Detection of Cyberbullying Patterns in Low Resource Colloquial Roman Urdu Microtext using Natural Language Processing, Machine Learning, and Ensemble Techniques

Abstract

Social media platforms have become a substratum for people to enunciate their opinions and ideas across the globe. Due to anonymity preservation and freedom of expression, it is possible to humiliate individuals and groups, disregarding social etiquette online, inevitably proliferating and diversifying the incidents of cyberbullying and cyber hate speech. This intimidating problem has recently sought the attention of researchers and scholars worldwide. Still, the current practices to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the recent prevalence of regional languages in social media, the dearth of language resources, and flexible detection approaches, specifically for low-resource languages. In this context, most existing studies are oriented towards traditional resource-rich languages and highlight a huge gap in recently embraced resource-poor languages. One such language currently adopted worldwide and more typically by South Asian users for textual communication on social networks is Roman Urdu. It is derived from Urdu and written using a Left-to-Right pattern and Roman scripting. This language elicits numerous computational challenges while performing natural language preprocessing tasks due to its inflections, derivations, lexical variations, and morphological richness. To alleviate this problem, this research proposes a cyberbullying detection approach for analyzing textual data in the Roman Urdu language based on advanced preprocessing methods, voting-based ensemble techniques, and machine learning algorithms. The study has extracted a vast number of features, including statistical features, word N-Grams, combined n-grams, and BOW model with TFIDF weighting in different experimental settings using GridSearchCV and cross-validation techniques. The detection approach has been designed to tackle users’ textual input by considering user-specific writing styles on social media in a colloquial and non-standard form. The experimental results show that SVM with embedded hybrid N-gram features produced the highest average accuracy of around 83%. Among the ensemble voting-based techniques, XGboost achieved the optimal accuracy of 79%. Both implicit and explicit Roman Urdu instances were evaluated, and the categorization of severity based on prediction probabilities was performed. Time complexity is also analyzed in terms of execution time, indicating that LR, using different parameters and feature combinations, is the fastest algorithm. The results are promising with respect to standard assessment metrics and indicate the feasibility of the proposed approach in cyberbullying detection for the Roman Urdu language.

Improving Cyberbullying Detection Through Adaptive External Dictionary in Machine Learning

“…Hint: 1 (Agrawal & Awekar, 2018;A. Ali & Syed, 2020;Dewani et al, 2023;Raj et al, 2021;Zubair et al, 2023). Table 4 shows that this research enhances accuracy, precision, memory, and F1-score evaluation.…”

The Use of a Large Language Model for Cyberbullying Detection

“…In their experiment, they tested the approaches with 30% of the dataset and showed a hybrid approach with lexicon features achieved superior performance, and models with SVM as classifier also achieved better performance amongst the ML algorithms deployed. Dewani et al [37] showed that SVM and embedded hybrid N-gram approach performed best in detecting cyberbullying in the Roman Urdu language context, with an accuracy of 83%. Suhas-Bharadwaj et al [38] applied extreme learning machine to classify cyberbullying messages, and achieved accuracy of 99% and an F1 score of 91%.…”