EXIST-2025: Sexism categorization in videos (hard-hard)

Many aspects of a woman’s life can be the target of sexist attitudes, such as domestic and parental roles, professional opportunities, sexual image, and life expectations, among others. Automatically detecting which of these facets are most frequently targeted on social media will facilitate the development of policies to combat sexism. In this task, each sexist video must be classified into one or more of the following categories: IDEOLOGICAL AND INEQUALITY, STEREOTYPING AND DOMINANCE, OBJECTIFICATION, SEXUAL VIOLENCE, MISOGYNY AND NON-SEXUAL VIOLENCE. In this task, a soft-soft evaluation is considered, where the probability of each label predicted by the system is compared with the probability defined based on annotation disagreement in the gold standard.

Publication
Plaza, L. et al. (2025). Overview of EXIST 2025: Learning with Disagreement for Sexism Identification and Characterization in Tweets, Memes, and TikTok Videos. In: Carrillo-de-Albornoz, J., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2025. Lecture Notes in Computer Science, vol 16089. Springer, Cham.
Language
Spanish
English
NLP topic
Year
2025
Ranking metric
ICMSoft

Task results

If you have published a result better than those on the list, send a message to odesia-comunicacion@lsi.uned.es indicating the result and the DOI of the article, along with a copy of it if it is not published openly.