## 2 Related Work and Multilingual Research
in [24], suggested a model using social media text written by bullies, victims, and bystanders for automatic cyberbullying detection. They described fine-grained annotation on English and Dutch language corpora. The model was based on a support vector machine algorithm and exploited a rich feature set and information sources that contributed most positively to the detection task. They developed an approach and produced 64% accuracy for the English language and 61% for the Dutch language. Work accomplished in [25] formed a cyberbullying detection algorithm on an unbalanced dataset intended to minimize the cyberbullying alert generation time by reducing the number of feature evaluations. The algorithm design was based on Instagram data and supervised ML techniques. Several studies have recently been published addressing the English language, and considerable research has been carried out recently in languages such as Urdu [26] and Arabic [27], among others. Work carried out in [28] was an initial attempt towards Turkish language hate speech detection. They formed a corpus of textual comments in the Turkish language, which was unbalanced in nature with respect to abusive and non-abusive contents and performed sentiment annotation. The study implemented CNN, machine learning classifiers, reweighted classifiers, social media features, NGrams, and weighing schemes to improve detection quality. The Arabic language hate speech detection task is addressed in [27]. The study developed an algorithm for the Arabic language based on word vectors, normalizing techniques with NLP, and supervised ML techniques. The corpus used for experimentation was unbalanced. Work suggests limitations in performance, which is approximately 0.30 in different metrics. Offensive and hate speech categorization for Danish language textual data was focused in [29]. The work proposed a detection framework generated mainly using Logistic Regression, Learned-BiLSTM (10 Epochs), Fast-BiLSTM (50 Epochs), and AUX-Fast-BiLSTM (40 Epochs). Different experimental settings could achieve an F1-score of 0.7. Due to the pervasiveness of social media content and their possible adverse impacts and alarming consequences on human well-being, an enormous number of academic events and shared tasks on linguistic analysis and identification of offensive and hate speech textual data have also taken place globally. Some of them include the GermEval 2021 Shared Task on the identification of toxic, engaging, and fact-claiming comments [30]; The First workshop on trolling, aggression, and cyberbullying (TRAC-2018), which focused on the phenomena of online aggression, trolling, and cyberbullying [31]; OffensEval 2020, which addressed multilingual offensive language identification in social media at an international workshop on semantic evaluation 2020 (SemEval 2020); and EVALITA 2018 [32], a shared task addressing Italian social media data. Communication by masses in regional dialects has become commonplace in the contemporary era. In its colloquial form, Roman Urdu has been widely adopted by Asian communities to share opinions and ideas easily. Recently, a limited number of preliminary research studies have also been contributed to in the Roman Urdu language. Work contributed in [33] is based on a lexicon-based approach and extracted unique words separated in bully and non-bully lexicons and polarity scores to categorize contents. While these approaches work fairly well when the text contains an explicit hate or abusive word, they often pose limitations in detecting implicit ones. Research in [34] proposed a CNN-gram model for offensive language detection. A corpus of almost 10k tweets was developed in RU with both coarse-grained and fine-grained labels. The study is limited in terms of preprocessing techniques, skewed datasets, and colloquial patterns generated by Roman Urdu users.