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Tackling a Challenging Corpus for Early Detection of Gambling Disorder: UNSL at MentalRiskES 2025

arXiv 2511.23325 ↗First posted 28 Nov 2025cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 28 Nov 2025.

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References · 9

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2022Efficient Few-Shot Learning Without Promptscited by 137
  3. 2023Spanish Pre-trained BERT Model and Evaluation Datacited by 81
  4. 2019A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streamscited by 18
  5. 2023Early Detection of Depression and Eating Disorders in Spanish: UNSL at MentalRiskES 2023cited by 5
  6. 2023Strategies to Harness the Transformers' Potential: UNSL at eRisk 2023cited by 3
  7. 2024A Time-Aware Approach to Early Detection of Anorexia: UNSL at eRisk 2024cited by 1
  8. 2025Hacia la interpretabilidad de la detecci'on anticipada de riesgos de depresi'on utilizando grandes modelos de lenguajecited by 1
  9. 2025Temporal fine-tuning for early risk detectioncited by 1

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