Description

According to a recent report by the World Health Organization, there is 1 in every 8 people in the world suffering from a mental disorder. The COVID-19 pandemic has raised the prevalence of anxiety and depression to more than 26% in just one year. Suicide is the fourth leading cause of death among 15-29 year-olds. The organisation considers that early identification is a key effective intervention to prevent these problems.

Consequently, there is a growing interest in detecting and identifying mental disorders in social media streams. This answers a demand from society due to the high increase in these problems among the population, in several kinds of mental risks: eating disorders, dysthymia, anxiety, depression, suicidal ideation, and others. Actually, relevant evaluation campaigns like the Cross-Lingual Evaluation Forum (CLEF)  have hosted during the last years the Early-Risk Identification task (eRisk). Unfortunately, these campaigns have focused mainly on English, leaving aside other languages, like Spanish. 

This proposal describes the second edition of a novel task on early risk identification of mental disorders in Spanish comments from social media sources. The first edition took place last year in the IberLEF evaluation forum as part of the SEPLN 2023. The task was resolved as an online problem, that is, the participants had to detect a potential risk as early as possible in a continuous stream of data. Therefore, the performance not only depended on the accuracy of the systems but also on how fast the problem is detected. These dynamics are reflected in the design of the tasks and the metrics used to evaluate participants. For this second edition, we propose three novel tasks, the first subtask is about the detection of the disorder, the second subtask consists of detecting the context that may be associated with the disorder, and the third subtask is about suicidal ideation detection. 

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