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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1160) Infodemiology and Infoveillance (1599) Infectious Diseases (non-STD/STI) (1759)

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

This is a member publication of Elsevier

Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States

Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States

Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States

Authors of this article:

Mohsen Farhadloo1, 2 Author Orcid Image ;   Kenneth Winneg2 Author Orcid Image ;   Man-Pui Sally Chan1 Author Orcid Image ;   Kathleen Hall Jamieson2 Author Orcid Image ;   Dolores Albarracin1 Author Orcid Image
Article Authors Cited by (32) Tweetations (20) Metrics

Journals

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  2. Mavragani A, Ochoa G. Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis. Journal of Big Data 2018;5(1) View
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  9. Safarnejad L, Xu Q, Ge Y, Bagavathi A, Krishnan S, Chen S. Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study. JMIR Public Health and Surveillance 2020;6(3):e17175 View
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  11. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  12. Thelwall M, Thelwall S. A thematic analysis of highly retweeted early COVID-19 tweets: consensus, information, dissent and lockdown life. Aslib Journal of Information Management 2020;72(6):945 View
  13. Nguyen H, Nguyen T, Nguyen D. A graph-based approach for population health analysis using Geo-tagged tweets. Multimedia Tools and Applications 2021;80(5):7187 View
  14. Mavragani A, Gkillas K. COVID-19 predictability in the United States using Google Trends time series. Scientific Reports 2020;10(1) View
  15. Donelson C, Sutter C, Pham G, Narang K, Wang C, Yun J. Using a Machine Learning Methodology to Analyze Reddit Posts regarding Child Feeding Information. Journal of Child and Family Studies 2021;30(5):1290 View
  16. Shah A, Yan X, Qayyum A, Naqvi R, Shah S. Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach. International Journal of Medical Informatics 2021;149:104434 View
  17. Chenworth M, Perrone J, Love J, Graves R, Hogg-Bremer W, Sarker A. Methadone and suboxone® mentions on twitter: thematic and sentiment analysis. Clinical Toxicology 2021;59(11):982 View
  18. Shah A, Naqvi R, Jeong O. Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets. International Journal of Environmental Research and Public Health 2021;18(9):4743 View
  19. Lossio-Ventura J, Gonzales S, Morzan J, Alatrista-Salas H, Hernandez-Boussard T, Bian J. Evaluation of clustering and topic modeling methods over health-related tweets and emails. Artificial Intelligence in Medicine 2021;117:102096 View
  20. Keller S, Honea J, Ollivant R. How Social Media Comments Inform the Promotion of Mask-Wearing and Other COVID-19 Prevention Strategies. International Journal of Environmental Research and Public Health 2021;18(11):5624 View
  21. Karami A, Kadari R, Panati L, Nooli S, Bheemreddy H, Bozorgi P. Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?. ISPRS International Journal of Geo-Information 2021;10(6):373 View
  22. Amusa L, Twinomurinzi H, Phalane E, Phaswana-Mafuya R. Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda. Interactive Journal of Medical Research 2023;12:e42292 View
  23. Liu Z, Jiang Z, Kip G, Snigdha K, Xu J, Wu X, Khan N, Schultz T. An infodemiological framework for tracking the spread of SARS-CoV-2 using integrated public data. Pattern Recognition Letters 2022;158:133 View
  24. Amusa L, Twinomurinzi H, Okonkwo C. Modeling COVID-19 incidence with Google Trends. Frontiers in Research Metrics and Analytics 2022;7 View
  25. Bağcı N, Peker I. Interest in dentistry in early months of the COVID‐19 global pandemic: A Google Trends approach. Health Information & Libraries Journal 2022;39(3):284 View
  26. Hussain Z, Sheikh Z, Tahir A, Dashtipour K, Gogate M, Sheikh A, Hussain A. Artificial Intelligence–Enabled Social Media Analysis for Pharmacovigilance of COVID-19 Vaccinations in the United Kingdom: Observational Study. JMIR Public Health and Surveillance 2022;8(5):e32543 View
  27. Park S, Jang D, Kim D, Choi C. Key Attributes and Clusters of the Korean Exercise Healthcare Industry Viewed through Big Data: Comparison before and after the COVID-19 Pandemic. Healthcare 2023;11(15):2133 View
  28. Shen Y, Luo Z, Song X, Liu C, Wang H. Research on the evolution of cross-platform online public opinion for public health emergencies considering stakeholders. PLOS ONE 2024;19(6):e0304877 View
  29. Baek K, Jeong J, Kim H, Shin D, Kim J, Lee G, Cho J. Seasonal and Weekly Patterns of Korean Adolescents’ Web Search Activity on Insomnia: Retrospective Study. JMIR Formative Research 2024;8:e52977 View
  30. Gorissen S, Liao Y, Jensen J, John K. Does scientific uncertainty alter public perception of HIV-cure science? A message experiment with U.S. adults. AIDS Care 2025;37(6):1003 View
  31. Idahor C, Esomu E, Ogbonna N, Momoh Z, Ogbeide O, Ikhu-Omoregbe O, Adigwe A, Erhabor O, Osaghae O, Orons N. Infectious Disease Surveillance in the Era of Big Data and AI: Opportunities and Pitfalls. Cureus 2025 View
  32. Ma Y, Lyu K, Xu H, Jiao C, Ren L, Lyu H, Zhang A, Yang Y, Li Z, Zhu K, Ren X, Chen J. A COVID-19 prediction Model based on symptomatology google trends and its optimization. BMC Infectious Diseases 2026 View