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Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.
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SEM Approach for TPB: Application to Digital Health Software and Self-Health Management
The goal of this research is to investigate the feasibility of Structure Equation Modeling approach for developing a quantitative behavior model grounded on the Theory of Planned Behavior. Data collected from an IRB sanctioned pilot consisting of approximately 500 participants were used to develop the model. The validity of the model is evaluated based on Chi-square, p-value, and RMSEA for statistical power and goodness of fit. The utility of the model is studied through correlation analysis related to user engagement for self-health management. Example association pattern discovered from the analysis was illustrated for its use in digital health software development.
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- Award ID(s):
- 1648780
- PAR ID:
- 10048039
- Date Published:
- Journal Name:
- 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17); Dec 14-16, 2017; Las Vegas, Nevada, USA
- Page Range / eLocation ID:
- 529-535
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
