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Loneliness is a widely affecting mental health symptom and can be mediated byand co-vary with patterns of social exposure. Using momentary survey andsmartphone sensing data collected from 129 Android-using college studentparticipants over three weeks, we (1) investigate and uncover the relationsbetween momentary loneliness experience and companionship type and (2) proposeand validate novel geosocial features of smartphone-based Bluetooth and GPSdata for predicting loneliness and companionship type in real time. We base ourfeatures on intuitions characterizing the quantity and spatiotemporalpredictability of an individual's Bluetooth encounters and GPS locationclusters to capture personal significance of social exposure scenariosconditional on their temporal distribution and geographic patterns. We examineour features' statistical correlation with momentary loneliness throughregression analyses and evaluate their predictive power using a sliding windowprediction procedure. Our features achieved significant performance improvementcompared to baseline for predicting both momentary loneliness and companionshiptype, with the effect stronger for the loneliness prediction task. As such werecommend incorporation and further evaluation of our geosocial featuresproposed in this study in future mental health sensing and context-awarecomputing applications.

Improving Prediction of Real-Time Loneliness and Companionship Type Using Geosocial Features of Personal Smartphone Data