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--- |
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license: cc-by-sa-4.0 |
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task_categories: |
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- text-classification |
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language: |
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- en |
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tags: |
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- hate-speech |
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- social-media |
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- intent-detection |
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- impact-analysis |
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- content-moderation |
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size_categories: |
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- 1K<n<10K |
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--- |
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# I2-HATE: Intent and Impact Hate Speech Dataset |
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## Dataset Description |
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The I2-HATE dataset introduces a novel dual-taxonomy approach to hate speech detection that separately captures **Intent** (the speaker's underlying motivations) and **Impact** (potential societal consequences). Unlike traditional hate speech datasets that use simple categorical labels, I2-HATE enables more nuanced content moderation by distinguishing between why hate speech is produced and what harm it may cause. |
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This dataset contains **3,296 annotated social media posts** with multi-label annotations across 7 intent categories and 8 impact categories. |
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### Key Features |
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- **Size**: 3,296 samples |
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- **Multi-label annotations**: Each post can have multiple intent and impact labels |
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- **Dual taxonomy framework**: Separate classification of Intent and Impact |
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### Intent Labels (7 categories) |
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1. **Affective Aggression [AA]**: Emotional outbursts and aggressive expressions |
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2. **Derisive Trolling [DT]**: Mocking and ridicule intended to provoke |
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3. **Dominance & Subjugation [D&S]**: Assertions of superiority and control |
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4. **Ideological Expression [IE]**: Promotion of specific ideological beliefs |
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5. **Performative Reinforcement [PR]**: Public displays to reinforce group identity |
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6. **Strategic Incitement [SI]**: Deliberate attempts to mobilize others |
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7. **Threat & Intimidation [T&I]**: Direct or implied threats |
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### Impact Labels (8 categories) |
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1. **Disruption of Public Discourse [DPD]**: Undermining constructive dialogue |
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2. **Glorification of Hate [GH]**: Celebrating hateful acts or ideologies |
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3. **Incitement to Discrimination/Exclusion [ID/E]**: Encouraging discriminatory behavior |
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4. **Incitement to Violence [IV]**: Promoting violent actions |
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5. **Misinformation/Disinformation Nexus [M/DN]**: Spreading false narratives |
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6. **Normalization of Prejudice [NP]**: Making prejudice socially acceptable |
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7. **Psychological Harm [PH]**: Causing emotional or mental distress |
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8. **Stigmatization & Dehumanization [S&D]**: Devaluing individuals or groups |
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## Dataset Structure |
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Each sample contains: |
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- `sample_id`: Unique identifier (integer) |
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- `text`: The social media post text |
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- `Intent Labels`: Comma-separated intent categories |
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- `Impact Labels`: Comma-separated impact categories |
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## Citation |
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If you use the I2-HATE dataset in your research, please cite our paper: |
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```bibtex |
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@inproceedings{singhal2026wordswear, |
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title={When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech}, |
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author={Singhal, Priyansh and Joshi, Piyush}, |
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booktitle={Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)}, |
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year={2026}, |
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publisher={Association for Computational Linguistics} |
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} |
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``` |
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## License |
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This dataset is released under **CC BY-SA 4.0** (Creative Commons Attribution-ShareAlike 4.0 International). You are free to: |
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- Share and redistribute the dataset |
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- Adapt and build upon the dataset |
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Under the following terms: |
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- **Attribution**: You must give appropriate credit by citing our paper |
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- **ShareAlike**: If you remix, transform, or build upon the dataset, you must distribute your contributions under the same CC BY-SA 4.0 license |
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## Contact |
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For questions or issues regarding the dataset, please contact: |
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- Priyansh Singhal |
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## Ethical Considerations |
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This dataset contains real social media posts with hate speech content. Researchers using this dataset should: |
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- Handle the data responsibly and ethically |
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- Consider potential biases in annotation |
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- Use the dataset solely for research purposes to combat online hate speech |
