Sexism classification

This is a multi-class classification tasks. The systems have to decide whether or not a given tweet contains sexist expressions or behaviours (i.e., it is sexist itself, describes a sexist situation or criticizes a sexist behaviour) and, if so, to categorize the message according to the type of sexism (according to the categorization proposed by experts and that takes into account the different facets of women that are undermined): (i) ideological and inequality, (ii) stereotyping and dominance, (iii) objectification, (iv) sexual violence, and (v) misogyny and non-sexual violence.

Publication
Francisco Rodríguez-Sánchez, Jorge Carrillo-de-Albornoz, Laura Plaza, Julio Gonzalo, Paolo Rosso, Miriam Comet, Trinidad Donoso. Overview of EXIST 2021: sEXism Identification in Social neTworks.. Procesamiento del Lenguaje Natural, Vol 67, septiembre 2021.
Language
Spanish
English
NLP topic
Abstract task
Dataset
Year
2021
Ranking metric
F1

Task results

System Accuracy MacroPrecision MacroRecall MacroF1 Sort ascending
AI_UPV_1 0.6577 0.5815 0.5774 0.5787
LHZ_1 0.6509 0.5772 0.5649 0.5706
SINAI_TL_1 0.6527 0.5848 0.5527 0.5667
QMUL-SDS_1 0.6426 0.5626 0.5573 0.5594
AIT_FHSTP_2 0.6445 0.5689 0.5531 0.5589

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.