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Artículo

Assessing the Impact of Contextual Information in Hate Speech Detection

Pérez, Juan ManuelIcon ; Luque, Franco MartínIcon ; Zayat, Demian; Kondratzky, Martin; Moro, Agustín; Serrati, Pablo SantiagoIcon ; Zajac, JoaquinIcon ; Miguel, Paula GabrielaIcon ; Debandi, NataliaIcon ; Gravano, AgustinIcon ; Cotik, Viviana EricaIcon
Fecha de publicación: 03/2023
Editorial: Institute of Electrical and Electronics Engineers
Revista: IEEE Access
ISSN: 2169-3536
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Ciencias de la Computación

Resumen

Social networks and other digital media deal with huge amounts of user-generated contents where hate speech has become a problematic more and more relevant. A great effort has been made to develop automatic tools for its analysis and moderation, at least in its most threatening forms, such as in violent acts against people and groups protected by law. One limitation of current approaches to automatic hate speech detection is the lack of context. The spotlight on isolated messages, without considering any type of conversational context or even the topic being discussed, severely restricts the available information to determine whether a post on a social network should be tagged as hateful or not. In this work, we assess the impact of adding contextual information to the hate speech detection task. We specifically study a subdomain of Twitter data consisting of replies to digital newspapers posts, which provides a natural environment for contextualized hate speech detection. We built a new corpus in Spanish (Rioplatense variant) focused on hate speech associated to the COVID-19 pandemic, annotated using guidelines carefully designed by our interdisciplinary team. Our classification experiments using state-of-the-art transformer-based machine learning techniques show evidence that adding contextual information improves the performance of hate speech detection for two proposed tasks: binary and multi-label prediction, increasing their Macro F1 by 4.2 and 5.5 points, respectively. These results highlight the importance of using contextual information in hate speech detection. Our code, models, and corpus has been made available for further research.
Palabras clave: CONTEXTUAL INFORMATION , COVID-19 HATE SPEECH , HATE SPEECH DETECTION , NLP , SPANISH CORPUS , TEXT CLASSIFICATION
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info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/219319
URL: https://ieeexplore.ieee.org/document/10076443
DOI: http://dx.doi.org/10.1109/ACCESS.2023.3258973
Colecciones
Articulos(ICC)
Articulos de INSTITUTO DE INVESTIGACION EN CIENCIAS DE LA COMPUTACION
Articulos(OCA CIUDAD UNIVERSITARIA)
Articulos de OFICINA DE COORDINACION ADMINISTRATIVA CIUDAD UNIVERSITARIA
Articulos(SEDE CENTRAL)
Articulos de SEDE CENTRAL
Citación
Pérez, Juan Manuel; Luque, Franco Martín; Zayat, Demian; Kondratzky, Martin; Moro, Agustín; et al.; Assessing the Impact of Contextual Information in Hate Speech Detection; Institute of Electrical and Electronics Engineers; IEEE Access; 11; 3-2023; 30575-30590
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