Risk prediction of pathological gambling on social media
Angelina Parfenova Marianne Clausel
This paper addresses the problem of risk prediction on social media data,
specifically focusing on the classification of Reddit users as having a
pathological gambling disorder. To tackle this problem, this paper focuses on
incorporating temporal and emotional features into the model. The preprocessing
phase involves dealing with the time irregularity of posts by padding
sequences. Two baseline architectures are used for preliminary evaluation: BERT
classifier on concatenated posts per user and GRU with LSTM on sequential data.
Experimental results demonstrate that the sequential models outperform the
concatenation-based model. The results of the experiments conclude that the
incorporation of a time decay layer (TD) and passing the emotion classification
layer (EmoBERTa) through LSTM improves the performance significantly.
Experiments concluded that the addition of a self-attention layer didn't
significantly improve the performance of the model, however provided easily
interpretable attention scores. The developed architecture with the inclusion
of EmoBERTa and TD layers achieved a high F1 score, beating existing benchmarks
on pathological gambling dataset. Future work may involve the early prediction
of risk factors associated with pathological gambling disorder and testing
models on other datasets. Overall, this research highlights the significance of
the sequential processing of posts including temporal and emotional features to
boost the predictive power, as well as adding an attention layer for
interpretability.