Abstract
The paper describes the EXIST 2024 lab on Sexism identification in social networks, that is expected to take place at the CLEF 2024 conference and represents the fourth edition of the EXIST challenge. The lab comprises five tasks in two languages, English and Spanish, with the initial three tasks building upon those from EXIST 2023 (sexism identification in tweets, source intention detection in tweets, and sexism categorization in tweets). In this edition, two new tasks have been introduced: sexism detection in memes and sexism categorization in memes. Similar to the prior edition, this one will adopt the Learning With Disagreement paradigm. The dataset for the various tasks will provide all annotations from multiple annotators, enabling models to learn from a range of training data, which may sometimes present contradictory opinions or labels. This approach facilitates the model’s ability to handle and navigate diverse perspectives. Data bias will be handled both in the sampling and in the labeling processes: seed, topic, temporal and user bias will be taken into account when gathering data; in the annotation process, bias will be reduced by involving annotators from different social and demographic backgrounds.
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Acknowledgments
This work has been financed by the European Union (Next Generation EU funds) through the “Plan de Recuperación, Transformación y Resiliencia”, by the Ministry of Economic Affairs and Digital Transformation and by the UNED University. It has also been financed by the Spanish Ministry of Science and Innovation (project FairTransNLP (PID2021-124361OB-C31 and PID2021-124361OB-C32)) funded by MCIN/AEI/10.13039/501100011033 and by ERDF, EU A way of making Europe, and by the Australian Research Council (DE200100064 and CE200100005).
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Plaza, L. et al. (2024). EXIST 2024: sEXism Identification in Social neTworks and Memes. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14612. Springer, Cham. https://doi.org/10.1007/978-3-031-56069-9_68
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DOI: https://doi.org/10.1007/978-3-031-56069-9_68
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