# A Review on Deep-Learning-Based Cyberbullying Detection
## 5 Deep-Learning-Based Models
### Performance Comparison of DL Models in Cyberbullying Detection
Precision = \(\frac{TP}{TP+FP}\)  
Recall: This metric is the ratio of true positives to true positives plus false negatives. A higher recall means the model is more accurate at spotting instances of cyberbullying and has fewer false negatives. Recall = \(\frac{TP}{TP+FN}\)  
F1 score: This metric, which gives an overall assessment of the performance of the model, is the harmonic mean of precision and recall. A higher F1 score means that the model is more accurate at classifying instances of cyberbullying and has balanced precision and recall. \[F1\,\text{score }=2\times\frac{\text{Precision }\times\text{Recall}}{\text{ Precision }+\text{Recall}}\]  
Matthews Correlation Coefficient (MCC): The metric is a balanced metric that penalizes false positives and false negatives while accounting for both true positives and true negatives. Model performance is better when the MCC is higher. \[MCC=\frac{(TP\times TN)-(FP\times FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}\]  
Area Under the Receiver Operating Characteristic Curve (AUC-ROC): This metric measures the trade-off between the true positive rate and the false positive rate and is a graphical representation of the performance of the model. Better model performance is indicated by a higher AUC-ROC. Several deep-learning-based cyberbullying detection methods have been conducted by using individual models and presenting the performance of the models with popular evaluation metrics [134; 135; 136; 137]. [PERSON] et al. [138] compared different deep learning and traditional machine learning approaches for cyberbullying classification tasks. They utilized LSTM, Bi-LSTM, GRU, and Bi-GRU for the cyberbullying classification. They employed the Wikipedia Attack Dataset and Wikipedia Web Toxicity Dataset for this purpose. The experimental results show that Bi-LSTM and Bi-GRU outperform other deep-learning-based models in this context on accuracy and F1 score. They obtained the best accuracy and F1 scores of 96.98% and 98.56%, respectively, by using Bi-GRU on the Wikipedia Attack Dataset. On the other hand, they achieved the best accuracy and F1 scores of 96.5% and 98.69%, respectively, using Bi-LSTM over the Wikipedia Web Toxicity Dataset. [PERSON] et al. [137] applied different deep learning models over the Twitter dataset and found that Bi-LSTM outperformed other deep learning models with accuracy, precision, and F1 scores of 92.60%, 96.60%, and 94.20%, respectively. [PERSON] et al. [134] used Bi-LSTM, GRU, LSTM, and RNN models for cyberbullying detection over the DISCo Kaggle dataset where Bi-LSTM outperformed other models with an accuracy score of 82.18%. [PERSON] et al. [135] utilized Bi-LSTM with attention layers over the Wikipedia dataset and compared their study with some existing works. Experimental results show that their proposed method outperformed the existing works in terms of precision, recall, and F1 score of 89%, 86%, and 88%, respectively. In the literature, there are several studies that also present the performance evaluation of different hybrid deep-learning-based approaches [137; 138; 139; 140]. [PERSON] et al. [138] proposed a multichannel deep learning framework where they proposed a combination of transformer block, Bi-GRU, and CNN to detect cyberbullying in Twitter comments where the model outperforms the individual model with an accuracy score of 88%. [PERSON] et al. [137] applied a combined CNN and LRCN model to detect cyberbullying from SNS commentswhere the model obtained an AUC-ROC and accuracy scores of 88.54% and 87.22%, respectively. [PERSON] et al. [151] proposed a hybrid method named DEA-RNN that combines Elman-type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) that optimizes the training time to detect cyberbullying on Twitter. They also applied Bi-LSTM and RNN models for the performance comparison, and the experimental results show that their proposed method outperformed the models in terms of accuracy, precision, recall, and F1 score of 90.45%, 89.52%, 88.98%, and 89.25%, respectively.