It is a binary classification task that consists of deciding whether a given tweet contains sexist expressions or behaviors (i.e., it is sexist itself, describes a sexist situation, or criticizes sexist behavior). In this task, a hard-hard evaluation is considered, where system-predicted labels are compared with the gold standard labels.
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
URL Task
NLP topic
Dataset
Year
2025
Publication link
Ranking metric
ICM
Task results
| System | ICM Sort ascending |
|---|---|
| Mario_1 | 0.6774 |
| CIMAT-GTO_2 | 0.6297 |
| CIMAT-GTO_3 | 0.6256 |
| warwick_1 | 0.6249 |
| CIMAT-CS-NLP_3 | 0.6127 |
