  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 $0.81 \pm 0.02$.
