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Turkish Hate Speech Superset

This dataset is a superset (N=41,423) resulting from the preprocessing and merge of all available Turkish 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.

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 post
  • labels: 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:

Additional datasets on demand

In our survey, we identified five additional datasets that are not public but can be requested to the authors, namely:

  • Large-Scale Hate Speech Detection with Cross-Domain Transfer
  • A Turkish Hate Speech Dataset and Detection System (Istanbul convention and refugees datasets)
  • SIU2023-NST - Hate Speech Detection Contest. 31. IEEE Conference on Signal Processing and Communications Applications, Istanbul. (Israel-Palestine and Turkey-Greece datasets)

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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