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.

翻译：赌博障碍是一种复杂的行为成瘾，其理解和应对均具挑战性，并伴随严重的生理、心理及社会后果。基于网络平台的早期风险检测已成为科学界通过社交媒体活动识别心理健康行为早期迹象的关键任务。本研究介绍了我们参与MentalRiskES 2025挑战赛（特别是任务1）的工作，该任务旨在对发展为赌博相关障碍的高风险与低风险用户进行分类。我们基于CPI+DMC框架提出了三种方法，将预测效能与决策速度作为独立目标进行处理。相关组件通过SS3、扩展词汇表的BERT以及SBERT模型实现，并辅以基于用户历史分析的决策策略。尽管该语料库极具挑战性，我们的两项提案仍在官方结果中位列前两名，在决策指标上表现突出。进一步分析揭示了区分高风险与低风险用户存在一定困难，这凸显了需探索提升数据解读与质量的策略，并推动建立更透明可靠的精神障碍早期风险检测系统。