  The widespread presence of hateful languages on social media has resulted in
adverse effects on societal well-being. As a result, addressing this issue with
high priority has become very important. Hate speech or 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,
common 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 OffensiveLang, a community based implicit
offensive language dataset generated by ChatGPT 3.5 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 the dataset 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. The dataset is available here:
https://github.com/AmitDasRup123/OffensiveLang
