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A Keyword Analysis Study on Postpartum Obesity Using Big Data.

Hyung-Ui Baik1, Bo-Kyung Seo1, Gyu-Ri Kim2

  • 1Department of Addiction Rehabilitation and Social Welfare, Eulji University, 553 Sanseong-daero, Sujeong-gu, Seongnam-si 13135, Gyeonggi-do, Korea.

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This study analyzed Korean internet data on postpartum obesity and depression. Findings offer insights for developing maternal health programs and digital healthcare solutions.

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Area of Science:

  • Health Informatics
  • Public Health
  • Data Science

Background:

  • Postpartum obesity and depression present significant challenges for maternal health.
  • Existing research and data are insufficient for effectively addressing these issues.
  • Internet portals are valuable sources for understanding public concerns and trends.

Purpose of the Study:

  • To analyze big data from Korean internet portals regarding postpartum obesity and depression.
  • To provide foundational data for maternal health problem-solving initiatives.
  • To inform the development of targeted maternal healthcare interventions.

Main Methods:

  • Selected Google and Naver as data sources.
  • Utilized keywords "postpartum obesity" and "postpartum depression" for data collection (Jan 1, 2019 - Dec 31, 2019).
  • Employed simple frequency analysis, N-gram analysis, and keyword network analysis.

Main Results:

  • Identified key themes and patterns related to postpartum obesity and depression in online data.
  • Established a data foundation for future research and program development.
  • Highlighted the need for integrated data collection on maternal health.

Conclusions:

  • Big data analysis from internet portals can inform maternal health strategies.
  • Further research is needed to integrate national data for comprehensive maternal care.
  • Development of mobile-based customized healthcare systems for maternal health is recommended.