Published on in Vol 20, No 7 (2018): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9413, first published .
Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models

Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models

Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models

Journals

  1. Fagherazzi G, Goetzinger C, Rashid M, Aguayo G, Huiart L. Digital Health Strategies to Fight COVID-19 Worldwide: Challenges, Recommendations, and a Call for Papers. Journal of Medical Internet Research 2020;22(6):e19284 View
  2. Gupta A, Katarya R. Social media based surveillance systems for healthcare using machine learning: A systematic review. Journal of Biomedical Informatics 2020;108:103500 View
  3. Chang Y, Chiang W, Wang W, Lin C, Hung L, Tsai Y, Suen J, Chen Y. Google Trends-based non-English language query data and epidemic diseases: a cross-sectional study of the popular search behaviour in Taiwan. BMJ Open 2020;10(7):e034156 View
  4. Zhao Y, Guo Y, He X, Wu Y, Yang X, Prosperi M, Jin Y, Bian J. Assessing mental health signals among sexual and gender minorities using Twitter data. Health Informatics Journal 2020;26(2):765 View
  5. Shah Z, Surian D, Dyda A, Coiera E, Mandl K, Dunn A. Automatically Appraising the Credibility of Vaccine-Related Web Pages Shared on Social Media: A Twitter Surveillance Study. Journal of Medical Internet Research 2019;21(11):e14007 View
  6. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  7. Amith M, Cohen T, Cunningham R, Savas L, Smith N, Cuccaro P, Gabay E, Boom J, Schvaneveldt R, Tao C. Mining HPV Vaccine Knowledge Structures of Young Adults From Reddit Using Distributional Semantics and Pathfinder Networks. Cancer Control 2020;27(1):107327481989144 View
  8. Mavragani A. Tracking COVID-19 in Europe: Infodemiology Approach. JMIR Public Health and Surveillance 2020;6(2):e18941 View
  9. Zhai Y, Yao Y, Guan Q, Liang X, Li X, Pan Y, Yue H, Yuan Z, Zhou J. Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata. International Journal of Geographical Information Science 2020;34(7):1475 View
  10. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  11. Rashid M, Wang D. CovidSens: a vision on reliable social sensing for COVID-19. Artificial Intelligence Review 2021;54(1):1 View
  12. Meadows C, Meadows C, Tang L, Liu W. Unraveling Public Health Crises Across Stages: Understanding Twitter Emotions and Message Types During the California Measles Outbreak. Communication Studies 2019;70(4):453 View
  13. Du J, Chen Q, Peng Y, Xiang Y, Tao C, Lu Z. ML-Net: multi-label classification of biomedical texts with deep neural networks. Journal of the American Medical Informatics Association 2019;26(11):1279 View
  14. Lili D, Lei S, Gang X, Patnaik S. Public opinion analysis of complex network information of local similarity clustering based on intelligent fuzzy system. Journal of Intelligent & Fuzzy Systems 2020;39(2):1693 View
  15. Du J, Luo C, Shegog R, Bian J, Cunningham R, Boom J, Poland G, Chen Y, Tao C. Use of Deep Learning to Analyze Social Media Discussions About the Human Papillomavirus Vaccine. JAMA Network Open 2020;3(11):e2022025 View
  16. Tang L, Zou W. Health Information Consumption under COVID-19 Lockdown: An Interview Study of Residents of Hubei Province, China. Health Communication 2021;36(1):74 View
  17. Shen L, Yao R, Zhang W, Evans R, Cao G, Zhang Z. Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data. JMIR Medical Informatics 2021;9(3):e27079 View
  18. Mavragani A, Gkillas K. COVID-19 predictability in the United States using Google Trends time series. Scientific Reports 2020;10(1) View
  19. Ibrahim M, Ghani Khan M, Mehmood F, Asim M, Mahmood W. GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification. Journal of Biomedical Informatics 2021;116:103699 View
  20. Karafillakis E, Martin S, Simas C, Olsson K, Takacs J, Dada S, Larson H. Methods for Social Media Monitoring Related to Vaccination: Systematic Scoping Review. JMIR Public Health and Surveillance 2021;7(2):e17149 View
  21. Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, Byrd B, Smyser J. Quantifying the rise of vaccine opposition on Twitter during the COVID-19 pandemic. Journal of Communication in Healthcare 2021;14(1):12 View
  22. Tang L, Liu W, Thomas B, Tran H, Zou W, Zhang X, Zhi D. Texas Public Agencies’ Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach. JMIR Public Health and Surveillance 2021;7(4):e26720 View