README.md

HateXplain: Annotated Dataset for Hate Speech and Offensive Language Explanation

HateXplain is a benchmark dataset for hate speech and offensive language detection, uniquely annotated with explanations and rationales. It is designed to support the development of interpretable models in online content moderation.

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📚 Dataset Summary

• Languages: English
• Samples: ~20,000 social media posts
• Annotations:
  • label: normal, offensive, or hatespeech
  • annotators: Multiple annotators per post with consensus labeling
  • rationales: Token-level binary rationales indicating why the label was chosen

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📁 Dataset Structure

Column	Description
post_id	Unique ID for each post (e.g., Twitter ID)
post_tokens	List of tokenized words from the post
annotators	List of dictionaries with label, annotator_id, and rationale
rationales	List of lists indicating which tokens are part of the explanation

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🔍 Example Entry

{
  "post_id": "1179055004553900032_twitter",
  "post_tokens": ["i", "dont", "think", "im", "getting", "my", "baby", "them", "white", "9", "s", "for", "school"],
  "annotators": [
    {
      "label": "normal",
      "annotator_id": 1,
      "rationale": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    }
  ],
  "rationales": []
}