# A Review on Deep-Learning-Based Cyberbullying Detection
## 5 Deep-Learning-Based Models
### Performance Comparison of DL Models in Cyberbullying Detection  
It is important to investigate the performance of a deep learning model for a classification problem such as cyberbullying detection. Training accuracy, validation accuracy, learning curves, and early stopping are crucial metrics that can be used to assess the model during the training and testing phases. Training loss and validation loss are also important metrics to measure the performance of a deep learning model. When we train our model, we usually evaluate the performance of a deep learning model using a test dataset. Four fundamental concepts are utilized to assess the performance of a model of classification task: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). In a binary classification problem, TP refers to the cases where the model correctly identifies a positive instance, TN refers to the cases where the model correctly identifies a negative instance, FP refers to the cases where the model incorrectly identifies a negative instance as positive, and FN refers to the cases where the model incorrectly identifies a positive instance as negative. A TP would be a circumstance in which a model correctly recognizes a piece of content as cyberbullying in the case of cyberbullying classification, while a TN would be a circumstance in which a model correctly identifies non-cyberbullying content. On the other hand, an FP would be a situation where the model incorrectly identifies non-cyberbullying content as cyberbullying, while an FN would be a situation where the model incorrectly identifies cyberbullying content as non-cyberbullying. Based on the four fundamental concepts (TP, TN, FP, FN), accuracy, precision, recall, F1 score, MCC, and area under the receiver operating characteristic (AUC-ROC) curve are frequent performance analysis evaluation metrics for the classification task. Accuracy: This metric counts the percentage of all predictions made by the model that came true, both positively and negatively. A higher accuracy means that more instances of cyberbullying have been correctly classified by the model. \[\text{Accuracy}\ =\frac{TP+TN}{TP+FP+TN+FN}\] \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**DL Models** & **Used in Cyberbullying Applications** & **Area of Applications** & **Limitations** \\ \hline Restricted Boltzmann Machines (RBMs) [96] & Turkish social media contents [145], Arabic content [74] and collaborative filtering & Dimensionality reduction, classification, regression, feature learning, topic modeling, and collaborative filtering & Training is more difficult because it is difficult to calculate the energy gradient function, the CD-k algorithm used in RBM is not as well known as the backpropagation algorithm, weight adjustment \\ \hline Gated Recurrent Units (GRU) [146] & Social Commentary [21], Facebook and Twitter aggressive speech [115], Bangla text [18], Formspring, m, MySpace and YouTube content [135] & Sequence learning, Solved Vanishing-Exploding gradients problem & Slow convergence and low learning efficiency \\ \hline Attention-based model [147] & Twitter bullied text identification [78], social media text analysis [112], online textual harassment detection [71], contextual textual bullies [148], Instagram bullied text identification [118], Abusive Bangla Comment detection [121], Trait-based bullying detection [114] & The method provides a simple and efficient architecture with a fixed length vector to pay attention of a sentence’s high-level meaning & The model requires more weight parameters, which results in a longer training time \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)Precision: The precision metric calculates the ratio of true positives to true positives plus false positives. A higher precision means the model is more accurate at classifying instances of cyberbullying and has fewer false positives.