Due to the accretion of social media communication and adverse effects arising from its darker side on users, the field of automatic cyberbullying detection has become an emerging and evolving research trend [
8]. Research work in [
9] presents cyberbullying detection algorithm for textual data in English language. It is considered as one of the pioneers and highly cited research. They divided the task in text-classification sub problems related to sensitive topics and collected 4500 textual comments on controversial YouTube videos. This study implemented Naive Bayes, SVM and J48 binary and multiclass classifiers using general and specific feature sets. Study contributed in [
10] applied deep learning architectures on Kaggle dataset and conducted experimental analysis to determine the effectiveness and performance of deep learning algorithms LSTM, BiLSTM, RNN and GRU in detecting antisocial behavior. Authors in [
11] extracted data from four platforms i-e Twitter, YouTube, Wikipedia, and Reddit for developing an online hate classifier in English language using different classification techniques. Research carried out in [
12] developed an automated approach to detect toxicity and unethical behavior in online communication using word embeddings and varying neural network layers. They suggested that LSTM layers and mimicked word embedding can uncover such behavior with good accuracy level.
Few of the studies in recent years has been contributed by researchers on other languages apart from English. Research work in [
13] is unique and has gathered textual data from Instagram and twitter in Turkish language. They have implemented Naïve Bayes Multinomial, SVM, KNN and decision trees for cyberbullying detection along with Chi-square and information gain (IG) for feature selection. Work accomplished in [
14] also addresses the problem of cyberaggression in Turkish language. The work extends comparison of different machine learning algorithms and found optimal results using Light Gradient Boosting Model. Van Hee, Cynthia, et al. in [
15] proposed cyberbullying detection scheme for Dutch language. This is the first study on Dutch social media. Data was collected from ASKfm where users can ask and answer questions. The research uses default parameter settings for un-optimized linear kernel SVM based on n-grams and keyword system to identify bullying traces. F1 score for Dutch language was 61%. Problem of Arabic language cyberbullying detection was addressed and accomplished in [
16]. This study used Dataiku DSS and WEKA for ML tasks. The data was scrapped from facebook and twitter. The study concluded that even though the detection approach was not comparable with the other studies in English language but overall Naive Bayes and SVM yield reasonable performance. Research work in [
17] by Gomez-Adorno, Helena, et al. proposed automatic aggression detection for Spanish tweets. Several types of n-grams and linguistically motivated patterns were used but the best run could only achieve F1 score of 42.85%. Studies presented in [
18‐
20] are based on automatic detection of cyberbullying content in German language. Research conducted in [
18] proposed an approach based on SVM, CNN and ensemble model using unigram, bigrams and character N-grams for categorizing offensive tweets in German language. Research presented in [
21] attempted for the very first time to identify bullying traces in Indonesian language. Association Rule mining and FP growth text mining were used to identify trends for bullying patterns in Jakarta and Surabaya cities using social media text. This baseline study on Indonesian language was further extended by Nurrahmi, Hani et al. in [
22]. Study in [
23] made first attempt to develop a corpus of code-mixed data considering Hindi and English language. They proposed a scheme for hate speech detection using N-grams and lexical features. An ensemble approach by combining the predictions of Convolutional Neural Network (CNN) and SVM algorithms were used for identifying such patterns. The weighted F1 score for Hindi language ranged between 0.37 and 0.55 for different experiments [
24]. In the year 2019, Association for computational linguistics initiated the project for automatic detection of cyberbullying in Polish language [
25]. Research conducted in [
26] attempted to uncover cyberbullying patterns in Bengali language implementing passive aggressive, SVM and logistic regression. The optimum accuracy achieved was 78.1%. Recently, work contributed in [
27] presented first study in Roman Urdu using lexicon based approach. The dataset was highly skewed comprising of only 2.2% toxic data. As according to [
28], biased sampling and measurement errors are highly prone to classification errors when working on such datasets. Moreover, pattern detection based on predefined bullying and non-bullying lexicons were shortcomings of this study.
For automated detection of complex cyberbullying patterns, studies contributed by different scholars employ supervised, unsupervised, hybrid and deep learning models, vast feature engineering techniques, corpora, and social media platforms. However, the existing literature is mainly oriented towards unstructured data in English language. Some recent studies and projects have been initiated in other languages as discussed previously. To the best of our knowledge and literature review, no detailed work has been contributed in Roman Urdu to systematically analyze cyberbullying detection phenomenon using advanced preprocessing techniques (involving the usage of Roman Urdu resources) and deep learning approaches under different configurations.