## 2 Related Works
The researchers employed a pre-trained VGG-16 model to extract the visual features of the images, while also utilizing a three-layered CNN to extract the textual data. The hybrid feature set, consisting of both image and text features, was optimized using the BPSO algorithm to extract the most pertinent characteristics. The enhanced model, incorporating advanced features and utilizing the Random Forest classifier, achieved a weighted F1-score of 0.74. [PERSON] et al. [27] explored cyber-trolling identification using text data from YouTube, Twitter, and other sources. They applied machine learning and DL techniques, including logistic regression, multinomial Naive Bayes, Random Forest, and Bidirectional LSTM. Their Random Forest model achieved the highest accuracy of 96.50%, outperforming other models. The dataset included topic-specific categories such as misogyny, racism, and xenophobia. [PERSON] et al. [28] introduced the \"ETHOS\" dataset, designed for multi-label hate speech detection. This textual dataset, sourced from comments on Reddit and YouTube, was validated using the FigureEight crowdsourcing platform. The dataset is available in two formats: binary classification and multi-label classification. For the multi-label classification task, the BiLSTM model achieved the following accuracy rates: violence (50.86%), directed vs. generalized (55.28%), gender (70.34%), race (75.97%), national origin (67.88%), disability (69.64%), religion (71.65%), and sexual orientation (89.83%). [PERSON] et al. [29] introduced a deep learning-based approach to identify cyberbullying on social media platforms for text and image data. Nine deep learning models were trained, including ResNet-50, CNN, and ViT for image data, as well as LSTM-2,GRU, RoBERTa, BERT, DistilBERT, and a hybrid (CNN+LSTM) model for textual data. For multi-class classification of public image data, the ViT model achieved the highest accuracy (99.5%) and an F1-score of 0.995. Similarly, the RoBERTa model outperformed other textual models, achieving an accuracy of 99.2% and an F1-score of 0.992 on public data. On the private dataset, the RoBERTa model achieved an F1-score of 0.986 and an accuracy of 98.6% for textual data, while the ViT model attained an F1-score of 0.9319 and an accuracy of 93.20% for image classification. These results highlight the effectiveness of RoBERTa for text classification and vision transformer (ViT) for image-based classification in identifying cyberbullying. [PERSON] et al. [30] introduced a framework that employed the weighted ensemble technique to allocate weights to the visual, textual, and multi-modal models involved. The framework utilized advanced visual models such as VGG19, VGG16, and ResNet50, as well as textual models like multilingual-BERT, multilingual-DistilBERT, and XLM-R. Additionally, two fusion methodologies, namely early fusion and late fusion, were employed to integrate the visual and textual characteristics in order to construct the multi-modal models. The results demonstrated that the proposed method achieved better performance on two multilingual benchmark datasets (MultiOFF and TamilMemes), with weighted F1-scores of 66.73% and 58.59%, by applying the best visual (VGG19), textual (m-DistilBERT), decision fusion (VGG19+m-DistilBERT), and feature fusion (VGG19+m-DistilBERT) models. [PERSON] et al. [31] introduced KnowNet, a novel model that uses the ConceptNet knowledge base to integrate prior knowledge and assess the relationship between images and text through cross-modal semantic similarity detection at the sample and word levels. They also applied contrastive learning to enhance the spatial distribution of sarcastic (positive) and non-sarcastic (negative) samples from the dataset of [PERSON] et al [21]. The model demonstrated state-of-the-art performance on publicly available benchmark datasets using BERT and ResNet-50 and achieved an accuracy of 64.35%. The limitation of the research is that it focused on binary classification. [PERSON] et al.