# A Review on Deep-Learning-Based Cyberbullying Detection
## 1 Introduction  
Bully that occurs through the Internet is called cyberbullying, or cyber harassment [1]. There are different forms of cyberbullying that we can observe nowadays. For example, writing indecent textual content and sharing inappropriate visual content, e.g., memes. Social media platforms such as Facebook, Instagram, Twitter, etc. have made it easier for us to create content, interact with others and connect with others. However, unfiltered exchange of message content and the missing protection of private information can lead to bullying on different social media platforms [2]. Cyberbuliles could be in any form, including flames, vitriolic comments, sending offensive emails, humiliating pictures, mean remarks made by comments, and harassing others by posting on blogs or social media. Buliles may bring severe consequences such as depression, which may even lead people to commit suicide [3, 4]. Detecting cyberbullying is important to stop the threatening problem. Detection of cyberbullying is a difficult task due to the lack of identifiable parameters and the absence of a quantifiable standard. These contents are short, noisy, and unstructured, with incorrect spelling and symbols. Sometimes users intentionally obfuscate the words or phrases (e.g., b**t**s**t**h, a**t**s**t**e**t**.) in the sentence to deceive automatic detection [5]. Researchers use traditional machine learning (ML) algorithms to identify cyberbullying (i.e., text and image format), whereas the majority of the existing solutions are based on supervised learning methods [6]. Due to the subjective nature of bulk expressions, traditional MLmodels perform lower in detecting cyber harassment than the deep learning (DL)-based approaches [7]. A recent study shows that DL models outperform traditional ML algorithms regarding cyberbullying identification. Deep Neural Networks such as Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) [8], Long Short-term Memory (LSTM) [9], Bi-LSTM [10] and several other DL models can be used to detect this problem. Introducing DL-based models for detecting cyberbullying over traditional models has several benefits. When the data size is large, several studies [11; 12; 13; 14] have shown that DL algorithms outperform the traditional ML algorithms. Extracting features manually for text and image classification is a tedious and error-prone task. Sometimes exploiting traditional ML models are not reasonable to extract features, whereas in DL-based models, the task is performed automatically in the hidden layers. However, extracting features intelligently is an essential task during cyberbullying detection from text and image [15; 16; 17; 18]. In addition, understanding the context of the text or images increases the chance of providing better accuracy [19; 20; 21; 22; 23]. When we have minimum domain knowledge, the performance of ML algorithms is prone to deteriorating over time during solving complex problems [24]. Furthermore, conventional ML models suffer in model adaptability and transferability. For instance, if we train a model over a YouTube dataset and reuse the model over a Twitter dataset, using ML will not provide the desired results. DL models outperform ML models when we encounter complex linguistic expressions such as harassment with cyberbullying [25]. Figure 1 shows a typical cyberbullying detection pipeline where different steps from social media data input to cyberbullying detection have been explained. In this pipeline, the input dataset can contain either the text data or the image data, which are collected from social media. The cyberbully image data can be extracted by using two methods: optical character recognition (OCR) and image similarity. On the other hand, the raw text data are sent to data preprocessing for improving the data quality. Various text preprocessing steps including data cleaning, tokenization, stemming, lemmatization, and stop word removal are used for the reduction of dimensionality.