## 1 Introduction  
With the rapid development of mobile network technology and the emergence of different social media platforms, Internet penetration rates continue to rise. The Internet has permeated people's daily lives and work, offering significant convenience through various applications for travel, shopping, communication, and more [1]. In the virtual society constructed by the Internet, netizens not only act as information recipients but also drive the widespread dissemination of information. Among these platforms, social media has experienced rapid growth in recent years, serving as a space for netizens to stay informed about current events, express opinions, and share snippets of their lives. However, on current social media platforms, some netizens exhibit a narrow perspective, hold extreme views, and are easily swayed by emotionally charged news reports, leading to the expression of offensive comments [2; 3]. Offensive language is a broad concept encompassing various terms such as abusive language, cyber-bullying, hate speech, irony, profanity, and more, depending on the data source or viewpoint definition [4]. In recent years, numerous measures have been introduced to address hate speech and offensive language. On 25 February 2019, United Nations Secretary-General [PERSON] unveiled the \"United Nations Strategy and Plan of Action on Hate Speech\" to tackle the escalating global issues of xenophobia, racism, violence against women, anti-Semitism, and anti-Muslim hatted [5]. From Rwanda to Bosnia and Cambodia, offensive and hate speech has evolved into heinous crimes, including ominous signs of ethnic cleansing [6]. Amidst the recent COVID-19 pandemic, some extremists have utilized Twitter to disseminate racist remarks. Swift moderation by Twitter promptly eradicated these messages, underscoring the platform's commitment to combatting racial speech [7]. In Malaysia, social media has also witnessed instances of violent incidents triggered by offensive language. In 2020, a 20-year-old girl, [PERSON], from Penang, tragically took her own life, leaving a note citing cyberbullying as the cause. The girl and her Bangladesh male colleague faced online harassment after uploading a dance video online. In 2023, Malaysian actress [PERSON] survived a suicide attempt by hanging and courageously shared her experience on social media, highlighting the horrors of online violence. That same year, a 27-year-old Chinese-Malaysian singer, [PERSON], fell victim to a fatal stabbing by an obsessive male fan. The assailant, in his 40s, had relentlessly pursued the victim on social media for years, demonstrating the profound impact of such behaviors not only on individuals but on society as a whole. Currently, research on offensive language detection (OLD) primarily focuses on high-resource languages, such as English, due to abundant dataset resources, monolingual dictionaries, and pre-trained language models [8; 9]. However, on social media platforms, diverse languages are often used for offensive language, including different national languages, ethnic languages, and regional dialects [10]. Research on OLD in low-resource languages faces significant challenges due to the lack of labeled training data instances. Early methods for detecting offensive comments mainly employed dictionaries and traditional machine learning (ML) tools. Dictionary-based approaches recorded malicious vocabulary for detection, while traditional ML tools relied on manual feature engineering to extract text features, construct ML models, and perform offensive comment detection [11]. However, these methods struggled to promptly maintain and update dictionaries for large volumes of user comments. Traditional ML methods required labor-intensive feature engineering, resulting in time-consuming efforts and models with room for improved generalization. In recent years, due to the superior practical results of deep learning (DL) models, OLD methods have predominantly shifted towards DL-based methods [12].