Exploring Time-Dependent Concerns about Pregnancy and Childbirth from Search Logs
Abstract
We study time-dependent patterns of information seeking about pregnancy, birth, and the first several weeks of caring for newborns via analyses of queries drawn from anonymized search engine logs. We show how we can detect and align web search behavior for a population of searchers with the natural clock of gestational physiology via proxies for ground truth based on searchers' self-report queries (e.g., [I am 30 weeks pregnant and my baby is moving a lot]). Then, we present a methodology for performing additional alignments, that are valuable for learning about the concerns, curiosities, and needs that arise over time with pregnancy and early parenting. Our findings have implications for learning about the temporal dynamics of pregnancy-related interests and concerns, and also for the design of systems that tailor their responses to point estimates of each searcher's current stage in pregnancy.
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Long-term search logs provide richer insights into the evolution of users' interests, needs, or how their experiences unfold than short-term, within-session search information.
“Others observed that long-term search logs (vs. short-term, within-session search information) provide richer insights into the evolution of users' interests, needs, or how their experiences unfold”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Fourney et al., 2015
Cites
Toward universal criteria for gestational diabetes: the 75-gram glucose tolerance test in pregnancy
Sacks, D. A., Greenspoon, J. S., Abu-Fadil, S., Henry, H. M., Wolde-Tsadik, G., & Yao, J. F. F. (1995) · American Journal of Obstetrics and Gynecology · doi:10.1016/0002-9378(95)90580-4 · Cited by 210 · see citing works
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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
Examining menstrual tracking to inform the design of personal informatics tools
Epstein, D. A., Lee, N. B., Kang, J. H., Agapie, E., Schroeder, J., Pina, L. R., Fogarty, J., Kientz, J. A., & Munson, S. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025635 · Cited by 346 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
- Authors
- Fourney, A., White, R. W., & Horvitz, E.
- Affiliations
- University of Waterloo (CA)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2015-04-18
- Pages
- 737-746
- OpenAlex
- W2107752831
- Cited by
- 41
- References
- 1
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 1 · 2022: 4 · 2021: 3 · 2020: 4 · 2019: 6 · 2018: 7 · 2017: 6 · 2016: 2 · 2015: 2
- FWCI
- 4.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Health Sciences › Health Professions › General Health Professions › Maternal and Neonatal Healthcare
- Keywords
- pregnancy, childbirth, early parenting, online search behavior, temporal dynamics, pregnancy stage, search logs, personalized search
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