Culturally Aware Malay–English Code-Mixed Hate Speech Detection: A Systematic Review and Research Taxonomy
Social media platforms such as X, formerly Twitter, have become major channels for communication, information sharing and public discussion. However, the rapid growth of user-generated content has also increased the spread of hate speech and offensive language. Automated hate speech detection remains challenging in multilingual and code-mixed environments, where users frequently combine languages, informal spelling, slang, abbreviations and culturally specific expressions. In Malaysia, online discourse often involves Malay-English code-mixing, commonly referred to as Manglish, which creates additional challenges for natural language processing systems. This study presents an evidence-informed systematic review and research-readiness taxonomy for culturally aware Malay-English hate speech detection. Unlike conventional reviews that mainly summarize model architecture and performance scores, this review critically evaluates existing studies based on dataset availability, code-mix authenticity, annotation practice, cultural sensitivity, model architecture, evaluation strategy, explainability, robustness and deployment readiness. To strengthen this review, this article incorporates a completed empirical case study on Manglish hate-speech detection, using posts collected from X (formerly Twitter). The case study used keyword-based data collection, Malaya NLP-based language filtering, bilingual manual annotation, manual class balancing and transformer-based evaluation using mBERT, XLNet and XLM-RoBERTa. The case evidence is used only as an empirical lens to illustrate practical challenges in dataset curation, class imbalance, lexical overlap and model robustness. It is not positioned as a new benchmark dataset or an independent experimental contribution. The review finds that existing Malay and Malay-English resources remain fragmented. Some datasets are monolingual Malay hate speech datasets, some are bilingual but language-separated, while others are code-mixed but developed for sentiment analysis rather than hate speech detection. Transformer-based models such as BERT, mBERT and XLM-RoBERTa show strong potential, but their results are difficult to compare due to inconsistent datasets, label definitions, class distributions, evaluation metrics and limited robustness testing. The main novelty of this review is the proposed taxon- omy that evaluates the field through six readiness dimensions, which are data, linguistic, cultural, modelling, evaluation and deployment readiness. The findings from this study provide a structured foundation for developing culturally aware, robust, explainable and deployable hate speech detection systems for Malay-English code-mixed social media.