EXIST: sEXism Identification in Social neTworks

EXIST aims to capture and categorize sexism, from explicit misogyny to other subtle behaviors, in social networks. In 2024 the EXIST campaign included multimedia content in the format of memes, stepping forward research on more robust techniques to identify sexism in social networks. Following this line, this year we will focus TikTok videos in the challenge, thus including in the dataset the three more important sources of sexism spreading: text, images and videos. Sexism on TikTok is also a growing concern as the platform’s algorithm often amplifies content that perpetuates gender stereotypes and internalized misogyny. Consequently, it is essential to develop automated multimodal tools capable of detecting sexism in text, images, and videos, to raise alarms or automatically remove such content from social networks. This lab will contribute to the creation of applications that identify sexist content in social media across all three formats.

Tasks:

  • Task 1 - Sexism Identification and Characterization in Tweets
    • Subtask 1.1 - Sexism Identification in Tweets

      The first subtask is a binary classification. The systems have to decide whether or not a given tweet contains or describes sexist expressions or behaviors (i.e., it is sexist itself, describes a sexist situation or criticizes a sexist behavior).

    • Subtask 1.2 - Source Intention in Tweets

      This subtask aims to categorize the sexist messages according to the intention of the author in one of the following categories: (i) direct sexist message, (ii) reported sexist message and (iii) judgemental message.

    • Subtask 1.3 - Sexism Categorization in Tweets

      The third subtask is a multiclass task that aims to categorize the sexist messages according to the type or types of sexism they contain (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.

  • Task 2 - Sexism Identification and Characterization in Memes
    • Subtask 2.1 - Sexism Identification in Memes

      Similar to subtask 1.1, subtask 2.1 is a binary classification task where participants must determine when a meme contains or describes sexist expressions or behaviors (i.e., it is sexist itself, describes a sexist situation or criticizes a sexist behavior).

    • Subtask 2.2 - Source Intention in Tweets

      This subtask aims to categorize the sexist messages according to the intention of the author in one of the following categories: (i) direct sexist message, (ii) judgemental message.

    • Subtask 2.3 - Sexism Categorization in Memes

      Finally, this subtask addresses the problem of categorizing a sexist meme according to the type of sexism that it encloses: (i) ideological and inequality, (ii) stereotyping and dominance, (iii) objectification, (iv) sexual violence and (v) misogyny and non-sexual violence.

  • Task 3 - Sexism Identification and Characterization in Tiktok Videos
    • Subtask 3.1 - Sexism Identification in Videos

      Similar to sub task 1.1 and 2.1, this subtask is a binary classification task where participants must determine when a meme contains or describes sexist expressions or behaviors (i.e., it is sexist itself, describes a sexist situation or criticizes a sexist behavior).

    • Subtask 3.2 - Source Intention in Videos

      This subtask aims to categorize the sexist messages according to the intention of the author in one of the following categories: (i) direct sexist message, (ii) judgemental message.

    • Subtask 3.3 - Sexism Categorization in Videos

      Finally, this subtask addresses the problem of categorizing a sexist meme according to the type of sexism that it encloses: (i) ideological and inequality, (ii) stereotyping and dominance, (iii) objectification, (iv) sexual violence and (v) misogyny and non-sexual violence.

Organizers

  • Laura Plaza, UNED, Spain
  • Jorge Carrillo-de-Albornoz, UNED, Spain
  • Paolo Rosso, UPV, Spain
  • Iván Arcos, UPV, Spain
  • Julio Gonzalo, UNED, Spain
  • Enrique Amigó, UNED, Spain
  • Damiano Spina, RMIT, Australia
  • Roser Morante, UNED, Spain

Contact

  • jcalbornoz@lsi.uned.es