad-cam visualization is presented for images to show highlighting regions. Conclusion: The results indicate that the proposed approach is efficient when compared with the baseline methods. Originality: The proposed approach is effective and conceptualized to improve cyberbullying detection on multi-modal data. Limitations: There is a need to develop a model which can identify bullying graphical images and videos