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The utility of Twitter data as a medium to support population-level mentalhealth monitoring is not well understood. In an effort to better understand thepredictive power of supervised machine learning classifiers and the influenceof feature sets for efficiently classifying depression-related tweets on alarge-scale, we conducted two feature study experiments. In the firstexperiment, we assessed the contribution of feature groups such as lexicalinformation (e.g., unigrams) and emotions (e.g., strongly negative) using afeature ablation study. In the second experiment, we determined the percentileof top ranked features that produced the optimal classification performance byapplying a three-step feature elimination approach. In the first experiment, weobserved that lexical features are critical for identifying depressivesymptoms, specifically for depressed mood (-35 points) and for disturbed sleep(-43 points). In the second experiment, we observed that the optimal F1-scoreperformance of top ranked features in percentiles variably ranged acrossclasses e.g., fatigue or loss of energy (5th percentile, 288 features) todepressed mood (55th percentile, 3,168 features) suggesting there is noconsistent count of features for predicting depressive-related tweets. Weconclude that simple lexical features and reduced feature sets can producecomparable results to larger feature sets.

Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health