Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An unobtrusive behavioral model of "gross national happiness"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting postpartum changes in emotion and behavior via social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social structure and depression in TrevorSpace
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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Depression is a serious and widespread public health challenge. We examine the potential for leveraging social media postings as a new type of lens in understanding depression in populations. Information gleaned from social media bears potential to complement traditional survey techniques in its ability to provide finer grained measurements over time while radically expanding population sample sizes. We present work on using a crowdsourcing methodology to build a large corpus of postings on Twitter that have been shared by individuals diagnosed with clinical depression. Next, we develop a probabilistic model trained on this corpus to determine if posts could indicate depression. The model leverages signals of social activity, emotion, and language manifested on Twitter. Using the model, we introduce a social media depression index that may serve to characterize levels of depression in populations. Geographical, demographic and seasonal patterns of depression given by the measure confirm psychiatric findings and correlate highly with depression statistics reported by the Centers for Disease Control and Prevention (CDC).