EXIST-2024: Sexism categorization in tweets (hard-hard)

Many aspects of a woman's life can be the target of sexist attitudes, such as domestic and parental roles, career opportunities, sexual image, and life expectations, among others. Automatically detecting which of these facets are most frequently attacked on social media will help develop policies to combat sexism. In this task, each sexist tweet 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. This task includes a hard-hard evaluation, where the labels predicted by the system are compared to the gold standard labels.

Publication
Plaza, L. et al. (2024).EXIST 2024: sEXism Identification in Social neTworks and Memes. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2024. Lecture Notes in Computer Science, volume 14612
Language
Spanish
English
NLP topic
Dataset
Year
2024
Ranking metric
ICM

Task results

System ICM Sort ascending
ABCD Team_1 0.3713
ABCD Team_3 0.3540
NYCU-NLP_3 0.3069
NYCU-NLP_1 0.2364
NYCU-NLP_2 0.1725

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.