--- 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 - name: interval_end_at dtype: int64 - name: interval_exact_highlight dtype: string - name: interval_start_at dtype: int64 - name: lang dtype: string - name: text_id dtype: int64 splits: - name: all num_bytes: 1150122 num_examples: 6027 - name: all_tags num_bytes: 1038949 num_examples: 5536 - name: gold_tags num_bytes: 221311 num_examples: 1131 - name: spa_all_tags num_bytes: 584111 num_examples: 3256 - name: ita_all_tags num_bytes: 454838 num_examples: 2280 - name: spa_all num_bytes: 610683 num_examples: 3407 - name: ita_all num_bytes: 539439 num_examples: 2620 - name: tags num_bytes: 817638 num_examples: 4405 - name: spa_tags num_bytes: 362800 num_examples: 2125 - name: ita_tags num_bytes: 454838 num_examples: 2280 - name: spa_gold_tags num_bytes: 102406 num_examples: 550 - name: ita_gold_tags num_bytes: 118905 num_examples: 581 - name: comments num_bytes: 111173 num_examples: 491 - name: spa_comments num_bytes: 26572 num_examples: 151 - name: ita_comments num_bytes: 84601 num_examples: 340 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-* - split: spa_all_tags path: data/spa_all_tags-* - split: ita_all_tags path: data/ita_all_tags-* - split: spa_all path: data/spa_all-* - split: ita_all path: data/ita_all-* - split: tags path: data/tags-* - split: spa_tags path: data/spa_tags-* - split: ita_tags path: data/ita_tags-* - split: spa_gold_tags 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.