| Téléchargement | - Voir la version finale : Tackling social bias against the poor: a dataset and a taxonomy on aporophobia (PDF, 2.7 Mio)
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| Lien | https://aclanthology.org/2025.findings-naacl.388/ |
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| Auteur | Rechercher : Curto, Georgina; Rechercher : Kiritchenko, Svetlana1Identifiant ORCID : https://orcid.org/0000-0003-2550-3918; Rechercher : Siddiqui, Muhammad Hammad Fahim; Rechercher : Nejadgholi, Isar1Identifiant ORCID : https://orcid.org/0000-0001-6241-6114; Rechercher : Fraser, Kathleen C1Identifiant ORCID : https://orcid.org/0000-0002-0752-6705 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL 2025), April 29 - May 4, 2025, Albuquerque, New Mexico |
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| Résumé | Eradicating poverty is the first goal in the U.N. Sustainable Development Goals. However, aporophobia – the societal bias against people living in poverty – constitutes a major obstacle to designing, approving and implementing poverty-mitigation policies. This work presents an initial step towards operationalizing the concept of aporophobia to identify and track harmful beliefs and discriminative actions against poor people on social media. In close collaboration with non-profits and governmental organizations, we conduct data collection and exploration. Then we manually annotate a corpus of English tweets from five world regions for the presence of (1) direct expressions of aporophobia, and (2) statements referring to or criticizing aporophobic views or actions of others, to comprehensively characterize the social media discourse related to bias and discrimination against the poor. Based on the annotated data, we devise a taxonomy of categories of aporophobic attitudes and actions expressed through speech on social media. Finally, we train several classifiers and identify the main challenges for automatic detection of aporophobia in social networks. This work paves the way towards identifying, tracking, and mitigating aporophobic views on social media at scale. |
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| Date de publication | 2025-04-29 |
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| Maison d’édition | Association for Computational Linguistics |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | f230f5d8-5f4d-4125-9e47-c79083a88aa6 |
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| Enregistrement créé | 2025-05-06 |
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| Enregistrement modifié | 2025-05-06 |
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