基于社交媒体数据，用三种方法实现赌博成瘾早期风险识别，效果领先。
Tackling a Challenging Corpus for Early Detection of Gambling Disorder: UNSL at MentalRiskES 2025
- 采用CPI+DMC框架，结合BERT与SBERT模型提升预测精度。
- 两种方案在官方评测中位列前二，决策指标表现突出。
- 适合关注心理健康预警系统建设的研究者与开发者。
赌博障碍是一种复杂的心理行为成瘾，具有严重的身心与社会后果。基于网络的早期风险检测（ERD）已成为科学界通过社交媒体活动识别心理健康问题早期迹象的关键任务。本文介绍我们参与2025年MentalRiskES挑战赛的任务一，旨在对用户是否处于高风险或低风险发展为赌博相关障碍进行分类。我们提出了三种基于CPI+DMC方法的方案，分别以预测有效性与决策速度为独立目标。模型组件采用SS3、带扩展词汇表的BERT及SBERT，并结合历史用户分析制定决策策略。尽管语料极具挑战性，但其中两种方案在官方结果中位列第一与第二，在决策指标上表现优异。进一步分析显示，区分高低风险用户仍存困难，凸显需改进数据解读质量与系统透明度，推动更可靠的心理健康早期预警体系构建。
原文摘要 · Abstract (English)
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.
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