Developing Linguistic Patterns to Mitigate Inherent Human Bias in
  Offensive Language Detection
Toygar Tanyel Besher Alkurdi Serkan Ayvaz
With the proliferation of social media, there has been a sharp increase in
offensive content, particularly targeting vulnerable groups, exacerbating
social problems such as hatred, racism, and sexism. Detecting offensive
language use is crucial to prevent offensive language from being widely shared
on social media. However, the accurate detection of irony, implication, and
various forms of hate speech on social media remains a challenge. Natural
language-based deep learning models require extensive training with large,
comprehensive, and labeled datasets. Unfortunately, manually creating such
datasets is both costly and error-prone. Additionally, the presence of
human-bias in offensive language datasets is a major concern for deep learning
models. In this paper, we propose a linguistic data augmentation approach to
reduce bias in labeling processes, which aims to mitigate the influence of
human bias by leveraging the power of machines to improve the accuracy and
fairness of labeling processes. This approach has the potential to improve
offensive language classification tasks across multiple languages and reduce
the prevalence of offensive content on social media.