Datasets:
ethos

	---
	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.