This work was supported by project PLEC2021-007662 (MCIN/AEI/10.13039/ 501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next Generation EU). The first and second authors thank the financial support supplied by the Xunta de Galicia-Consellería de Cultura, Educación, Formación Profesional e Universidade (GPC ED431B 2022/33) and the European Regional Development Fund and project PID2022- 137061OB-C21 (MCIN/AEI/ 10.13039/501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; supported by “ERDF A way of making Europe”, by the “European Union”). The CITIC, as a center accredited for excellence within the Galician University System and a member of the CIGUS Network, receives subsidies from the Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia. Additionally, CITIC is co-financed by the EU through the FEDER Galicia 2021-27 operational program (Ref. ED431G 2023/01). The third author thanks the financial support supplied by the Xunta de Galicia-Consellería de Cultura, Educación, Formación Profesional e Universidade (accreditation 2019-2022 ED431G-2019/04, ED431C 2022/19) and the European Regional Development Fund, which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System. David E. Losada also thanks the financial support obtained from project SUBV23/00002 (Ministerio de Consumo, Subdirección General de Regulación del Juego) and project PID2022-137061OB-C22 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; supported by the European Regional Development Fund).

[Abstract]: This paper presents eRisk 2024, the eighth edition of the CLEF conference’s lab dedicated to early risk detection. Since its inception, the lab has been at the forefront of developing and refining evaluation methodologies, effectiveness metrics, and processes for early risk detection across various domains. These early alerting models hold significant value, particularly in sectors focused on health and safety, where timely intervention can be crucial. eRisk 2024 featured three main tasks designed to push the boundaries of early risk detection techniques. The first task challenged participants to rank sentences based on their relevance to standardized depression symptoms, a crucial step in identifying early signs of depression from textual data. The second task focused on the early detection of anorexia indicators, aiming to develop models that can recognize the subtle cues of this eating disorder before it becomes critical. The third task was centered around estimating responses to an eating disorders questionnaire by analyzing users’ social media posts. Participants had to leverage the rich, real-world textual data available on social media to gauge potential mental health risks. Through these tasks, eRisk 2024 continues to advance the field of early risk detection, fostering innovations that could lead to significant improvements in public health interventions.

Included in: Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024) Grenoble, France, 9-12 September, 2024.

Xunta de Galicia; ED431B 2022/33

Xunta de Galicia; ED431G 2023/01

Xunta de Galicia; ED431C 2022/19

Xunta de Galicia; ED431G 2019/04