EXIST-2025: Source Intention in tweets (hard-hard)

This task aims to classify sexist tweets according to the author’s intention, allowing for a better understanding of the role social media plays in the expression and dissemination of sexist messages. In this task, a ternary classification is proposed: direct, reported, and judgemental. 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
Dataset
Year
2025
Ranking metric
ICMSoft

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.