LennardZuendorf/Dynamically-Generated-Hate-Speech-Dataset
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--- 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)
数据集卡片 for Dynamically Generated Hate Speech Dataset
数据集描述
数据集摘要
Dynamically Generated Hate Speech Dataset 是由以下作者在论文中提出的：
- Bertie Vidgen
- Tristan Thrush
- Zeerak Waseem
- Douwe Kiela
数据集描述
- 版本: v0.2.2.csv 和 v0.2.3.csv
- 数据集结构:
- acl.id: 条目的唯一ID。
- Text: 输入的内容，所有内容都是合成的。
- Label: 二元变量，表示内容是否被识别为仇恨言论，取值为
hate或nothate。 - Type: 分类变量，为仇恨内容提供次级标签。对于
hate，可以取五个值：Animosity, Derogation, Dehumanization, Threatening 和 Support for Hateful Entities。对于nothate，类型为none。在第一轮中，类型未给出，标记为notgiven。 - Target: 分类变量，提供仇恨攻击的群体。可以包括交叉特征，多个群体可以被识别。对于
nothate，类型为none。在第一轮中，目标未给出，标记为notgiven。 - Level: 报告条目是原始内容还是扰动。
- Round: 分类变量，给出数据输入的轮次（1, 2, 3 或 4），以及条目是原始内容（a）还是扰动（b）。第一轮没有扰动。
- Round.base: 分类变量，仅用数字表示数据输入的轮次（1, 2, 3 或 4）。
- Split: 分类变量，给出条目被分配到的数据分割，取值为
train,dev和test。 - Annotator: 分类变量，给出输入内容的标注者。标注者ID是随机的字母数字字符串。数据集中有20个标注者。
- acl.id.matched: 匹配条目的ID，连接原始条目和扰动版本。
身份标签
- none: 非仇恨条目
- NoTargetRecorded: 仇恨条目，但没有记录目标
- mixed: 混合种族背景
- ethnic minority: 少数民族
- indig: 原住民
- indigwom: 原住民女性
- non-white: 非白人
- trav: 旅行者（包括罗姆人、吉普赛人）
- bla: 黑人
- blawom: 黑人女性
- blaman: 黑人男性
- african: 非洲人
- jew: 犹太人
- mus: 穆斯林
- muswom: 穆斯林女性
- wom: 女性
- trans: 跨性别者
- gendermin: 性别少数群体
- bis: 双性恋
- gay: 同性恋（男性和女性）
- gayman: 同性恋男性
- gaywom: 女同性恋
- dis: 残疾人
- working: 工人阶级
- old: 老年人
- asi: 亚洲人
- asiwom: 亚洲女性
- east: 东亚人
- south: 南亚人（例如印度人）
- chinese: 中国人
- pak: 巴基斯坦人
- arab: 阿拉伯人，包括中东人
- immig: 移民
- asylum: 寻求庇护者
- ref: 难民
- for: 外国人
- eastern european: 东欧人
- russian: 俄罗斯人
- pol: 波兰人
- hispanic: 西班牙裔人，包括拉丁裔和墨西哥人
- nazi: 纳粹（支持类型的仇恨）
- hitler: 希特勒（支持类型的仇恨）
代码
代码使用 hugging face transformers 库实现。
附加信息
许可信息
原始仓库未提供任何许可证，但可免费使用，需正确引用原始论文。
引用信息
引用为 arXiv:2012.15761 或 https://doi.org/10.48550/arXiv.2012.15761
