Tackling a Challenging Corpus for Early Detection of Gambling Disorder: UNSL at MentalRiskES 2025
This work addresses early risk detection for gambling disorder, a behavioral addiction with severe consequences, but it is incremental as it applies existing methods to a new challenge corpus.
The paper tackled early detection of gambling disorder by classifying users as high or low risk using social media data, achieving top two positions in the MentalRiskES 2025 challenge with notable performance in decision metrics.
Gambling disorder is a complex behavioral addiction that is challenging to understand and address, with severe physical, psychological, and social consequences. Early Risk Detection (ERD) on the Web has become a key task in the scientific community for identifying early signs of mental health behaviors based on social media activity. This work presents our participation in the MentalRiskES 2025 challenge, specifically in Task 1, aimed at classifying users at high or low risk of developing a gambling-related disorder. We proposed three methods based on a CPI+DMC approach, addressing predictive effectiveness and decision-making speed as independent objectives. The components were implemented using the SS3, BERT with extended vocabulary, and SBERT models, followed by decision policies based on historical user analysis. Although it was a challenging corpus, two of our proposals achieved the top two positions in the official results, performing notably in decision metrics. Further analysis revealed some difficulty in distinguishing between users at high and low risk, reinforcing the need to explore strategies to improve data interpretation and quality, and to promote more transparent and reliable ERD systems for mental disorders.