A multi-class cyberbullying classification on image and text in code-mixed Bangla-English social media content

Social media platforms like Facebook, Instagram, and Twitter are widely used; users frequently share their daily lives by uploading pictures, posts, and videos, which gain significant popularity. However, social media posts often receive a mix of reactions, ranging from positive to negative, and in some instances, negative comments escalate into cyberbullying. Numerous studies have addressed this issue by focusing on cyberbullying classification, primarily through binary classification using multimodal data or targeting either text or image data. This study investigates the identification of multi-class images like No-bullying, Religious, Sexual, and Others using the deep learning pre-trained model MobileNetV2 to detect multiple image labels and achieved an F1-score of 0.86. For categorizing hate comments, we consider multiple classes, including Not Hate, Slang, Sexual, Racial, and Religious-related content. Extensive experiments were conducted on a novel Bengali-English code-mixed dataset, utilizing a combination of advanced transformer models, traditional machine learning techniques, and deep learning approaches to detect multiple hate comment labels. Bangla BERT achieved the highest F1-score of 0.79, followed closely by SVM at 0.78 and BiLSTM with attention at 0.73. These findings underscore the effectiveness of these models in capturing the complexities of code-mixed Bengali-English, offering valuable insights into cyberbullying detection in diverse linguistic contexts. This research contributes essential strategies for improving online safety and fostering respectful digital interactions.