The goal of this study is to examine, among college students in the United States, the relationships of deteriorating depression and anxiety conditions with the changes in user behaviors when engaging with Google Search and YouTube during COVID-19.
Pervasive Computing; Mental Health.
We propose a personalized framework for classifying individuals with and without depression symptoms. We simulated the interplay between the distributional semantics and the temporal stochasticity of online activities with a mutually exciting multidimensional Hawkes Process.
Machine Learning → Temporal Point Process; Mental Health.
Despite the multitude of empirical advances, there is no theoretical understanding of the effectiveness of different pruning methods. We address this issue by setting up the problem in the statistical mechanics formulation of a teacher-student framework and deriving generalization error (GE) bounds of specific pruning methods.
Theory for Deep Learning → Statistical Mechanical Analysis.
In this work, we propose an alternative method to identify individuals with anxiety and further estimate their anxiety levels using personal online activity histories from YouTube and the Google Search engine, platforms that are used by millions of people daily.
Pervasive Computing; Mental Health.
We propose a framework for identifying individuals with a history of suicide ideation from their daily online search logs. We designed a graphical model optimized by MCMC methods that incorporates the latent interdependence between self-esteem, cognitive states, and online engagements to identify past suicidal ideation.
Machine Learning → Bayesian Inference; Mental Health.
Implemented three deep graph encoders over WordNet for word sense embeddings bounded by hypernyms-hyponyms and meronyms-holonyms: a child-sum graph bi-LSTM, a matrix decomposition method with graph kernels, and a GCN.
Word Sense Disambiguation.
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