language:
- en
- yo
- ha
- ig
- pcm
size_categories:
- 10K<n<100K
task_categories:
- text-classification
Dataset Card for NaijaHate
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
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 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. 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 theclass
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 theclass
column), if it contains a personal attack or an insult that does not target an individual based on their identity - neutral (
0
in theclass
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 if you use this dataset.
@inproceedings{XXX}