Datasets:

manueltonneau
/

NaijaHate

@@ -7,139 +7,62 @@ language:
 - pcm
 size_categories:
 - 10K<n<100K
 ---
 # Dataset Card for NaijaHate
 <!-- Provide a quick summary of the dataset. -->
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
-## Dataset Details
-### Dataset Description
-<!-- Provide a longer summary of what this dataset is. -->
-- **Curated by:** [More Information Needed]
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-<!-- Provide the basic links for the dataset. -->
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-- **Demo [optional]:** [More Information Needed]
-## Uses
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-### Direct Use
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-[More Information Needed]
-### Out-of-Scope Use
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 ## Dataset Structure
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 ## Dataset Creation
-### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
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 ### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-#### Data Collection and Processing
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-#### Who are the source data producers?
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-### Annotations [optional]
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-#### Annotation process
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-#### Who are the annotators?
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-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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 - pcm
 size_categories:
 - 10K<n<100K
+task_categories:
+- text-classification
 ---
 # Dataset Card for NaijaHate
 <!-- Provide a quick summary of the dataset. -->
+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 (TODO).
 ## Dataset Structure
 <!-- 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. -->
+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:
+- `stratified`:
+- `al`:
+- `eval`:
+- `random`:
 ## Dataset Creation
 ### Source Data
+This dataset was sourced for 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.
+### Annotation
+We recruited a team of four annotators, two female and two male, each of them from one of the four most populated Nigerian ethnic groups -- Hausa, Yoruba, Igbo and Fulani.
+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).
+Tweets are annotated as belonging to one of three classes:
+- 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)
+- 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
+- neutral (`0` in the `class` column) if it is neither hateful nor offensive.
+If a tweet is labeled as hateful, it is also annotated for the communities being targeted. The possible target communities in our dataset are:
+- Christians (`christian` column)
+- Muslims (`muslim`)
+- Northerners (`northerner`)
+- Southerners (`southerner`)
+- Hausas (`hausa`)
+- Fulanis (`fulani`)
+- Yorubas (`yoruba`)
+- Igbos (`igbo`)
+- Women (`women`)
+- LGBTQ+ (`lgbtq+`)
+- Herdsmen (`herdsmen`)
+- Biafra (`biafra`)
+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).
+## BibTeX entry and citation information
+TODO
+Please cite the [reference paper](https://aclanthology.org/2022.lrec-1.27/) if you use this dataset.
+```bibtex
+@inproceedings{XXX}
+```