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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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size_categories: |
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- 1K<n<10K |
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configs: |
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- config_name: main_data |
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data_files: |
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- split: all |
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path: "data/CREHate_final_annotations.csv" |
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- split: sbic |
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path: "data/CREHate_SBIC.csv" |
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- split: cp |
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path: "data/CREHate_CP.csv" |
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- config_name: raw_data |
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data_files: "data/CREHate_raw_annotations.csv" |
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--- |
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# CREHate |
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Repository for the CREHate dataset, presented in the paper "[Exploring Cross-cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis](https://arxiv.org/abs/2308.16705)". (NAACL 2024) |
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## About CREHate (Paper Abstract) |
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<img src="CREHate_Dataset_Construction.png" width="400"> |
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Most hate speech datasets neglect the cultural diversity within a single language, resulting in a critical shortcoming in hate speech detection. |
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To address this, we introduce **CREHate**, a **CR**oss-cultural **E**nglish **Hate** speech dataset. |
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To construct CREHate, we follow a two-step procedure: 1) cultural post collection and 2) cross-cultural annotation. |
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We sample posts from the SBIC dataset, which predominantly represents North America, and collect posts from four geographically diverse English-speaking countries (Australia, United Kingdom, Singapore, and South Africa) using culturally hateful keywords we retrieve from our survey. |
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Annotations are collected from the four countries plus the United States to establish representative labels for each country. |
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Our analysis highlights statistically significant disparities across countries in hate speech annotations. |
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Only 56.2% of the posts in CREHate achieve consensus among all countries, with the highest pairwise label difference rate of 26%. |
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Qualitative analysis shows that label disagreement occurs mostly due to different interpretations of sarcasm and the personal bias of annotators on divisive topics. |
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Lastly, we evaluate large language models (LLMs) under a zero-shot setting and show that current LLMs tend to show higher accuracies on Anglosphere country labels in CREHate. |
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## Dataset Statistics |
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<div id="tab:3_1_stats"> |
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| **Data** | **Division** | **Source** | **\# Posts** | |
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|:---------|:--------|:-----------|:------------:| |
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| **CREHate** | **CC-SBIC** | Reddit | 568 | |
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| | | Twitter | 273 | |
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| | | Gab | 80 | |
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| | | Stormfront | 59 | |
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| | **CP** | Reddit | 311 | |
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| | | YouTube | 289 | |
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| | | **total** | **1,580** | |
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Data statistics and sources of CREHate. CC-SBIC refers to cross-culturally |
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re-annotated SBIC posts. CP refers to additionally collected cultural |
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posts from four countries (AU, GB, SG, and ZA), which are also |
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cross-culturally annotated. |
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</div> |
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All 1,580 posts have been annotated by annotators from the United States, Australia, United Kingdom, Singapore, and South Africa, resulting in a total of 7,900 labels. |
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## File Structure |
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- `data/`: Contains each country's labels for all posts, including a raw annotation file. Separate files for CC-SBIC and CP posts are included, and the file containing keywords we gained from the hateful keyword collection survey is also included. |
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- `llm_inference.py`: Codes used for LLM inference. OpenAI key is needed for inference in GPT models. By changing L468, you can test on prompts with country personas. |
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```shell |
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$ python llm_inference.py |
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``` |
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- `finetune/`: Contains codes and training data splits for finetuning BERT-variants (Appendix F) |
