## 2 Related Works  
Significant research has explored the application of deep learning (DL) algorithms for cyberbullying classification. This section presents existing studies that focus on multi-class cyberbullying classification, examining the DL models employed, the methods used for data collection and labeling, and the effectiveness of these approaches in handling multi-modal datasets. [PERSON] et al. [20] proposed DL-based methods for cyberbullying identification on Twitter. They created a textual dataset of 99,991 tweets and conducted both binary and multi-class classification. For multi-class classification (e.g., religion, ethnicity/race, gender/sexual), their RoBERTa model achieved an accuracy of 99.80%. [PERSON] et al. [21] introduced a multitask DL framework for the identification of cyberbullying, such as sentiment, sarcasm, and emotion-aware cyberbullying from multi-modal memes. In their study, the authors collected images and memes from _Twitter_ and _Reddit_. To scrape the images, they used hashtags like MeToo, KathuaRapeCase, Nirbhya, Rendi, Chuthiya, and Kamini on Twitter, as well as subreddits like Desimemes, HindiMemes, and Bakchodi on Reddit, resulting in around 5854 images and memes. Various DL models, such as BERT, ResNET-Feedback, and CLIP-CentralNet, were developed and trained using textual and visual data. The task of the sentiment-emotion-sarcasm-aware multi-modal cyberbullying identification in a code-mixed scenario was introduced for the first time in their paper. To tackle this challenge, they developed a novel multi-modal meme dataset called MultiBully, annotated with labels for bullies, attitude, emotion, and sarcasm. The purpose of this annotation was to determine whether this information could aid in more accurate cyberbullying identification. An attention-based multi-task multi-modal framework, CLIP-CentralNet, was developed as a new architecture for sentiment, emotion, and sarcasm-assisted cyberbullying identification. The proposed model included ResNet, mBERT, and CLIP for effective representations of many modalities and support in learning generic features across several tasks. The newly created CLIP-CentralNet framework performed noticeably better than all single-task and uni-modal models in their task. For the purpose of identifying cyberbullying, they achieved an accuracy of 61.14% for textual data using BERT, GRU, and a fully connected layer, and 63.36% for image data using ResNet and a fully connected layer. [PERSON] et al. [22] developed a DL model named Bengali BERT for classifying different categories of cyberbullying in Bengali-language text data. They utilized a dataset of YouTube textual comments from [23; 24]. This dataset included classes such as religious, sexual, linguistic, political, personal, and crime-related content. Offensive categories such as personal, geographical, and religious content were also included, alongside data related to entertainment, sports, memes, and TikTok. Their Bengali BERT model achieved an accuracy of 0.706 and a weighted F1-score of 0.705. [PERSON] et al. [25] analyzed Bengali social media comments, focusing on textual data from Facebook. They collected a text dataset of approximately 42,036 comments, categorized into classes like political, religious, sexual, acceptable, and combined. They implemented CNN and LSTM models for classification. Their proposed CLSTM architecture achieved an accuracy of 85.8% and an F1-score of 0.86. [PERSON] et al. [26] proposed a model that employed a Convolutional Neural Network (CNN) and Binary Particle Swarm Optimization (BPSO) to classify SM posts from platforms like Facebook, Twitter, and Instagram. The model categorized posts containing both images and written comments into three classes: non-aggressive, medium-aggressive, and high-aggressive. The dataset, comprising symbolic images and their corresponding textual comments, was created to validate the proposed model.