Hate Speech Supersets
Collection
Supersets combining all publicly available hate speech corpora in eight languages and for three countries.
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Datasets:
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Nigerian Hate Speech Superset
This dataset is a superset (N=48,076) of posts annotated as hateful or not. It results from the preprocessing and merge of all available hate speech datasets grounded geographically in Kenya in April 2024. These datasets were identified through a systematic survey of hate speech datasets conducted in mid 2024. We only kept datasets that:
- are documented
- are publicly available or could be retrieved with the Twitter API
- 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)
The survey procedure is further detailed in our survey paper (link TBD).
Data access and intended use
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.
Columns
The dataset contains six columns:
text: the annotated postlabels: annotation of whether the post is hateful (== 1) or not (==0). As datasets have different annotation schemes, we systematically binarized the labels.source: origin of the data (e.g., Twitter)dataset: dataset the data is from (see "Datasets" part below)nb_annotators: number of annotators by post
Datasets
The datasets that compose this superset are:
- Building and Annotating a Codeswitched Hate Speech Corpora (
code-switchedin thedatasetcolumn)
Additional datasets on demand
In our survey, we identified one additional dataset that is not public but can be requested to the authors:
- Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments
Preprocessing
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).
Citation
TBD
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