Analytic features in gambling study are performed based on the amount of data
monitoring on user daily actions. While performing the detection of problem
gambling, existing datasets provide relatively rich analytic features for
building machine learning based model. However, considering the complexity and
cost of collecting the analytic features in real applications, conducting
precise detection with less features will tremendously reduce the cost of data
collection. In this study, we propose a deep neural networks PGN4 that performs
well when using limited analytic features. Through the experiment on two
datasets, we discover that PGN4 only experiences a mere performance drop when
cutting 102 features to 5 features. Besides, we find the commonality within the
top 5 features from two datasets.

基于日常用户行为监测数据进行赌博研究的分析特征，提出一种深度神经网络模型 PGN4，使用少量分析特征时性能表现良好，研究发现该模型仅在将 102 个特征削减为 5 个特征时略有性能下降，且发现两个数据集中 5 个特征存在共同性。

使用机器学习方法检测问题博彩时更少特征的应用

Detection of Problem Gambling with Less Features Using Machine Learning  Methods

Problem gambling is a major public health concern and is associated with
profound psychological distress and economic problems. There are numerous
gambling communities on the internet where users exchange information about
games, gambling tactics, as well as gambling-related problems. Individuals
exhibiting higher levels of problem gambling engage more in such communities.
Online gambling communities may provide insights into problem-gambling
behaviour. Using data scraped from a major German gambling discussion board, we
fine-tuned a large language model, specifically a Bidirectional Encoder
Representations from Transformers (BERT) model, to predict signs of
problem-gambling from forum posts. Training data were generated by manual
annotation and by taking into account diagnostic criteria and gambling-related
cognitive distortions. Using k-fold cross-validation, our models achieved a
precision of 0.95 and F1 score of 0.71, demonstrating that satisfactory
classification performance can be achieved by generating high-quality training
material through manual annotation based on diagnostic criteria. The current
study confirms that a BERT-based model can be reliably used on small data sets
and to detect signatures of problem gambling in online communication data. Such
computational approaches may have potential for the detection of changes in
problem-gambling prevalence among online users.

使用德国一个主要赌博讨论论坛上的数据，通过对论坛帖子进行 BERT 模型的调整，训练结果显示，通过基于诊断标准进行手工注释生成高质量训练材料，可以在小数据集上可靠地使用 BERT 模型检测在线交流数据中的问题赌博迹象，此类计算方法可能有助于检测在线用户中问题赌博患病率的变化。