Data science for human well-being
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| Call number | Note | Status |
|---|---|---|
| 3781 2018 A |
Context
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Description
Creators/Contributors
- Author/Creator
- Althoff, Christopher Tim, author.
- Contributor
- Leskovec, Jurij degree supervisor. Thesis advisor
- Delp, Scott degree committee member. Thesis advisor
- Jurafsky, Dan, 1962- degree committee member. Thesis advisor
- Stanford University. Computer Science Department.
Contents/Summary
- Summary
- The popularity of wearable and mobile devices, including smartphones and smartwatches, has generated an explosion of detailed behavioral data. These massive digital traces provide us with an unparalleled opportunity to realize new types of scientific approaches that enable novel insights about our lives, health, and happiness. However, gaining actionable insights from these data requires new computational approaches that turn observational, scientifically "weak" data into strong scientific results and can computationally test domain theories at scale. In this dissertation, we describe novel computational methods that leverage digital activity traces at the scale of billions of actions taken by millions of people. These methods combine insights from data mining, social network analysis, and natural language processing to improve our understanding of physical and mental well-being: (1) We show how massive digital activity traces reveal unknown health inequality around the world, and (2) how personalized predictive models can support targeted interventions to combat this inequality. (3) We demonstrate that modeling the speed of user search engine interactions can improve our understanding of sleep and cognitive performance. (4) Lastly, we describe how natural language processing methods can help improve counseling services for millions of people in crisis.
Bibliographic information
- Publication date
- 2018
- Copyright date
- 2018
- Note
- Submitted to the Computer Science Department.
- Note
- Thesis Ph.D. Stanford University 2018.
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