# A Review on Deep-Learning-Based Cyberbullying Detection
## 2 Related Works  
This section briefly discusses a few notable review papers on machine learning-based cyberbullying detection. We also present a comparison between our work with these existing works to show the novelty of our work. We have mentioned the survey papers according to the year of publication. [PERSON] et al. [30] first detected cyberbullying in Arabic. They also offered a brief background on cyberbullying, related technologies, and an exhaustive survey on multilingual cyberbullying detection techniques. They finally proposed a plan to address the problem of Arabic cyberbullying. [PERSON] et al. [26] presented a systematic review on cyberbullying detection approaches. They divided the existing approaches into four categories based on their substantial literature review: supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based techniques commonly use classifiers such as SVM and naive Bayes to create predictive models for cyberbullying detection. Lexicon-based techniques identify cyberbullying using word lists and the presence of words within the lists. Mixed-initiative approaches combine human-based reasoning with one or more of the above-mentioned approaches to identify bullying. Rule-based approaches compare text to predetermined rules to identify bullying. The authors discovered two significant obstacles in cyberbullying detection research: the shortage of labeled datasets and academics' failure to take a holistic approach to cyberbully while creating detection systems. Their study effectively presents the current state of cyberbullying detection research with traditional ML techniques. [PERSON] et al. [27] analyzed the existing research on automatic cyberbullying detection in depth. Their findings revealed that cyberbullying is frequently misinterpreted in the literature, resulting in erroneous systems with limited real-world utility. Furthermore, there is no standard methodology for evaluating these systems, and the natural imbalance of datasets continues to be an issue. They identified the future trend of research on the issue toward a position more consistent with the phenomenon's description and depiction, allowing future systems to be more practical and focused. [PERSON] et al. [31] studied existing publications to detect aggressive behavior using ML approaches. They summarized and recognized the critical factors for detecting cyberbullying through ML techniques, especially supervised learning. For this purpose, they have utilized accuracy, precision-recall and f-measure to determine the area under the curve function for modeling the behaviors in cyberbullying. [PERSON] et al. [29] reveal some challenges and constraints of cyberbullying detection. Their paper represents a systematic literature review on automated cyberbullying detection that wraps all the steps in the ML pipeline. They also demonstrate that utilizing slang-based word embedding improves the detection of cyberbullying. [PERSON] et al. [28] give a thorough analysis of the past ten years of computational research concentrating on developing ML models for cyberbullying detection. A saturated corpus of 56 papers examined how humans are involved and considered directly or indirectly in building these detection algorithms. The authors focused on current algorithms' congruence with theories of cyberbullying. They then examined if and how current algorithms have incorporated humans. Finally, they shed insight into how academics have envisioned using current detection algorithms. Their evaluation reveals essential gaps in this research area due to the lack of human-centeredness in algorithm creation. A comparison of automated cyberbullying detection methods, including data annotation, preprocessing, and feature engineering, is presented in the study by [PERSON] et al. [45]. Emoji use in cyberbullying detection and the application of self-supervised learning to annotation are also covered.