# A Review on Deep-Learning-Based Cyberbullying Detection
## 1 Introduction
After the preprocessing step, feature extraction is carried out to transform the raw data into numerical features, which are more meaningful to a machine learning model. Next, the outcome is sent to a deep-learning-based cyberbully detection module for detecting the cyberbully contents. Finally, the cyberbullying content is classified as bully or non-bully. Consider the case where we need to identify cyberbullying on a social media site such as Twitter. The text of tweets would be analyzed by a text-based model to spot any words or phrases that suggest harassment, aggression, or discrimination. Tweets containing the words \"kill yourself\", \"ugly\" or \"stupid\", for instance, might be labeled as cyberbullying. Again, consider that we want to identify cyberbullying on an Instagram-like photo-sharing app. Images with offensive gestures, hate symbols, or violent scenes are just a few examples of visual cues that can be used by an image-based model to analyze the content of images and detect cyberbullying. In this paper, we have reviewed the research works that focus on automated cyberbullying detection using either DL models [26; 27; 28; 29] or supervised learning techniques [30; 31]. A few papers focused on the DL models, but none concentrated on the frameworks and area of applications to detect cyberbullying. Prior survey papers do not present an overall ecosystem for cyberbullying detection methods to understand the DL-based solution systems' comprehensive structure. These studies do not show publicly available datasets for cyberbullying detection, and a few survey papers address the open issues and challenges [26; 27]. The absence of a globally acknowledged definition of cyberbullying is one of the major issues in the studied literature on automated cyberbullying detection. In this study, we first develop a clear taxonomy for our DL-based cyberbullying ecosystems. Figure 2 shows the graphical presentation of the detailed taxonomy of our cyberbullying ecosystem. The ecosystem broadly encompasses data representation techniques, DL models, and DL frameworks. To predict cyberbullying behavior, we collect datasets from the Internet where the content can be texts or images. Machine learning algorithms are typically fed vectorized numeric data, but the natural language or images are non-numeric data. To represent the data to be compatible with machine learning algorithms, we use data representation techniques to present the data in a numeric form. To convert the text into numeric form, we generally use two types of word-embedding techniques: pretrained and non-pretrained. Pretrained word-embedding techniques include but are not limited to Word2 Vec [32; 33], GloVe [34], ELMo [35], fastText [36], and BERT [37] whereas One-hot encoding and TF-IDF [38] are nonpretrained. To convert the image data to numeric data, we use effective, Graph, or ANN-based methods. After converting the non-numerical data into numeric data, we deliberately choose a suitable deep-learning algorithm. If the problem requires a generative model, then we might choose the Boltzmann Machine (BMs) [39], Deep Belief Network (DBN) [40], Deep Autoencoder (DAE) [41], and Generative Adversarial Network (GAN) [42] techniques. If the problem demands a discriminative model, Convolutional Neural Network (CNN) [43] and Recurrent Neural Network (RNN) [44] might be chosen. However, the problem may need to utilize a hybrid model if the dataset is multi-modal (i.e., image, text, speech) or different algorithms may enhance the accuracy by fusing multiple techniques. To simplify the process of building and training Deep Neural Networks by providing pre-built libraries and abstractions, several popular deep learning frameworks (i.e., TensorFlow, Torch, Theano, etc.) have been introduced. Note that Sections 4-7 present the contents of this cyberbullying ecosystem in detail. Although numerous studies have been conducted on cyberbullying, a limited number of survey papers on DL-based cyberbullying have been found in the literature.