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German Hate Speech Superset
This dataset is a superset (N=50,545) resulting from the preprocessing and merge of all available German hate speech datasets in April 2024. These datasets were identified through a systematic survey of hate speech datasets conducted in early 2024. We only kept datasets that:
- are documented
- are publicly available
- 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.
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:
- RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets (
RP-mod-crowd
in thedataset
column) - Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis (
refugees
in thedataset
column) - Detecting Offensive Statements Towards Foreigners in Social Media (
Bretschneider
) - Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages (
hasoc
) - Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset (
gahd
)
Additional datasets on demand
In our survey, we identified one additional dataset that is not public but can be requested to the authors, namely:
- DeTox: A Comprehensive Dataset for German Offensive Language and Conversation Analysis
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.
Citation
Please cite our survey paper if you use this dataset.
@article{tonneau2024languages,
title={From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets},
author={Tonneau, Manuel and Liu, Diyi and Fraiberger, Samuel and Schroeder, Ralph and Hale, Scott A and R{\"o}ttger, Paul},
journal={arXiv preprint arXiv:2404.17874},
year={2024}
}
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