Many facets of a woman’s life may be the focus of sexist attitudes including domestic and parenting roles, career opportunities, sexual image, and life expectations, to name a few. Automatically detecting which of these facets of women are being more frequently attacked in social networks will facilitate the development of policies to fight against sexism. In this task, each sexist tweet must be categorized in one or more of the following categories: IDEOLOGICAL AND INEQUALITY, STEREOTYPING AND DOMINANCE, OBJECTIFICATION, SEXUAL VIOLENCE, MISOGYNY AND NON-SEXUAL VIOLENCE.
This task includes a hard-hard evaluation in which labels predicted by the system are compared with the gold standard labels.
Publication
Plaza, L. et al. (2023). Overview of EXIST 2023 – Learning with Disagreement for Sexism Identification and Characterization. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2023. Lecture Notes in Computer Science, vol 14163. Springer, Cham. https://doi.org/10.1007/978-3-031-42448-9_23
Competition
Language
Spanish
URL Task
NLP topic
Abstract task
Dataset
Year
2023
Publication link
Ranking metric
ICM
