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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (718) Infodemiology and Infoveillance (895) Public (e)Health (289) Infectious Diseases (non-STD/STI) (1138) Influenza and Influenza-Like-Illness (ILI) Surveillance (75)

Published on in Vol 4, No 3 (2018): Jul-Sept

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/8627, first published .
Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study

Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study

Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study

Authors of this article:

Shoko Wakamiya 1 Author Orcid Image ;   Yukiko Kawai 2, 3 Author Orcid Image ;   Eiji Aramaki 1 Author Orcid Image
Article Authors Cited by (24) Tweetations (11) Metrics

Journals

  1. Huang Y, Huang D, Nguyen Q. Census Tract Food Tweets and Chronic Disease Outcomes in the U.S., 2015–2018. International Journal of Environmental Research and Public Health 2019;16(6):975 View
  2. Wakamiya S, Morita M, Kano Y, Ohkuma T, Aramaki E. Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations. Journal of Medical Internet Research 2019;21(2):e12783 View
  3. Edo-Osagie O, De La Iglesia B, Lake I, Edeghere O. A scoping review of the use of Twitter for public health research. Computers in Biology and Medicine 2020;122:103770 View
  4. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  5. Wakamiya S, Matsune S, Okubo K, Aramaki E. Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis. Journal of Medical Internet Research 2019;21(2):e10450 View
  6. Barros J, Duggan J, Rebholz-Schuhmann D. The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review. Journal of Medical Internet Research 2020;22(3):e13680 View
  7. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  8. Alotaibi S, Mehmood R, Katib I, Rana O, Albeshri A. Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning. Applied Sciences 2020;10(4):1398 View
  9. Tavoschi L, Quattrone F, D’Andrea E, Ducange P, Vabanesi M, Marcelloni F, Lopalco P. Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy. Human Vaccines & Immunotherapeutics 2020;16(5):1062 View
  10. Bartal A, Pliskin N, Tsur O, Karsai M. Local/Global contagion of viral/non-viral information: Analysis of contagion spread in online social networks. PLOS ONE 2020;15(4):e0230811 View
  11. Sarsam S, Al-Samarraie H, Al-Sadi A. Disease discovery-based emotion lexicon: a heuristic approach to characterise sicknesses in microblogs. Network Modeling Analysis in Health Informatics and Bioinformatics 2020;9(1) View
  12. Adnan M, Gao X, Bai X, Newbern E, Sherwood J, Jones N, Baker M, Wood T, Gao W. Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering. JMIR Public Health and Surveillance 2020;6(3):e18281 View
  13. Alomari E, Katib I, Albeshri A, Mehmood R. COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning. International Journal of Environmental Research and Public Health 2021;18(1):282 View
  14. Rodríguez-González A, Tuñas J, Prieto Santamaría L, Fernández Peces-Barba D, Menasalvas Ruiz E, Jaramillo A, Cotarelo M, Conejo Fernández A, Arce A, Gil A. Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques. Applied Sciences 2020;10(24):9019 View
  15. Prieto Santamaría L, Tuñas J, Fernández Peces-Barba D, Jaramillo A, Cotarelo M, Menasalvas E, Conejo Fernández A, Arce A, Gil de Miguel A, Rodríguez González A. Influenza and Measles-MMR: two case study of the trend and impact of vaccine-related Twitter posts in Spanish during 2015-2018. Human Vaccines & Immunotherapeutics 2021:1 View
  16. Amin S, Uddin M, Zeb M, Alarood A, Mahmoud M, Alkinani M. Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding. IEEE Access 2020;8:189054 View
  17. KÜÇÜK D, ARICI N, KÜÇÜK E. Sosyal medyada otomatik halk sağlığı takibi: Güncel bir derleme. Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 2021 View
  18. Amin S, Uddin M, alSaeed D, Khan A, Adnan M, Aziz F. Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches. Complexity 2021;2021:1 View
  19. Cai M. Natural language processing for urban research: A systematic review. Heliyon 2021;7(3):e06322 View
  20. Gabarron E, Rivera-Romero O, Miron-Shatz T, Grainger R, Denecke K. Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review. Yearbook of Medical Informatics 2021 View
  21. Miller M, Romine W, Oroszi T. Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events. JMIR Public Health and Surveillance 2021;7(6):e27976 View

Books/Policy Documents

  1. Suprem A, Musaev A, Pu C. Services – SERVICES 2019. View
  2. Amrani G, Khennou F, Chaoui N. Information and Software Technologies. View
  3. Krishnan G, Sowmya Kamath S, Sugumaran V. Natural Language Processing and Information Systems. View