# A Review on Deep-Learning-Based Cyberbullying Detection
## 6 DL in Cyberbullying Detection
In their experiment, they discovered that for one dataset, CNN, a DL algorithm outperforms other ML and DL models, whereas ML models outperform DL models for the other two datasets. [PERSON] et al. [7] introduced a neural network design for cyberbullying detection that is based on an existing design, which combines convolution layers with LSTM layers. They also introduced a novel activation mechanism known as SVM-like activation, which is accomplished by using L2 weight regularization. They evaluated their suggested model using the bullying traces dataset to classify the challenge between open aggressiveness, covert aggression, and non-aggression in social media writings. [PERSON] et al. [63] suggested an LSTM-CNN model for text-based cyberbullying detection that captures sentence semantics. In addition, they developed a web application for their suggested paradigm. [PERSON] et al. [66] suggested an approach that combines two DL models, one of which is a character-level CNN and the other a word-level LRCN. The first model extracts low-level syntactic information from a character sequence. It is also noise-resistant. The second model, which works in tandem with the CNN model, gathers high-level semantic information from a series of words. They also demonstrated that their suggested ensemble technique outperforms the state-of-the-art algorithms for detecting cyberbullying in comments of social networking sites. [PERSON] et al. [68] developed models by implementing DL models. They used three real-world datasets: Formspring ([[https://spring.me/](https://spring.me/)]([https://spring.me/](https://spring.me/)) accessed on 18 April 2023), Twitter ([[https://github.com/zeeraktalat/hatespeech/](https://github.com/zeeraktalat/hatespeech/)]([https://github.com/zeeraktalat/hatespeech/](https://github.com/zeeraktalat/hatespeech/)) accessed on 18 April 2023), and Wikipedia ([[https://figshare.com/articles/dataset/Wikipedia_Talk_Corpus/4264973](https://figshare.com/articles/dataset/Wikipedia_Talk_Corpus/4264973)]([https://figshare.com/articles/dataset/Wikipedia_Talk_Corpus/4264973](https://figshare.com/articles/dataset/Wikipedia_Talk_Corpus/4264973)) accessed on 18 April 2023), to conduct comprehensive tests. The study gives some interesting insights on the detection of cyberbullying, such as that swear words are not sufficient for detecting cyberbullying. Powerful models that are used for detecting cyberbullying are not expected to depend on such handcrafted features. [PERSON] et al. [25] found that DL-based models outperform traditional ML models. They used Wikipedia, Twitter, and Formspring datasets. [PERSON] et al. [67] suggested optimized Twitter cyberbullying detection based on DL (OCDD), a unique technique to the cyberbullying detection. To preserve the meaning of the words, their suggested approach encodes a tweet as a set of word vectors rather than collecting characteristics from tweets and feeding them into a classifier. For the classification phase, they employed DL, and for parameter tuning, they used a metaheuristic optimization approach. [PERSON] et al. [136] offered classical ML, DL, and a mixture of both approaches. To test their algorithms, they used data from Twitter and Facebook ([[https://sites.google.com/view/trac1/shared-task](https://sites.google.com/view/trac1/shared-task)]([https://sites.google.com/view/trac1/shared-task](https://sites.google.com/view/trac1/shared-task)) accessed on 18 April 2023). They ensembled classic ML with DL algorithms by using a voting mechanism. [PERSON] et al. [73] suggested a unique strategy to identify cyberbullying on social media platforms that improve on current findings by combining a pre-trained BERT model with a single linear neural network layer as a classifier. Their algorithm trains and tests on two manually labeled social media datasets: Formspring (a Q&A forum) and Wikipedia, using a consolidated DL approach. [PERSON] et al. [22] demonstrates a unique use of BERT for detecting cyberbullying.