Community evolution in dynamic multi-mode networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The structure of online diffusion networks
Proceedings of the 13th ACM Conference on Electronic Commerce
Major life changes and behavioral markers in social media: case of childbirth
Proceedings of the 2013 conference on Computer supported cooperative work
Predicting postpartum changes in emotion and behavior via social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Mental illness is a serious and widespread health challenge in our society today. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. This position paper highlights some recent attempts examining the potential for leveraging social media postings as a new type of lens in understanding mental illness in individuals and populations. Information gleaned from social media bears potential to complement traditional survey techniques in its ability to provide finer grained measurements of behavior over time while radically expanding population sample sizes. We conclude highlighting how this research direction may be useful in developing tools for identifying the onset of depressive disorders in individuals, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from mental illness to be more proactive about their mental health.