This task aims at aims to categorize sexist tweets according to the intention of the author, which provides insights in the role played by social networks on the emission and dissemination of sexist messages. In this task, we propose a ternary classification task: direct, reported, judgemental.
This task includes a soft-soft evaluation in which the probability of each label predicted by the system is compared with the probability defined from the disagreement in the gold standard annotation.
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
Task results
| System | ICM Sort ascending |
|---|---|
| Mario_2 | 0.5711 |
| roh-neil_1 | 0.4783 |
| roh-neil_2 | 0.4783 |
| UniBo_2 | 0.3485 |
| AIT_FHSTP_1 | 0.2948 |
| UniBo_1 | 0.2941 |
| UMUTeam_1 | 0.2571 |
| JPM_UNED_2 | 0.2351 |
| JPM_UNED_3 | 0.2231 |
| JPM_UNED_1 | 0.1986 |
