Toxicity in Spanish News Comments and its Relationship with Constructiveness

• Autores: Pilar López Úbeda, Flor Miriam Plaza del Arco, Manuel Carlos Díaz Galiano, María Teresa Martín Valdivia
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 73, 2024, págs. 43-53
• Idioma: inglés
• Títulos paralelos:
  • Toxicidad en Comentarios de Noticias en Español y su Relación con la Constructividad
• Enlaces
  • Texto completo
• Resumen
  • español

    Los comentarios en plataformas de noticias digitales constituyen una fuente esencial de información y opinión. Sin embargo, frecuentemente se transforman en focos de discurso toxico e incivilidad. La detección de la toxicidad en dichos comentarios es fundamental para comprender y atenuar este problema. Este artículo introduce un corpus de comentarios de noticias en español, etiquetados por su toxicidad (NECOS-TOX), y realiza una serie de experimentos empleando diversos algoritmos de aprendizaje automático, incluyendo modelos de lenguaje basados en la arquitectura de transformers. Los resultados obtenidos demuestran que los modelos de lenguaje específicos para el español, como BETO, poseen la capacidad de identificar la toxicidad en los comentarios de noticias en español. Adicionalmente, se exploró la relación existente entre la toxicidad y la constructividad en estos comentarios, concluyendo que no se aprecia una correlación evidente entre ambos factores. Estos hallazgos aportan luz sobre las complejidades inherentes al discurso en línea y subrayan la necesidad imperante de realizar investigaciones adicionales para comprender de manera más profunda la relación entre la toxicidad y la constructividad en los comentarios de noticias en español.

  • English

    Online news comments are a critical source of information and opinion, but they often become a breeding ground for toxic discourse and incivility. Detecting toxicity in these comments is essential to understand and mitigate this problem. This paper presents a corpus of Spanish news comments labeled with toxicity (NECOS-TOX) and conducts a series of experiments using several machine learning algorithms, including different language models based on transformers. Our findings show that Spanish language models, such as BETO, are capable of detecting toxicity in Spanish news comments. Additionally, we investigated the relationship between toxicity and constructiveness in these comments and found that there is no clear correlation between the two factors. These results provide insights into the complexities of online discourse and highlight the need for further research to better understand the relationship between toxicity and constructiveness in Spanish news comments.

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