EXIST 2022: Sexism detection

Binary classification problem, consisting in determmine whether a text or message is sexist or not. It includes any type of sexist expression or related phenomena, like descriptive or reported assertions where the sexist message is a report or a description of a sexist event. In particular, we consider two labels:

  • Sexist: the tweet or gab expresses sexist behaviours or discourses.
  • Non-Sexist: the tweet or gab does not express any sexist behaviour or discourse.
Publicación
Francisco Rodríguez-Sánchez, Jorge Carrillo-de-Albornoz, Laura Plaza, Adrián Mendieta-Aragón, Guillermo Marco-Remón, Maryna Makeienko, María Plaza, Julio Gonzalo, Damiano Spina, Paolo Rosso (2022) Overview of EXIST 2022: sEXism Identification in Social neTworks. Procesamiento del Lenguaje Natural, Revista nº 69, septiembre de 2022, pp. 229-240.
Competición
EXIST 2022: sEXism Identification in Social neTworks
Idioma
Inglés
URL Tarea
http://nlp.uned.es/exist2022/
NLP topic
detección de odio
Tarea abstracta
Clasificación
Dataset
EXIST-2022-ES
Año
2022
Enlace publicación
http://journal.sepln.org/sepl…
Métrica Ranking
Accuracy

Mejores resultados para la tarea

Sistema Precisión Recall F1 Accuracy ICM ICM Norm
avacaondata_1 0.8454 0.8454 0.8454 0.8500 0.5184 0.7650
avacaondata_3 0.8454 0.8454 0.8454 0.8500 0.5184 0.7650
SINAI-TL_1 0.8179 0.8252 0.8200 0.8231 0.4465 0.7282
SINAI-TL_3 0.8173 0.8224 0.8192 0.8231 0.4427 0.7262
I2C_1 0.8161 0.8253 0.8171 0.8192 0.4406 0.7252
AI-UPV_3 0.8140 0.8212 0.8161 0.8192 0.4347 0.7222
CIMATCOLMEX_3 0.8116 0.8175 0.8136 0.8173 0.4264 0.7179
CIMATCOLMEX_2 0.8060 0.8126 0.8081 0.8115 0.4102 0.7097
multiaztertest_2 0.8024 0.7997 0.8009 0.8077 0.3858 0.6972
ELiRF-VRAIN_3 0.7956 0.7975 0.7965 0.8019 0.3734 0.6909