Problem Gambling

Problem gambling research focuses on identifying and predicting this behavioral disorder, aiming to improve prevention and intervention strategies. Current research utilizes machine learning, particularly deep neural networks like recurrent neural networks (RNNs) including LSTMs and GRUs, and transformer architectures such as BERT, to analyze diverse data sources, including social media posts and online gambling transaction records. These models leverage features like temporal patterns, emotional content, and behavioral metrics to classify individuals at risk or exhibiting problem gambling behaviors. This work holds significant implications for public health by enabling earlier detection and potentially more effective personalized interventions.