## 1 Introduction  
In recent years, with the widespread popularity of social media platforms such as Twitter, Facebook, and microblogging, the scope and scale of the online discussions people engage in has expanded. Social networks provide people around the world with the opportunity to express and share their thoughts instantly and widely. Unfortunately, a few users abuse the anonymity provided by online social media as an advantage and engage in social behavior that would not be acceptable in the real world, resulting in the constant emergence of offensive language. The reasons for the proliferation of offensive language may be: (1) due to the accessibility and anonymity of the platform [1]. Social media provides a relatively anonymous and virtual platform allowing people to express their opinions without revealing their identities. This anonymity may make some users more prone to adopting offensive language because they do not have to bear the social consequences they might face when communicating face-to-face. (2) Social media has emphasized freedom of speech, allowing people to express their views more freely, even extreme or offensive ones, which raises questions about the balance between free speech and offensive speech. (3) There may be some commercial motives. These reasons provide a fertile environment for the spread of offensive and harmful content. Due to the lack of a well-developed legal system regarding offensive language, the difficulty of setting limits for the evolving cyberspace, the increased need for individuals to express their views and opponents to fight back, and the delay in manual checks by Internet operators, the spread of offensive language online has gained new momentum. It continues to challenge policymakers and the research community [2]. Offensive language often involves racism, sexism, and negative stereotypes about countries, religious entities, individuals, or minorities [1]. This language can easily cause adverse effects, such as spreading hate and harassment. In addition, offensive language encourages inappropriate online behavior, such as personal attacks, cyberbullying [3], and hate speech, which seriously undermines the harmony of the online environment [4]. In order to solve the above problems, many social media platforms check users' comments for offensive language and other violations through manual review mechanisms. Still, compared to the scale of people's online discussions, the manual review can only cover a small part of online interactions and is particularly costly in human resources [5]. Automatic identification of offensive language allows the platform to detect offensive language and remove it faster and more efficiently than manual filtering, which is very time-consuming. Therefore, the task of automatic offensive language detection [6; 7] has been widely discussed by researchers. Currently, there are several methods for detecting offensive language and inappropriate content. These methods include rule-based, machine learning, deep learning, integrated, and pre-training methods in deep learning. The rule-based approach uses predefined rules and patterns to detect offensive language. For example, you can create rules to detect common offensive words, threatening phrases, or abusive speech. While these methods are relatively simple, they often require a lot of manual rule design and updating. Machine learning methods that can grasp diverse connections between text fragments and predict specific outputs for specific inputs using pre-labeled data as training material is widely favored in the scholarly exploration of detecting offensive language. Common machine learning algorithms include support vector machine (SVM), decision tree, random forest, etc. With the continuous improvement of machine learning models and the dramatic improvement in the performance of modern models trained for language tasks, AI-based offensive language detection has become a reality. Deep learning methods have made remarkable progress in the detection of offensive language.