EXIST 2022: Sexism detection (EN)

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
AIT_FHSTP_1	0.7705	0.7788	0.7693	0.7712	0.30
multiaztertest_1	0.7939	0.8018	0.7953	0.7981	0.37
AIT_FHSTP_3	0.7633	0.7690	0.7650	0.7692	0.28
I2C_1	0.8161	0.8253	0.8171	0.8192	0.44
avacaondata_1	0.8454	0.8454	0.8454	0.8500	0.52
I2C_2	0.7926	0.8011	0.7876	0.7885	0.36
avacaondata_2	0.5625	0.0551	0.1002	0.0558	-0.92
I2C_3	0.7900	0.7892	0.7896	0.7962	0.35
avacaondata_3	0.8454	0.8454	0.8454	0.8500	0.52
LPtower_1	0.7838	0.7870	0.7852	0.7904	0.34