Developing linguistic patterns to mitigate inherent human bias in offensive language detection

HIGHLIGHTS

• What: The authors 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. In the subsequent section, the authors attempt to address this question further. * Correspondence: To accurately detect offensive language use, natural-language-based deep learning models require extensive training with large, comprehensive, and labeled datasets . . .
  

   

  

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