This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1349) Innovations and Technology for Physical Activity Education (742) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 21, No 6 (2019): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12394, first published .
Monitoring Physical Activity Levels Using Twitter Data: Infodemiology Study

Monitoring Physical Activity Levels Using Twitter Data: Infodemiology Study

Monitoring Physical Activity Levels Using Twitter Data: Infodemiology Study

Authors of this article:

Sam Liu1 Author Orcid Image ;   Brian Chen2 Author Orcid Image ;   Alex Kuo2 Author Orcid Image
Article Authors Cited by (14) Tweetations (52) Metrics

Journals

  1. Cossin S, Thiébaut R. Public Health and Epidemiology Informatics: Recent Research Trends Moving toward Public Health Data Science. Yearbook of Medical Informatics 2020;29(01):231 View
  2. Liu S, Lithopoulos A, Zhang C, Garcia-Barrera M, Rhodes R. Personality and perceived stress during COVID-19 pandemic: Testing the mediating role of perceived threat and efficacy. Personality and Individual Differences 2021;168:110351 View
  3. Wang Y, Zhang H, Zheng Q, Tang K, Sun Q. Public interest in Raynaud's phenomenon: A Google Trends analysis. Dermatologic Therapy 2020;33(6) View
  4. Singh T, Roberts K, Cohen T, Cobb N, Wang J, Fujimoto K, Myneni S. Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review. JMIR Public Health and Surveillance 2020;6(4):e21660 View
  5. Camacho-Rivera M, Vo H, Huang X, Lau J, Lawal A, Kawaguchi A. Evaluating Asthma Mobile Apps to Improve Asthma Self-Management: User Ratings and Sentiment Analysis of Publicly Available Apps. JMIR mHealth and uHealth 2020;8(10):e15076 View
  6. van Draanen J, Tao H, Gupta S, Liu S. Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study. JMIR Public Health and Surveillance 2020;6(4):e18540 View
  7. Pulido-Polo M, Hernández-Santaolalla V, Lozano-González A. Uso institucional de Twitter para combatir la infodemia causada por la crisis sanitaria de la Covid-19. El profesional de la información 2021 View
  8. Liu S, Perdew M, Lithopoulos A, Rhodes R. The Feasibility of Using Instagram Data to Predict Exercise Identity and Physical Activity Levels: Cross-sectional Observational Study. Journal of Medical Internet Research 2021;23(4):e20954 View
  9. Sun P, Lu W, Jin L. How the natural environment in downtown neighborhood affects physical activity and sentiment: Using social media data and machine learning. Health & Place 2023;79:102968 View
  10. Jin P, Zhao Q, Zang Y, Zhang Q, Shen C, Zhang H, Zhang H, Zhi L. A Google Trends analysis revealed global public interest and awareness of nasal polyps. European Archives of Oto-Rhino-Laryngology 2023;280(6):2831 View
  11. Liu Z, Maneekul P, Pendergrast C, Doubleday A, Miles S, Errett N, Choe Y. Physical activity monitoring data following disasters. Sustainable Cities and Society 2022;81:103814 View
  12. Morita P, Zakir Hussain I, Kaur J, Lotto M, Butt Z. Tweeting for Health Using Real-time Mining and Artificial Intelligence–Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on Twitter. Journal of Medical Internet Research 2023;25:e44356 View
  13. Christodoulakis N, Abdelkader W, Lokker C, Cotterchio M, Griffith L, Vanderloo L, Anderson L. Public Health Surveillance of Behavioral Cancer Risk Factors During the COVID-19 Pandemic: Sentiment and Emotion Analysis of Twitter Data. JMIR Formative Research 2023;7:e46874 View
  14. Sun P, Zhao H, Lu W. How urban environments affect public sentiment and physical activity using a cognitive computing framework. Frontiers of Architectural Research 2024;13(5):946 View