Pérez, AnxoPiot, PalomaParapar, JavierBarreiro, Álvaro2024-10-302024-10-302023-03Pérez, A., Piot-Pérez-Abadín, P., Parapar, J., Barreiro, Á. (2023). PsyProf: A Platform for Assisted Screening of Depression in Social Media. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13982. Springer, Cham. https://doi.org/10.1007/978-3-031-28241-6_3097830312824090302-9743http://hdl.handle.net/2183/39876Presented at: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, 2 April 2023 through 6 April 2023.This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-28241-6_30[Abstract]: Depression is one of the most prevalent mental disorders. For its effective treatment, patients need a quick and accurate diagnosis. Mental health professionals use self-report questionnaires to serve that purpose. These standardized questionnaires consider different depression symptoms in their evaluations. However, mental health stigmas heavily influence patients when filling out a questionnaire. In contrast, many people feel more at ease discussing their mental health issues on social media. This demo paper presents a platform for assisted examination and tracking of symptoms of depression for social media users. In order to bring a broader context, we have complemented our tool with user profiling. We show a platform that helps professionals with data labelling, relying on depression estimators and profiling models.eng© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AGDepression estimationAuthor profilingBDI-IIconference outputopen access10.1007/978-3-031-28241-6_30