--- task_categories: - text-classification - text-generation language: - en tags: - not-for-all-audiences - legal pretty_name: dynamically generated hate speech dataset --- # Dataset Card for dynamically generated hate speech dataset ## Dataset Description - **Homepage:** [GitHub](https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset) - **Point of Contact:** [bertievidgen@gmail.com](mailto:bertievidgen@gmail.com) ### Dataset Summary This is a copy of the Dynamically-Generated-Hate-Speech-Dataset, presented in [this paper](https://arxiv.org/abs/2012.15761) by - **Bertie Vidgen**, **Tristan Thrush**, **Zeerak Waseem** and **Douwe Kiela** ## Original README from [GitHub](https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset/blob/main/README.md) ## Dynamically-Generated-Hate-Speech-Dataset ReadMe for v0.2 of the Dynamically Generated Hate Speech Dataset from Vidgen et al. (2021). If you use the dataset, please cite our paper in the Proceedings of ACL 2021, and available on [Arxiv](https://arxiv.org/abs/2012.15761). Contact Dr. Bertie Vidgen if you have feedback or queries: bertievidgen@gmail.com. The full author list is: Bertie Vidgen (The Alan Turing Institute), Tristan Thrush (Facebook AI Research), Zeerak Waseem (University of Sheffield) and Douwe Kiela (Facebook AI Research). This paper is an output of the Dynabench project: https://dynabench.org/tasks/5#overall ### Dataset descriptions v0.2.2.csv is the full dataset used in our ACL paper. v0.2.3.csv removes duplicate entries, all of which occurred in round 1. Duplicates come from two sources: (1) annotators entering the same content multiple times and (2) different annotators entering the same content. The duplicates are interesting for understanding the annotation process, and the challenges of dynamically generating datasets. However, they are likely to be less useful for training classifiers and so are removed in v0.2.3. We did not lower case the text before removing duplicates as capitalisations contain potentially useful signals. ### Overview The Dynamically Generated Hate Speech Dataset is provided in one table. 'acl.id' is the unique ID of the entry. 'Text' is the content which has been entered. All content is synthetic. 'Label' is a binary variable, indicating whether or not the content has been identified as hateful. It takes two values: hate, nothate. 'Type' is a categorical variable, providing a secondary label for hateful content. For hate it can take five values: Animosity, Derogation, Dehumanization, Threatening and Support for Hateful Entities. Please see the paper for more detail. For nothate the 'type' is 'none'. In round 1 the 'type' was not given and is marked as 'notgiven'. 'Target' is a categorical variable, providing the group that is attacked by the hate. It can include intersectional characteristics and multiple groups can be identified. For nothate the type is 'none'. Note that in round 1 the 'target' was not given and is marked as 'notgiven'. 'Level' reports whether the entry is original content or a perturbation. 'Round' is a categorical variable. It gives the round of data entry (1, 2, 3 or 4) with a letter for whether the entry is original content ('a') or a perturbation ('b'). Perturbations were not made for round 1. 'Round.base' is a categorical variable. It gives the round of data entry, indicated with just a number (1, 2, 3 or 4). 'Split' is a categorical variable. it gives the data split that the entry has been assigned to. This can take the values 'train', 'dev' and 'test'. The choice of splits is explained in the paper. 'Annotator' is a categorical variable. It gives the annotator who entered the content. Annotator IDs are random alphanumeric strings. There are 20 annotators in the dataset. 'acl.id.matched' is the ID of the matched entry, connecting the original (given in 'acl.id') and the perturbed version. For identities (recorded under 'Target') we use shorthand labels to constructed the dataset, which can be converted (and grouped) as follows: none -> for non hateful entries NoTargetRecorded -> for hateful entries with no target recorded mixed -> Mixed race background ethnic minority -> Ethnic Minorities indig -> Indigenous people indigwom -> Indigenous Women non-white -> Non-whites (attacked as 'non-whites', rather than specific non-white groups which are generally addressed separately) trav -> Travellers (including Roma, gypsies) bla -> Black people blawom -> Black women blaman -> Black men african -> African (all 'African' attacks will also be an attack against Black people) jew -> Jewish people mus -> Muslims muswom -> Muslim women wom -> Women trans -> Trans people gendermin -> Gender minorities, bis -> Bisexual gay -> Gay people (both men and women) gayman -> Gay men gaywom -> Lesbians dis -> People with disabilities working -> Working class people old -> Elderly people asi -> Asians asiwom -> Asian women east -> East Asians south -> South Asians (e.g. Indians) chinese -> Chinese people pak -> Pakistanis arab -> Arabs, including people from the Middle East immig -> Immigrants asylum -> Asylum seekers ref -> Refguees for -> Foreigners eastern european -> Eastern Europeans russian -> Russian people pol -> Polish people hispanic -> Hispanic people, including latinx and Mexicans nazi -> Nazis ('Support' type of hate) hitler -> Hitler ('Support' type of hate) ### Code Code was implemented using hugging face transformers library. ## Additional Information ### Licensing Information The original repository does not provide any license, but is free for use with proper citation of the original paper in the Proceedings of ACL 2021, available on [Arxiv](https://arxiv.org/abs/2012.15761) ### Citation Information cite as [arXiv:2012.15761](https://arxiv.org/abs/2012.15761) or [https://doi.org/10.48550/arXiv.2012.15761](https://[doi.org/10.48550/arXiv.2012.15761)