eRisk 2026: Tasks on Symptoms Ranking, Contextual and Conversational Approaches for Early Mental Health Detection

Abstract

[Abstract]: Since its foundation in 2017, the eRisk CLEF Lab has pioneered research in early risk detection on the Internet, focusing on mental health challenges such as depression, anorexia, and pathological gambling. Over the years, participants have contributed to the development of detection models and exploited the datasets we constructed to advance this critical area. In 2026, which marks the tenth edition of the lab, we continue this trajectory with three tasks that emphasize conversational and contextual modeling as well as symptom-oriented retrieval. The first task, Conversational Depression Detection, introduces the challenge of identifying depression through interactions with fine-tuned Large Language Models (LLMs) personas. The second task, Contextualised Early Detection of Depression, focuses on user-level classification by analyzing full conversational contexts, with participants engaging iteratively in natural interactions. Finally, the third task, ADHD Symptom Sentence Ranking, expands our scope beyond depression by requiring systems to rank sentences according to their relevance to the symptoms defined in the Adult ADHD Self-Report Scale. This paper outlines the progress of the lab to date, introduces the three tasks of eRisk 2026, and discusses our innovative plans for promoting research on mental health challenges.