Title: Automatic detection of problem-gambling signs from online texts using large language models Show Chinese title

Title: 使用大型语言模型从在线文本中自动检测赌博问题迹象

Abstract: 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. Show Chinese abstract

Abstract: 问题赌博是一个主要的公共卫生问题，与严重的心理困扰和经济问题有关。 互联网上存在许多赌博社区，用户在这些社区中交流关于游戏、赌博策略以及与赌博相关的问题的信息。 表现出更高水平问题赌博的个体更频繁地参与此类社区。 在线赌博社区可能为问题赌博行为提供见解。 使用从一个主要的德国赌博讨论板上抓取的数据，我们微调了一个大型语言模型，具体来说是一个双向编码器表示的变换器（BERT）模型，以从论坛帖子中预测问题赌博的迹象。 训练数据通过手动标注以及考虑诊断标准和与赌博相关的认知扭曲来生成。 使用k折交叉验证，我们的模型实现了0.95的精确率和0.71的F1分数，表明通过基于诊断标准的手动标注生成高质量的训练材料可以实现令人满意的分类性能。 当前研究证实，基于BERT的模型可以在小数据集上可靠使用，并用于检测在线通信数据中的问题赌博特征。 这种计算方法可能在检测在线用户中问题赌博流行率的变化方面具有潜力。