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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1666) Infodemiology and Infoveillance (1369) Descriptive Epidemiology and Population Size Estimates (109)

Published on in Vol 4, No 1 (2018): Jan-Mar

Online Health Monitoring using Facebook Advertisement Audience Estimates in the United States: Evaluation Study

Online Health Monitoring using Facebook Advertisement Audience Estimates in the United States: Evaluation Study

Online Health Monitoring using Facebook Advertisement Audience Estimates in the United States: Evaluation Study

Authors of this article:

Yelena Mejova1 Author Orcid Image ;   Ingmar Weber1 Author Orcid Image ;   Luis Fernandez-Luque1 Author Orcid Image
Article Authors Cited by (17) Tweetations (23) Metrics

Journals

  1. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  2. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  3. Zhang Y, Xia T, Huang L, Yin M, Sun M, Huang J, Ni Y, Ni J. Factors Influencing User Engagement of Health Information Disseminated by Chinese Provincial Centers for Disease Control and Prevention on WeChat: Observational Study. JMIR mHealth and uHealth 2019;7(6):e12245 View
  4. Nitzburg G, Weber I, Yom-Tov E. Internet Searches for Medical Symptoms Before Seeking Information on 12-Step Addiction Treatment Programs: A Web-Search Log Analysis. Journal of Medical Internet Research 2019;21(5):e10946 View
  5. An J, Kwak H, Qureshi H, Weber I. Precision Public Health Campaign: Delivering Persuasive Messages to Relevant Segments Through Targeted Advertisements on Social Media. JMIR Formative Research 2021;5(9):e22313 View
  6. Kassa Y, Cuevas R, Cuevas A. A Large-Scale Analysis of Facebook’s User-Base and User Engagement Growth. IEEE Access 2018;6:78881 View
  7. Zhang Y, Zong R, Kou Z, Shang L, Wang D. CrowdNAS: A Crowd-guided Neural Architecture Searching Approach to Disaster Damage Assessment. Proceedings of the ACM on Human-Computer Interaction 2022;6(CSCW2):1 View
  8. Hanna J, Nijhawan A, Lehmann C, Medford R. Simulating Facebook Advertisements to Establish Cost per New HIV Diagnosis Using Routine and Targeted Models in a Local Population. Healthcare 2022;10(7):1195 View
  9. Al Tamime R, Weber I. Using social media advertisement data to monitor the gender gap in STEM: opportunities and challenges. PeerJ Computer Science 2022;8:e994 View
  10. Hanna J, Saleh S, Lehmann C, Nijhawan A, Medford R. Reaching Populations at Risk for HIV Through Targeted Facebook Advertisements: Cost-Consequence Analysis. JMIR Formative Research 2023;7:e38630 View
  11. Bashar A, Wasiq M, Nyagadza B, Maziriri E. Emerging trends in social media marketing: a retrospective review using data mining and bibliometric analysis. Future Business Journal 2024;10(1) View
  12. Menezes S, Kumar A, Dutta S. Navigating in Turbulent Times: Using Social Media to Examine Small and family-Owned Business Topics and Sentiments during the COVID-19 Crisis. Information Systems Frontiers 2024 View

Books/Policy Documents

  1. Mejova Y. Handbook of Computational Social Science for Policy. View