HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection

Authors

  • Binny Mathew Indian Institute of Technology, Kharagpur, India
  • Punyajoy Saha Indian Institute of Technology, Kharagpur, India
  • Seid Muhie Yimam Universität Hamburg, Germany
  • Chris Biemann Universität Hamburg, Germany
  • Pawan Goyal Indian Institute of Technology, Kharagpur, India
  • Animesh Mukherjee Indian Institute of Technology, Kharagpur, India

Keywords:

Social Welfare, Justice, Fairness and Equality, Computational Social Science, Other Social Impact

Abstract

Hate speech is a challenging issue plaguing the online social media. While better models for hate speech detection are continuously being developed, there is little research on the bias and interpretability aspects of hate speech. In this paper, we introduce HateXplain, the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in our dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling decision (as hate, offensive or normal) is based. We utilize existing state-of-the-art models and observe that even models that perform very well in classification do not score high on explainability metrics like model plausibility and faithfulness. We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities. We have made our code and dataset public for other researchers.

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Published

2021-05-18

How to Cite

Mathew, B., Saha, P., Yimam, S. M., Biemann, C., Goyal, P., & Mukherjee, A. (2021). HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14867-14875. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17745

Issue

Section

AAAI Special Track on AI for Social Impact