## 2 Related Work  
In this section, we provide a comprehensive overview of state-of-the-art proposals and relevant related work in the context of cyberbullying detection, prevention, and analysis on social media. Ref. [41] focuses on addressing the annoying problem of detecting aggressive activity and cyberbullying behavior in textual content on social media platforms, which is critical for reducing its harmful impacts. Existing approaches leveraging traditional natural language processing (NLP) and ML techniques often face limitations in accuracy and contextual understanding. To address these drawbacks, this paper proposes an automated deep learning model using a binary chimp optimization (BCO)-based feature selection (BCO-FSS) technique and a stacked bidirectional gated recurrent unit (SBiGRU) with attention to spatial and sequential semantic learning. The study employs datasets from various platforms, like Formspring, Instagram, and MySpace, and integrates linguistic complexity metrics, such as feature density (FD), and finally utilizes BERT as a baseline classifier. The experimental evaluation of the proposed approach achieves high performance in terms of several well-known metrics, such as accuracy, precision, recall, and F1-score. Finally, this approach provides insights into the decision-making process of the model, due to the incorporation of attention mechanisms that visualize how sentiment weights are assigned at different layers. Ref. [42] introduces an innovative and reliable framework for detecting cyberbullying built on top of a multi-modal cyberbullying detection (MMCD) approach that integrates data from diverse social networks. The proposed model effectively addresses the limitations of traditional text-based detection methods in handling the multifaceted nature of cyberbullying, by integrating hierarchical attention networks (HANs) with multi-modal data, including visual, textual, and temporal features, in order to provide a holistic cyberbullying detection platform. Specifically, it incorporates a self-attention-based BiLSTM model and HAN model focusing on word and comment levels, and other embedding techniques. The integration of these components enables efficient information merging and contributes to the effective resolution of the complex issue of cyberbullying. Moreover, the experimental analysis conducted over three social media datasets highlights that the MMCD model achieves greater performance when compared with several state-of-the-art models in terms of accuracy and F1-score. In Ref. [43], the authors propose RoBERTaNET, an enhanced RoBERTa transformer model tailored for cyberbullying detection with global vectors for word representation (GloVe) word embedding features. Through this innovative approach, this research deals with the challenge of automatically identifying cyberbullying in Tweets from a publicly available cyberbullying dataset. The experimental analysis involves comparing the proposed approach with several state-of-the-art machine learning (e.g., random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN)), deep learning (e.g., bidirectional long short-term memory (BiLSTM), convolutional long short-term memory (ConvLSTM)), and transformer-based models (e.g., bidirectional encoder representations from transformers (BERT)). The results demonstrate the effectiveness of RoBERTaNET by achieving a notable 95% accuracy, along with 95%, 97%, and 96% for precision, recall, and F1-score, respectively. In summary, this approach of combining robust transformer-based architectures with word embedding techniques paves the way for more effective and scalable solutions. Ref. [38] proposes FedBully, a novel federated learning (FL) approach for cyberbullying detection to preserve client privacy in learning systems. The proposed approach leverages sentence encoders for feature extraction and secure aggregation protocols in order to ensure data privacy while enabling effective collaborative learning.