The many facets of hateful content detection: from perspectivism to Bias
- Autores: Giulia Rizzi
- Directores de la Tesis: Paolo Rosso (dir. tes.), Elisabetta Fersini (dir. tes.)
- Lectura: En la Universitat Politècnica de València ( España ) en 2025
- Idioma: inglés
- Tribunal Calificador de la Tesis: Craig Hutton Macdonald (presid.), Alberto Barrón Cedeño (secret.), Giacomo Boracchi (voc.)
- Programa de doctorado: Programa de Doctorado en Informática por la Universitat Politècnica de València
- Materias:
- Enlaces
- Tesis en acceso abierto en: RiuNet
- Resumen
Hate Speech detection is a critical area of research aimed at identifying harmful and offensive content.
However, the task is hindered by challenges related to perspectivism and bias, which compromise the efficacy and fairness of detection systems, especially in a real-case scenario.
Perspectivism frequently emerges due to the subjective interpretation of what constitutes hate speech, which is influenced by cultural, linguistic, and contextual factors.
On the other hand, Bias is frequently associated with a skewed distribution of specific elements in the datasets that will be used for training hate speech detection models.
The thesis, focusing both on unimodal (text) and multimodal (memes) user-generated content, initially deals with perspectivism to model disagreement at different granularities, i.e., at constituent and instance levels. Subsequently, bias identification and mitigation are tackled by novel metrics and debiasing strategies.
By addressing these challenges, this work aims to improve the reliability and fairness of hate speech detection systems, paving the way for more equitable content moderation strategies, able to capture multiple perspectives and interpretations.
