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

Journals

  1. Mavragani A, Ochoa G, Tsagarakis K. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. Journal of Medical Internet Research 2018;20(11):e270 View
  2. Mavragani A, Ochoa G. Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis. Journal of Big Data 2018;5(1) View
  3. Caputi T, Ayers J, Dredze M, Suplina N, Burd-Sharps S. Collateral Crises of Gun Preparation and the COVID-19 Pandemic: Infodemiology Study. JMIR Public Health and Surveillance 2020;6(2):e19369 View
  4. Mavragani A. Tracking COVID-19 in Europe: Infodemiology Approach. JMIR Public Health and Surveillance 2020;6(2):e18941 View
  5. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  6. Rajan A, Sharaf R, Brown R, Sharaiha R, Lebwohl B, Mahadev S. Association of Search Query Interest in Gastrointestinal Symptoms With COVID-19 Diagnosis in the United States: Infodemiology Study. JMIR Public Health and Surveillance 2020;6(3):e19354 View
  7. Ali K, Zain-ul-abdin K, Li C, Johns L, Ali A, Carcioppolo N. Viruses Going Viral: Impact of Fear-Arousing Sensationalist Social Media Messages on User Engagement. Science Communication 2019;41(3):314 View
  8. Safarishahrbijari A, Osgood N. Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study. JMIR Public Health and Surveillance 2019;5(2):e11615 View
  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
  10. Mulderij-Jansen V, Elsinga J, Gerstenbluth I, Duits A, Tami A, Bailey A, Kuch U. Understanding risk communication for prevention and control of vector-borne diseases: A mixed-method study in Curaçao. PLOS Neglected Tropical Diseases 2020;14(4):e0008136 View
  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