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--- |
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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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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- text-classification |
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--- |
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# Dataset Card for NaijaHate |
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<!-- Provide a quick summary of the dataset. --> |
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NaijaHate is a hate speech dataset tailored to the Nigerian context. It contains 35,976 annotated Nigerian tweets, including 29,999 tweets randomly sampled from Nigerian Twitter. For a complete description of the data, please refer to the [reference paper](https://arxiv.org/abs/2403.19260). |
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## Source Data |
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This dataset was sourced from a large Twitter dataset of 2.2 billion tweets posted between March 2007 and July 2023 and forming the timelines of 2.8 million Twitter users with a profile location in Nigeria. |
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## Dataset Structure |
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> |
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The dataset is made of four components detailed in the `dataset` column: two components used for training a hate speech model (`stratified` and `al`) and two components for model evaluation (`eval` and `random`). We detail each component below: |
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- `stratified`: 1,607 tweets collected through stratified sampling. We use both hate-related and community-related keywords as seeds. |
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- `al`: 2,405 tweets sampled through active learning |
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- `eval`: 1,965 tweets from the general Nigerian Twitter dataset and with high likelihood of being hateful according to 10 benchmarked hate speech classifiers |
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- `random`: 29,999 tweets randomly sampled from the general Nigerian Twitter dataset |
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## Annotation |
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We recruited a team of four Nigerian annotators, two female and two male, each of them from one of the four most populated Nigerian ethnic groups -- Hausa, Yoruba, Igbo and Fulani. |
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We followed a prescriptive approach by instructing annotators to strictly adhere to extensive annotation guidelines describing our taxonomy of hate speech (see reference paper for full guidelines). |
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Tweets are annotated as belonging to one of three classes: |
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- hateful (`2` in the `class` column) if it contains an attack on an individual or a group based on the perceived possession of a certain characteristic (e.g., gender, race) |
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- offensive (`1` in the `class` column), if it contains a personal attack or an insult that does not target an individual based on their identity |
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- neutral (`0` in the `class` column) if it is neither hateful nor offensive. |
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If a tweet is labeled as hateful, it is also annotated for the communities being targeted. The possible target communities in our dataset are: |
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- Christians (`christian` column) |
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- Muslims (`muslim`) |
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- Northerners (`northerner`) |
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- Southerners (`southerner`) |
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- Hausas (`hausa`) |
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- Fulanis (`fulani`) |
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- Yorubas (`yoruba`) |
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- Igbos (`igbo`) |
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- Women (`women`) |
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- LGBTQ+ (`lgbtq+`) |
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- Herdsmen (`herdsmen`) |
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- Biafra (`biafra`) |
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Each tweet was labeled by three annotators. For the three-class annotation task, the 3 annotators agreed on 90\% of labeled tweets, 2 out of 3 agreed in 9.5\% of cases, and all three of them disagreed in 0.5\% of cases (Krippendorff's alpha = 0.7). |
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## Language Composition |
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We further detail the share of each language by dataset component below: |
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| | Stratified + active learning sets (%) | Random set (%) | |
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|----------------------------------|-----------------------------------|------------| |
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| English | 74.2 | 77 | |
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| English & Nigerian Pidgin | 11 | 1.5 | |
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| English & Yoruba | 4.2 | - | |
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| Nigerian Pidgin | 3.6 | 7.3 | |
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| English & Hausa | 2.2 | - | |
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| Hausa | 1 | 1.2 | |
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| Yoruba | - | 1 | |
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| URLs | - | 6 | |
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| Emojis | - | 2.3 | |
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## BibTeX entry and citation information |
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Please cite the [reference paper](https://arxiv.org/abs/2403.19260) if you use this dataset. |
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```bibtex |
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@article{tonneau2024naijahate, |
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title={NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data}, |
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author={Tonneau, Manuel and de Castro, Pedro Vitor Quinta and Lasri, Karim and Farouq, Ibrahim and Subramanian, Lakshminarayanan and Orozco-Olvera, Victor and Fraiberger, Samuel}, |
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journal={arXiv preprint arXiv:2403.19260}, |
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year={2024} |
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} |
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``` |