###### Abstract  
Cyberbullying involves the use of social media platforms to harm or humiliate people online. Victims may resort to self-harm due to the abuse they experience on these platforms, where users can remain anonymous and spread malicious content. This highlights an urgent need for efficient systems to identify and classify cyberbullying. Many researchers have approached this problem using various methods such as binary and multi-class classification, focusing on text, image, or multi-modal data. While deep learning has advanced cyberbullying detection and classification, the multi-class classification of cyberbullying using multi-modal data, such as memes, remains underexplored. This paper addresses this gap by proposing several multi-modal hybrid deep learning models, such as LSTM+ResNet, LSTM+CNN, LSTM+ViT, GRU+ResNet, GRU+CNN, GRU+ViT, BERT+ResNet, BERT+CNN, BERT+ViT, DistilBERT+ResNet, DistilBERT+CNN, DistilBERT+ViT, RoBERTa+ResNet, RoBERTa+CNN, and RoBERTa+ViT, for classifying multi-classes of cyberbullying. The proposed model incorporates a late fusion process, combining the LSTM, GRU, BERT, DistilBERT, and RoBERTa models for text extraction and the ResNet, CNN, and ViT models for image extraction. These models are trained on two datasets: a private dataset, collected from various social media platforms, and a public dataset, obtained from previously published research. Our experimental results demonstrate that the RoBERTa+ViT model achieves an accuracy of 99.20% and an F1-score of 0.992 on the public dataset, and an accuracy of 96.10% and an F1-score of 0.959 on the private dataset when compared with other hybrid models. cyberbullying; multi-modal data; multi-class classification; deep learning; hybrid (RoBERTa+ViT) model; late fusion module; social media +
Footnote †: journal: applied sciences  
emotional distress, anxiety, and even self-harm among victims. Notably, the _EU-Kids Online Report_[6] from 2014 revealed that 20% of young people aged 11 to 16 years experience cyberbullying. Similarly, a quantitative study conducted by researchers [7] reported that cyber victimization rates range from 20% to 40%, underscoring the urgency of addressing this issue. As SM platforms continue to grow and short-form video content gains prominence, the need for effective tools to detect and prevent cyberbullying becomes increasingly critical. The automated identification and classification of cyberbullying incidents offers a promising approach to mitigate this problem. Various researchers [8; 9] have explored techniques for detecting and classifying bullying behaviors and supporting victims. However, existing methods often focus on binary classification or multi-class textual analysis, leaving gaps in the multi-class classification of multi-modal data. Deep learning (DL) technologies have shown remarkable success in numerous domains, including spam SMS detection [10], healthcare diagnosis [11], disaster management [12], and stock prediction [13]. In the context of cyberbullying, DL methods offer the potential to analyze complex and previously unseen datasets, identifying patterns and behaviors indicative of bullying [14; 15; 16; 17]. Existing studies have applied DL for binary and multi-class textual classification, yet comprehensive analysis for multi-modal data remains underexplored [18; 19; 20; 21]. This research aims to bridge this gap by leveraging advanced DL models for multi-class classification of cyberbullying in multi-modal datasets sourced from SM platforms, with a focus on comments related to short videos. The primary objective of this study is to develop and evaluate a hybrid DL model, particularly transformer architectures, to improve the accuracy of cyberbullying multi-class classification using multi-modal data and contribute to safer online environments. To achieve this objective, the following research questions will be addressed:  
1. Which deep learning models perform best for the multi-class classification of cyberbullying on multi-modal data? 2.