# Early Detection of Depression and Eating Disorders in Spanish: UNSL at MentalRiskES 2023

arXiv: https://arxiv.org/abs/2310.20003 · HTML: https://mlsanity.com/paper/2310.20003/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [Spanish Pre-trained BERT Model and Evaluation Data](https://mlsanity.com/paper/2308.02976/) (2023)
- [A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams](https://mlsanity.com/paper/1905.08772/) (2019)

## Built on by (most cited first, 5 of 5)

- [A Time-Aware Approach to Early Detection of Anorexia: UNSL at eRisk 2024](https://mlsanity.com/paper/2410.17963/) (2024)
- [Hacia la interpretabilidad de la detecci'on anticipada de riesgos de depresi'on utilizando grandes modelos de lenguaje](https://mlsanity.com/paper/2503.20939/) (2025)
- [Temporal fine-tuning for early risk detection](https://mlsanity.com/paper/2505.11280/) (2025)
- [Tackling a Challenging Corpus for Early Detection of Gambling Disorder: UNSL at MentalRiskES 2025](https://mlsanity.com/paper/2511.23325/) (2025)
- [Improving Mental Health Screening and Early Risk Detection in Spanish](https://mlsanity.com/paper/2607.28476/) (2026)

## Abstract (arXiv)

MentalRiskES is a novel challenge that proposes to solve problems related to early risk detection for the Spanish language. The objective is to detect, as soon as possible, Telegram users who show signs of mental disorders considering different tasks. Task 1 involved the users' detection of eating disorders, Task 2 focused on depression detection, and Task 3 aimed at detecting an unknown disorder. These tasks were divided into subtasks, each one defining a resolution approach. Our research group participated in subtask A for Tasks 1 and 2: a binary classification problem that evaluated whether the users were positive or negative. To solve these tasks, we proposed models based on Transformers followed by a decision policy according to criteria defined by an early detection framework. One of the models presented an extended vocabulary with important words for each task to be solved. In addition, we applied a decision policy based on the history of predictions that the model performs during user evaluation. For Tasks 1 and 2, we obtained the second-best performance according to rankings based on classification and latency, demonstrating the effectiveness and consistency of our approaches for solving early detection problems in the Spanish language.