Detecting Individuals with Depressive Disorder fromPersonal Google Search and YouTube History Logs
This addresses the need for non-invasive, immediate screening of depression, though it is incremental as it applies existing methods to new data types.
The paper tackled the problem of detecting depressive disorder by using personal Google Search and YouTube history logs, achieving an average F1 score of 0.77 and AUC ROC of 0.81.
Depressive disorder is one of the most prevalent mental illnesses among the global population. However, traditional screening methods require exacting in-person interviews and may fail to provide immediate interventions. In this work, we leverage ubiquitous personal longitudinal Google Search and YouTube engagement logs to detect individuals with depressive disorder. We collected Google Search and YouTube history data and clinical depression evaluation results from $212$ participants ($99$ of them suffered from moderate to severe depressions). We then propose a personalized framework for classifying individuals with and without depression symptoms based on mutual-exciting point process that captures both the temporal and semantic aspects of online activities. Our best model achieved an average F1 score of $0.77 \pm 0.04$ and an AUC ROC of $0.81 \pm 0.02$.