# A Review on Deep-Learning-Based Cyberbullying Detection
## 6 DL in Cyberbullying Detection  
Several studies [21; 64; 115; 134] have been conducted on the automatic identification of cyberbullying by using different independent ML techniques. A few studies [21; 134] exploit RNN-based techniques, i.e., LSTM, BiLSTM, RNN, etc., while some other studies [5; 137] use CNN-based techniques (i.e., CNN, PCNN, Char-CNNS, etc.) to detect cyberbullying from different sources. However, we also observe that some authors [63] perform integration of RNN-CNN-based techniques. In this section, we briefly discuss different applications of DL models in cyberbullying detection. In Table 6, we have organized the papers based on three main themes: improvements of DL models, optimization of model performance, and improving data capabilities. Furthermore, we have included a comprehensive group-wise analysis of the significant contributions made by these papers, as well as their potential impact on future research directions. [PERSON] et al. [21] applied three DL models: Bi-LSTM, LSTM, and RNN, to investigate the performance of DL algorithms in identifying bullying (i.e., insults) in social media. They discovered that Bi-LSTM outperforms other models in terms of accuracy and F1 scores after extensive testing. They also asserted that DL is the most effective method for detecting cyberbullying and related cyber challenges. [PERSON] et al. [64] constructed a DL-based model employing three common text classification algorithms: LSTM, Bi-LSTM, and CNN, to detect bullying on Twitter in Indonesia. [PERSON] et al. [2] applied a DL technique on a large human-labeled dataset to categorize cyberbullying. LSTM, Bi-LSTM, and CNN were the DL models employed in their tests compared to other algorithms. [PERSON] et al. [134] developed an RNN-based technique to identify and categorize cyberbullying posts. To decrease data imbalance and remove ambiguities in classification, they employed a Tomek Link approach to accomplish under-sampling. Their classification model was Max-Pooling combined with a Bi-LSTM network and an attention layer. To test their model, they utilized Wikipedia datasets. [PERSON] et al. [115] offered automation to identify violent cyberbullying act. The approach uses multichannel DL-based on BiGRU, transformer blocks, and CNN models to determine whether a Twitter comment is hostile. They also integrated three well-known hate speech datasets to assess the model performance. [PERSON] et al. [135] presented a BiGRU-CNN sentiment classification model for cyberbullying identification. The BiGRU layer, attention mechanism layer, CNN layer, fully connected layer, and classification layer are different parts of the model. They trained and tested their proposed model using the Kaggle text dataset and the emoji dataset scraped from social networks, which outperforms traditional algorithms. [PERSON] et al. [5] presented the Char-CNNS (Character-level Convolutional Neural Network with Shortcuts) model for detecting cyberbullying in social media discourse. Since the content available on social media is short, noisy, and unstructured with wrong spellings and symbols, they chose the character as the smallest unit of learning to overcome spelling mistakes and purposeful obfuscation. They conducted experiments over the Chinese Weibo dataset and the English Tweet dataset. Results of their experiment show that outstanding performance on the cyberbullying detection task is competitive with the state-of-the-art approaches. [PERSON] et al. [137] proposed a new pronunciation-based convolutional neural network (PCNN) to handle the difficulty of noise and distortion in social media postings and messages in detecting cyberbullying. They also used three strategies in their model to solve the problem of class imbalance: threshold-moving, cost function modifying, and a hybrid solution. [PERSON] et al. [18] built a model to identify cyberbullying in Bangla and Romanized Bangla writings by using ML and DL methods.