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****We apologize for the multiple copies of this e-mail**** ---------------------------------------------------------------------------------------------------- Call for Participation ---------------------------------------------------------------------------------------------------- First Call for Participation: EXIST 2026: Multimodal sexism identification with sensor data Website: http://nlp.uned.es/exist2026/ EXIST is a series of scientific events and shared tasks on sexism identification in social networks. EXIST aims to foster the automatic detection of sexism in a broad sense, from explicit misogyny to other subtle expressions that involve implicit sexist behaviours (EXIST 2021, EXIST 2022, EXIST 2023, EXIST 2024, EXIST 2025). The sixth edition of the EXIST shared task will be held as a Lab in CLEF 2026, on September 21-24, 2026, at Friedrich-Schiller-Universität Jena, Germany . In EXIST 2026, we take a significant step forward by integrating the principles of Human-Centered AI (HCAI) into the development of automatic tools for detecting sexism online. Recognizing that no single interpretation can fully capture the diversity of human perception, we go beyond traditional annotation paradigms by combining Learning With Disagreement (LeWiDi) with sensor-based data (EEG, heart rate, and eye-tracking signals) collected from subjects exposed to potentially sexist content, with the aim of capturing unconscious responses to sexism. This dual approach represents a breakthrough in dataset creation for sensitive and value-laden tasks: for the first time, datasets will include not only divergent judgments from annotators, but also the embodied traces of how this content affect. This richer, multidimensional annotation process will enable the development of more inclusive, equitable, and socially aware AI systems for detecting sexism in complex multimedia formats like memes and short videos, where ambiguity and affect play a critical role. Similar to the approaches in the 2023, 2024 and 2025 edition, this edition will also embrace the Learning With Disagreement (LeWiDi) paradigm for both the development of the dataset and the evaluation of the systems. The LeWiDi paradigm doesn’t rely on a single “correct” label for each example. Instead, the model is trained to handle and learn from conflicting or diverse annotations. This enables the system to consider various annotators’ perspectives, biases, or interpretations, resulting in a fairer learning process. Building upon the EXIST 2025 dataset, this edition focuses exclusively on multimedia formats, comprising six experimental subtasks applied to images (memes) and videos (TikToks). Participants are challenged to address three main objectives: sexism identification (x.1), source intention detection (x.2), and sexism categorization (x.3) (numbering of subtask is consistent with EXIST 2025). Participants will be asked to classify memes and videos (in English and Spanish) according to the following tasks: TASK 2: Sexism detection in Memes: TASK 2.1 - Sexism Identification in Memes: this is a binary classification subtask consisting on determining wheter a meme describes a sexist situation or criticizes a sexist behaviour, and classifying it into two categories: YES and NO. Task 2.2: Source Intention in Memes: this subtask aims to categorize the meme according to the intention of the author. Due to the characteristics of the memes systems should only classify memes into the DIRECT or JUDGEMENTAL categories. Task 2.3: Sexism Categorization in Memes: once a message has been classified as sexist, the third subtask aims to categorize the message in different types of sexism (according to a categorization proposed by experts and that takes into account the different facets of women that are undermined). In particular, each sexist tweet must be categorized in one or more of the following categories: (i) IDEOLOGICAL AND INEQUALITY, (ii) STEREOTYPING AND DOMINANCE, (iii) OBJECTIFICATION, (iv) SEXUAL VIOLENCE and (v) MISOGYNY AND NON-SEXUAL VIOLENCE. TASK 3: Sexism detection in Videos: SUBTASK 3.1 - Sexism Identification in Videos: this is a binary classification task as in Subtasks 2.1. SUBTASK 3.2: Source Intention in Videos: this subtask replicates subtask 2.2 for memes, but it takes as source videos. SUBTASK 3.3: This subtask aims to classify sexist videos according to the categorization provided for Subtask 2.3: (i) IDEOLOGICAL AND INEQUALITY, (ii) STEREOTYPING AND DOMINANCE, (iii) OBJECTIFICATION, (iv) SEXUAL VIOLENCE and (v) MISOGYNY AND NON-SEXUAL VIOLENCE. Although we recommend to participate in all subtasks and in both languages, participants are allowed to participate just in one of them (e.g. subtask 2.1) and in one language (e.g. English). During the training phase, the task organizers will provide the participants with the manually-annotated EXIST 2026 dataset. For the evaluation of the systems, the unlabeled test data will be released. We encourage participation from both academic institutions and industrial organizations. We invite participants to register for the lab at CLEF 2026 Labs Registration site (https://clef-labs-registration.dipintra.it/). You will receive information about how to join the Discord Group for the EXIST 2026 shared task. Important Dates: * 17 November 2025: Registration opens. * 26 February 2026: Training set available. * 9 April 2026: Test set available. * 23 April 2026: Registration closes. * 7 May 2026: Runs submission due to organizers. * 28 May 2026: Results notification to participants. * 4 June 2026: Submission of Working Notes by participants. * 30 June 2026: Notification of acceptance (peer reviews). * 6 July 2026: Camera-ready participant papers due to organizers. * 21-24 September 2026: EXIST 2026 at CLEF Conference. ** Note: All deadlines are 11:59PM UTC-12:00 ("anywhere on Earth") ** Organizers: Laura Plaza, Universidad Nacional de Educación a Distancia (UNED) Jorge Carrillo-de-Albornoz, Universidad Nacional de Educación a Distancia (UNED) Iván Arcos, Universitat Politècnica de València (UPV) Maria Aloy Mayo, Universitat Politècnica de València (UPV) Paolo Rosso, Universitat Politècnica de València (UPV) Damiano Spina, Royal Melbourne Institute of Technology (RMIT) Contact: Contact the organizers by writing to: [email protected] Website: http://nlp.uned.es/exist2026/ AVISO LEGAL. 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