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--- |
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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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- yo |
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- ha |
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- ig |
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- pcm |
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tags: |
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- hate speech |
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size_categories: |
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- 10K<n<100K |
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extra_gated_prompt: >- |
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You agree to not use the dataset to conduct any activity that causes harm to |
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human subjects. |
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extra_gated_fields: |
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Please provide more information on why you need this dataset and how you plan to use it: |
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type: text |
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--- |
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# Nigerian Hate Speech Superset |
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This dataset is a superset (N=38,686) of posts annotated as hateful or not. It results from the preprocessing and merge of all available hate speech datasets grounded geographically in Nigeria in April 2024. These datasets were identified through a systematic survey of hate speech datasets conducted in mid 2024. We only kept datasets that: |
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- are documented |
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- are publicly available or could be retrieved with the Twitter API |
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- focus on hate speech, defined broadly as "any kind of communication in speech, writing or behavior, that attacks or uses pejorative or discriminatory language with reference to a person or a group on the basis of who they are, in other words, based on their religion, ethnicity, nationality, race, color, descent, gender or other identity factor" (UN, 2019) |
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The survey procedure is further detailed in *our survey paper (link TBD)*. |
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## Data access and intended use |
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Please send an access request detailing how you plan to use the data. The main purpose of this dataset is to train and evaluate hate speech detection models, as well as study hateful discourse online. This dataset is NOT intended to train generative LLMs to produce hateful content. |
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## Columns |
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The dataset contains six columns: |
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- `text`: the annotated post |
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- `labels`: annotation of whether the post is hateful (`== 1`) or not (`==0`). As datasets have different annotation schemes, we systematically binarized the labels. |
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- `source`: origin of the data (e.g., Twitter) |
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- `dataset`: dataset the data is from (see "Datasets" part below) |
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- `nb_annotators`: number of annotators by post |
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## Datasets |
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The datasets that compose this superset are: |
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- NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data (`NaijaHate` in the `dataset` column) |
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- [paper link](https://aclanthology.org/2024.acl-long.488/) |
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- [raw data link](https://huggingface.co/datasets/worldbank/NaijaHate) |
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- HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeria (`Herdphobia` in the `dataset` column) |
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- [paper link](https://arxiv.org/pdf/2211.15262v1) |
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- [raw data link](https://github.com/hausanlp/HERDPhobia) |
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- EkoHate: Abusive Language and Hate Speech Detection for Code-switched Political Discussions on Nigerian Twitter (`ekohate`) |
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- [paper link](https://aclanthology.org/2024.woah-1.3/) |
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- [raw data link](https://github.com/befittingcrown/EkoHate) |
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## Additional datasets on demand |
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In our survey, we identified one additional dataset that is not public but can be requested to the authors: |
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- Detection of Hate Speech Code Mix Involving English and other Nigerian Languages |
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- [paper link](https://www.journal-isi.org/index.php/isi/article/view/595/284) |
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## Preprocessing |
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We drop duplicates. In case of non-binary labels, the labels are binarized (hate speech or not). We replace all usernames and links by fixed tokens to maximize user privacy. Further details on preprocessing can be found in the preprocessing code *[here] (TBD)*. |
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## Citation |
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TBD |
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``` |
