Data science for human well-being
Abstract/Contents
- Abstract
- 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.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Althoff, Christopher Tim |
|---|---|
| Degree supervisor | Leskovec, Jurij |
| Thesis advisor | Leskovec, Jurij |
| Thesis advisor | Delp, Scott |
| Thesis advisor | Jurafsky, Dan, 1962- |
| Degree committee member | Delp, Scott |
| Degree committee member | Jurafsky, Dan, 1962- |
| Associated with | Stanford University, Computer Science Department. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Christopher Tim Althoff. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Christopher Tim Althoff
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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