The widespread presence of offensive languages on social media has resulted in adverse effects on societal well-being. As a result, it has become very important to address this issue with high priority. Offensive languages exist in both explicit and implicit forms, with the latter being more challenging to detect. Current research in this domain encounters several challenges. Firstly, the existing datasets primarily rely on the collection of texts containing explicit offensive keywords, making it challenging to capture implicitly offensive contents that are devoid of these keywords. Secondly, usual methodologies tend to focus solely on textual analysis, neglecting the valuable insights that community information can provide. In this research paper, we introduce a novel dataset OffLanDat, a community based implicit offensive language dataset generated by ChatGPT containing data for 38 different target groups. Despite limitations in generating offensive texts using ChatGPT due to ethical constraints, we present a prompt-based approach that effectively generates implicit offensive languages. To ensure data quality, we evaluate our data with human. Additionally, we employ a prompt-based Zero-Shot method with ChatGPT and compare the detection results between human annotation and ChatGPT annotation. We utilize existing state-of-the-art models to see how effective they are in detecting such languages. We will make our code and dataset public for other researchers.

翻译：社交媒体上冒犯性语言的广泛存在对社会福祉造成了负面影响，因此迫切需要优先解决这一问题。冒犯性语言既有显性形式也有隐性形式，其中后者更难检测。当前该领域研究面临多项挑战：首先，现有数据集主要依赖包含显性冒犯关键词的文本收集，难以捕捉不含此类关键词的隐含冒犯内容；其次，常规方法仅聚焦于文本分析，忽视了社区信息所能提供的宝贵见解。本文提出新型数据集OffLanDat——一个由ChatGPT生成的、覆盖38个不同目标群体的基于社区的隐含冒犯性语言数据集。尽管因伦理约束限制了ChatGPT直接生成冒犯性文本，我们提出了一种基于提示的方法，有效生成了隐含冒犯性语言。为确保数据质量，我们采用人工评估数据；同时，通过基于提示的零样本方法对比人工标注与ChatGPT标注的检测结果。我们利用现有最优模型检验其对此类语言的检测效能，并将公开代码与数据集供其他研究者使用。