OffensiveLang: A Community Based Implicit Offensive Language Dataset

TL;DR

This paper introduces OffensiveLang, a novel community-based dataset of implicit offensive language generated with ChatGPT, addressing the challenge of detecting subtle hate speech without explicit keywords and incorporating community context.

Contribution

The paper presents a new dataset for implicit offensive language, created using prompt-based ChatGPT generation and human evaluation, filling gaps in existing explicit keyword-based datasets.

Findings

ChatGPT effectively generates implicit offensive language data.

State-of-the-art models show limited effectiveness in detecting implicit offensive content.

Community context enhances understanding of offensive language.

Abstract

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