--- annotations_creators: - crowdsourced - expert-generated language_creators: - found - other language: - en license: - agpl-3.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - multi-label-classification - sentiment-classification paperswithcode_id: ethos pretty_name: onlinE haTe speecH detectiOn dataSet tags: - Hate Speech Detection dataset_info: - config_name: binary features: - name: text dtype: string - name: label dtype: class_label: names: '0': no_hate_speech '1': hate_speech splits: - name: train num_bytes: 124823 num_examples: 998 download_size: 123919 dataset_size: 124823 - config_name: multilabel features: - name: text dtype: string - name: violence dtype: class_label: names: '0': not_violent '1': violent - name: directed_vs_generalized dtype: class_label: names: '0': generalied '1': directed - name: gender dtype: class_label: names: '0': 'false' '1': 'true' - name: race dtype: class_label: names: '0': 'false' '1': 'true' - name: national_origin dtype: class_label: names: '0': 'false' '1': 'true' - name: disability dtype: class_label: names: '0': 'false' '1': 'true' - name: religion dtype: class_label: names: '0': 'false' '1': 'true' - name: sexual_orientation dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: train num_bytes: 79112 num_examples: 433 download_size: 62836 dataset_size: 79112 config_names: - binary - multilabel --- # Dataset Card for Ethos ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [ETHOS Hate Speech Dataset](https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset) - **Repository:**[ETHOS Hate Speech Dataset](https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset) - **Paper:**[ETHOS: an Online Hate Speech Detection Dataset](https://arxiv.org/abs/2006.08328) ### Dataset Summary ETHOS: onlinE haTe speecH detectiOn dataSet. This repository contains a dataset for hate speech detection on social media platforms, called Ethos. There are two variations of the dataset: - **Ethos_Dataset_Binary**: contains 998 comments in the dataset alongside with a label about hate speech *presence* or *absence*. 565 of them do not contain hate speech, while the rest of them, 433, contain. - **Ethos_Dataset_Multi_Label** which contains 8 labels for the 433 comments with hate speech content. These labels are *violence* (if it incites (1) or not (0) violence), *directed_vs_general* (if it is directed to a person (1) or a group (0)), and 6 labels about the category of hate speech like, *gender*, *race*, *national_origin*, *disability*, *religion* and *sexual_orientation*. ***Ethos /ˈiːθɒs/*** is a Greek word meaning “character” that is used to describe the guiding beliefs or ideals that characterize a community, nation, or ideology. The Greeks also used this word to refer to the power of music to influence emotions, behaviors, and even morals. ### Supported Tasks and Leaderboards [More Information Needed] - `text-classification-other-Hate Speech Detection`, `sentiment-classification`,`multi-label-classification`: The dataset can be used to train a model for hate speech detection. Moreover, it can be used as a benchmark dataset for multi label classification algorithms. ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances A typical data point in the binary version comprises a comment, with a `text` containing the text and a `label` describing if a comment contains hate speech content (1 - hate-speech) or not (0 - non-hate-speech). In the multilabel version more labels like *violence* (if it incites (1) or not (0) violence), *directed_vs_general* (if it is directed to a person (1) or a group (0)), and 6 labels about the category of hate speech like, *gender*, *race*, *national_origin*, *disability*, *religion* and *sexual_orientation* are appearing. An example from the binary version, which is offensive, but it does not contain hate speech content: ``` {'text': 'What the fuck stupid people !!!', 'label': '0' } ``` An example from the multi-label version, which contains hate speech content towards women (gender): ``` {'text': 'You should know women's sports are a joke', `violence`: 0, `directed_vs_generalized`: 0, `gender`: 1, `race`: 0, `national_origin`: 0, `disability`: 0, `religion`: 0, `sexual_orientation`: 0 } ``` ### Data Fields Ethos Binary: - `text`: a `string` feature containing the text of the comment. - `label`: a classification label, with possible values including `no_hate_speech`, `hate_speech`. Ethis Multilabel: - `text`: a `string` feature containing the text of the comment. - `violence`: a classification label, with possible values including `not_violent`, `violent`. - `directed_vs_generalized`: a classification label, with possible values including `generalized`, `directed`. - `gender`: a classification label, with possible values including `false`, `true`. - `race`: a classification label, with possible values including `false`, `true`. - `national_origin`: a classification label, with possible values including `false`, `true`. - `disability`: a classification label, with possible values including `false`, `true`. - `religion`: a classification label, with possible values including `false`, `true`. - `sexual_orientation`: a classification label, with possible values including `false`, `true`. ### Data Splits The data is split into binary and multilabel. Multilabel is a subset of the binary version. | | Instances | Labels | | ----- | ------ | ----- | | binary | 998 | 1 | | multilabel | 433 | 8 | ## Dataset Creation ### Curation Rationale The dataset was build by gathering online comments in Youtube videos and reddit comments, from videos and subreddits which may attract hate speech content. ### Source Data #### Initial Data Collection and Normalization The initial data we used are from the hatebusters platform: [Original data used](https://intelligence.csd.auth.gr/topics/hate-speech-detection/), but they were not included in this dataset #### Who are the source language producers? The language producers are users of reddit and Youtube. More informations can be found in this paper: [ETHOS: an Online Hate Speech Detection Dataset](https://arxiv.org/abs/2006.08328) ### Annotations #### Annotation process The annotation process is detailed in the third section of this paper: [ETHOS: an Online Hate Speech Detection Dataset](https://arxiv.org/abs/2006.08328) #### Who are the annotators? Originally anotated by Ioannis Mollas and validated through the Figure8 platform (APEN). ### Personal and Sensitive Information No personal and sensitive information included in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset This dataset will help on the evolution of the automated hate speech detection tools. Those tools have great impact on preventing social issues. ### Discussion of Biases This dataset tries to be unbiased towards its classes and labels. ### Other Known Limitations The dataset is relatively small and should be used combined with larger datasets. ## Additional Information ### Dataset Curators The dataset was initially created by [Intelligent Systems Lab](https://intelligence.csd.auth.gr). ### Licensing Information The licensing status of the datasets is [GNU GPLv3](https://choosealicense.com/licenses/gpl-3.0/). ### Citation Information ``` @misc{mollas2020ethos, title={ETHOS: an Online Hate Speech Detection Dataset}, author={Ioannis Mollas and Zoe Chrysopoulou and Stamatis Karlos and Grigorios Tsoumakas}, year={2020}, eprint={2006.08328}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@iamollas](https://github.com/iamollas) for adding this dataset.