Datasets:

oeg
/

virc

	---
	language:
	- es
	- it
	license: mit
	size_categories:
	- n<1K
	task_categories:
	- zero-shot-classification
	- question-answering
	- text-classification
	pretty_name: VIRC
	dataset_info:
	features:
	- name: annotation
	dtype: string
	- name: annotation_type
	dtype: string
	- name: annotator_id
	dtype: string
	- name: headline
	dtype: string
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	- name: ita_comments
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	download_size: 1213163
	dataset_size: 6678386
	configs:
	- config_name: default
	data_files:
	- split: all
	path: data/all-*
	- split: all_tags
	path: data/all_tags-*
	- split: gold_tags
	path: data/gold_tags-*
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	path: data/spa_all_tags-*
	- split: ita_all_tags
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	path: data/ita_tags-*
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	path: data/spa_gold_tags-*
	- split: ita_gold_tags
	path: data/ita_gold_tags-*
	- split: comments
	path: data/comments-*
	- split: spa_comments
	path: data/spa_comments-*
	- split: ita_comments
	path: data/ita_comments-*
	---

	# Vulnerable Identities Recognition Corpus (VIRC) for Hate Speech Analysis
	Welcome to the Vulnerable Identities Recognition Corpus (VIRC), a dataset created to enhance hate speech analysis in Italian and Spanish news headlines. VIRC provides annotated headlines aimed at identifying vulnerable identities, dangerous discourse, derogatory mentions, and entities. This corpus contributes to developing more sophisticated hate speech detection tools and policies for creating a safer online environment. The work has been published at the Tenth Italian Conference on Computational Linguistics (CLiC-it 2024) with the name of [Vulnerable Identities Recognition Corpus (VIRC) for Hate Speech Analysis](https://ceur-ws.org/Vol-3878/49_main_long.pdf). The code for the experiments performed in the paper are available in the github repo [https://github.com/oeg-upm/virc](https://github.com/oeg-upm/virc).

	## Overview

	VIRC is designed to support the study of hate speech in headlines from two languages: Italian and Spanish. It includes 880 headlines (532 Italian and 348 Spanish), collected and annotated with the following labels:
	- Named Entities: Identifies persons, locations, organizations, groups, etc. mentioned in the headline.
	- Vulnerable Identity Mentions: Labels groups such as women, LGBTQI, ethnic minorities, and migrants targeted by hate speech.
	- Derogatory Mentions: Marks phrases that are derogatory towards vulnerable groups.
	- Dangerous Speech: Highlights parts of the text perceived as potentially inciting hate or perpetuating harmful stereotypes.

	## Data
	- `Spanish`: The Spanish datasets are split into two sets, agreement and disagreement. Agreement set contains the data annotated by the two original annotators, while the disagreement set contains the news where no agreement was reached and a third annotator was needed.
	- `Italian`: The Italian data consists of only one set annotated by two annotators.

	## Annotation
	The `VIRC_Guidelines.pdf` contains the annotation guidelines provided to annotators. This can be seen sintetized in the paper. The dataset is provided with several splits depending of which elements are included:
	- Annotations (Spanish): Annotators annotations for Spanish.
	- Annotations (Italian): Annotators annotations for Italian.
	- Gold (Spanish): Gold standard annotations for Spanish.
	- Gold (Italian): Gold standard annotations for Italian.
	- Comments (Spanish): Annotators comments for Spanish.
	- Comments (Italian): Annotators comments for Italian.

	The different splits include:
	\| Configuration \| Annotations (Spanish) \| Annotations (Italian) \| Gold (Spanish) \| Gold (Italian) \| Comments (Spanish) \| Comments (Italian) \| Number of Rows \|
	\|----------------------\|---------------------------\|---------------------------\|--------------------\|---------------------\|------------------------\|------------------------\|-------------------\|
	\| `all` \| ✅ \| ✅ \| ✅ \| ✅ \| ✅ \| ✅ \|6027 \|
	\| `all_tags` \| ✅ \| ✅ \| ✅ \| ✅ \| ❌ \| ❌ \|5536 \|
	\| `spa_all` \| ✅ \| ❌ \| ✅ \| ❌ \| ✅ \| ❌ \|3407 \|
	\| `ita_all` \| ❌ \| ✅ \| ❌ \| ✅ \| ❌ \| ✅ \|2620 \|
	\| `spa_all_tags` \| ✅ \| ❌ \| ✅ \| ❌ \| ❌ \| ❌ \|3256 \|
	\| `ita_all_tags` \| ❌ \| ✅ \| ❌ \| ✅ \| ❌ \| ❌ \|2280 \|
	\| `gold_tags` \| ❌ \| ❌ \| ✅ \| ✅ \| ❌ \| ❌ \|1131 \|
	\| `spa_gold_tags` \| ❌ \| ❌ \| ✅ \| ❌ \| ❌ \| ❌ \|550 \|
	\| `ita_gold_tags` \| ❌ \| ❌ \| ❌ \| ✅ \| ❌ \| ❌ \|581 \|
	\| `tags` \| ✅ \| ✅ \| ❌ \| ❌ \| ❌ \| ❌ \|4405 \|
	\| `spa_tags` \| ✅ \| ❌ \| ❌ \| ❌ \| ❌ \| ❌ \|2125 \|
	\| `ita_tags` \| ❌ \| ✅ \| ❌ \| ❌ \| ❌ \| ❌ \|2280 \|
	\| `comments` \| ❌ \| ❌ \| ❌ \| ❌ \| ✅ \| ✅ \|491 \|
	\| `spa_comments` \| ❌ \| ❌ \| ❌ \| ❌ \| ✅ \| ❌ \|151 \|
	\| `ita_comments` \| ❌ \| ❌ \| ❌ \| ❌ \| ❌ \| ✅ \|340 \|


	## Usage and Information
	The dataset can be loaded with:
	``` python
	from datasets import load_dataset
	dataset = load_dataset("Ibaii99/virc")
	```



	## BibTeX Entry and Citation Info
	``` bibtex
	@inproceedings{IbaiArianna2024,
	author = {Ibai Guill{\'{e}}n{-}Pacho and
	Arianna Longo and
	Marco Antonio Stranisci and
	Viviana Patti and
	Carlos Badenes{-}Olmedo},
	editor = {Felice Dell'Orletta and
	Alessandro Lenci and
	Simonetta Montemagni and
	Rachele Sprugnoli},
	title = {The Vulnerable Identities Recognition Corpus {(VIRC)} for Hate Speech
	Analysis},
	booktitle = {Proceedings of the Tenth Italian Conference on Computational Linguistics
	(CLiC-it 2024), Pisa, Italy, December 4-6, 2024},
	series = {{CEUR} Workshop Proceedings},
	volume = {3878},
	publisher = {CEUR-WS.org},
	year = {2024},
	url = {https://ceur-ws.org/Vol-3878/49_main_long.pdf},
	}
	```

	## Acknowledgements
	This work is supported by the Predoctoral Grant (PIPF-2022/COM-25947) of the Consejería de Educación, Ciencia y Universidades de la Comunidad de Madrid, Spain. Arianna Longo's work has been supported by aequa-tech.
	The authors gratefully acknowledge the Universidad Politécnica de Madrid (www.upm.es) for providing computing resources on the IPTC-AI innovation Space AI Supercomputing Cluster.

	## License
	This work is licensed under the MIT License. For more details, see the LICENSE file.