EXIST

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This lab focuses on the detection of sexist messages in social networks (SN), particularly in complex multimedia formats such as memes and short videos. Inequality and discrimination against women that remains embedded in society is increasingly being replicated online. In this edition, we extend the Learning with Disagreement (LwD) framework by incorporating sensor-based data from people exposed to potentially sexist content. This includes measurements such as heart rate variability, EEG, eye-tracking, and other sensor data.

Tasks

The EXIST Lab features three different tasks:

Sexism Identification
Binary classification where the systems must decide whether or not a given meme or video contain sexist expressions or behaviours.
Source Intention
Aims to categorize the message according to the intention of the author.
Sexism Categorization
Aims to categorize the meme in different types of sexism according to the categorization proposed by experts that considers the different facets of women that are undermined.

Organizers

  • Laura Plaza (Universidad Nacional de Educación a Distancia, Spain)
  • Jorge Carrillo-De-Albornoz (Universidad Nacional de Educacion a Distancia, Spain)
  • Iván Arcos (Universitat Politècnica de València, Spain)
  • María Aloy (Universitat Politècnica de València, Spain)
  • Paolo Rosso (Universitat Politècnica de València, Spain)
  • Damiano Spina (RMIT University, Australia)