Depression and Self-Harm Risk Assessment in Online Forums
Table 1: The hyperparameters used by each model.
['Method', 'Method', 'Convolution Size', 'Convolution Filters', 'Convolution Pool Len.', 'Dense Layers', 'Dropout', 'Class Balance']
The severity risk assessment models’ hyperparameters were chosen using 10-fold cross validation on the 947 ReachOut training posts, with 15% of each fold used as validation data. The depression identification model’s hyperparameters were chosen using the Reddit validation set. The depression identification model’s second convolutional layer (i.e., the layer used to merge post representations) used filters of length 15, a stride of length 15, and the same number of filters as the first convolutional layer. The convolution size, number of convolutional filters, pooling type, pooling length, and number of dense layers was similar across all post models. Class balancing was performed with Categorical Cross Ent. by weighting classes inversely proportional to their frequencies, whereas sampling an equal number of instances for each class worked best with the other methods.