## 2 Related Work and Multilingual Research  
This section presents an extensive survey regarding the techniques and advancements in multilingual hate speech and cyberbullying detection research. The field of AI has gained enormous research attention worldwide. We understand AI as \"machine-learning-based systems with the ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation\" [1]. Gaining insights from the big data, scholars establish solutions and models that aim to identify toxicity, cyberbullying instances, and hate speech via various techniques and NLP-based tools [12]. AI-based automated cyberbullying detection is gaining enormous attention of researchers, a progress that is also reflected in the scientific literature (such as [13; 14; 15]). Identifying cyberbullying is undeniably challenging since there are controversies about how cyber hate speech should be defined. The content can be perceived as cyberbullying and hate speech by a group, not by others, based on their individual subjective assessment and respective definitions. Currently, there are three significant paradigms of analysis, namely, the network analysis, the content analysis, and the fusion of both the network and content analysis. The network analysis usually considers the usage statistic parameter of digital media, such as data of sender and receiver; users' names, content, location, and time of communication; account IDs; and so on. The content analysis is typically focused on analyzing textual data and using natural language preprocessing techniques involving data acquisition, corpora development, text preprocessing, feature extraction, feature selection, and text-based classification [12]. A systematic review of the related articles on various digital libraries and study of review papers is evident that most of the research on content analysis, usage of computational methods, and ML algorithms for cyberbullying detection was only after 2011 [16]. Research work in [17] by [PERSON] et al. on cyberbullying detection from textual data is a highly cited study in the literature and is considered one of the pioneers. They split the task based on the sensitive topics in text-classification sub-problems and extracted textual comments on controversial YouTube videos. The study formed a detection approach based on SVM, naive Bayes, and J48 binary and multiclass models incorporating specific feature sets. They concluded that the performance of binary classifiers outweighs multiclass classifiers. A study in [18] performed at yahoo labs developed a corpus by taking comments posted on yahoo related to news and finance. The data were annotated for abusive language. Amazon's Mechanical Turk experiment was conducted, and [PERSON]'s regression model was used. Work conducted in [19] uses a deep learning-based approach for detecting racism and sexism-related hate speech. They trained a convolutional neural network and created word vectors using semantic information and 4 g, which were then downsized for classification. They incorporated 10-fold cross-validation based on word2 vec, and the overall f-score was 78.3%. In contrast, a study in [20] presented a time-aware supervised learning model-based profile, session, and different other comment-related features to detect anti-social and bullying instances from data. The model also penalized late detections. Work accomplished by [PERSON] et al. in [21] compares supervised models, expert systems, and a hybrid model combining the two. [PERSON] et al. in [22] employed the distributed computing technique for the detection of cyberbullying instances. Rather than only focusing on detection accuracy, their work also emphasizes robust performance. A study in [23] proposed a weakly supervised approach inferring vocabulary indicators and user roles in harassment. The model uses small seeds of vocabulary for a large corpus to extract bullying traces. [PERSON], [PERSON] et al.