Automatic detection of problem-gambling signs from online texts using
  large language models
Elke Smith Nils Reiter Jan Peters
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.