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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1015) Infodemiology and Infoveillance (1278) Public (e)Health, Digital Epidemiology and Public Health Informatics (646) Infectious Diseases (non-STD/STI) (1490) Influenza and Influenza-Like-Illness (ILI) Surveillance (203)

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 Wakamiya1 Author Orcid Image ;   Yukiko Kawai2, 3 Author Orcid Image ;   Eiji Aramaki1 Author Orcid Image
Article Authors Cited by (49) Tweetations (11) Metrics

Journals

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  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
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  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
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  22. Kellert O, Matlis N, Blythe R. Geolocation of multiple sociolinguistic markers in Buenos Aires. PLOS ONE 2022;17(9):e0274114 View
  23. Ferawati K, Liew K, Aramaki E, Wakamiya S. Monitoring Mentions of COVID-19 Vaccine Side Effects on Japanese and Indonesian Twitter: Infodemiological Study. JMIR Infodemiology 2022;2(2):e39504 View
  24. Abu Lekham L, Wang Y, Hey E, Khasawneh M. Multi-label text mining to identify reasons for appointments to drive population health analytics at a primary care setting. Neural Computing and Applications 2022;34(17):14971 View
  25. Siriaraya P, Zhang Y, Kawai Y, Jeszenszky P, Jatowt A, Zhao J. A city-wide examination of fine-grained human emotions through social media analysis. PLOS ONE 2023;18(2):e0279749 View
  26. Kim M, Jang J, Jeon S, Youm S. A Study on Customized Prediction of Daily Illness Risk Using Medical and Meteorological Data. Applied Sciences 2022;12(12):6060 View
  27. Wakamiya S, Morimoto O, Omichi K, Hara H, Kawase I, Koshiba R, Aramaki E. Exploring Relationships Between Tweet Numbers and Over-the-counter Drug Sales for Allergic Rhinitis: Retrospective Analysis. JMIR Formative Research 2022;6(2):e33941 View
  28. Jing F, Li Z, Qiao S, Zhang J, Olatosi B, Li X. Using geospatial social media data for infectious disease studies: a systematic review. International Journal of Digital Earth 2023;16(1):130 View
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  30. Albali N, Almudarra S, Al-Farsi Y, Alarifi A, Al Wahaibi A, Penttinen P. Comparative Performance Evaluation of the Public Health Surveillance Systems in 6 Gulf Cooperation Countries: Cross-sectional Study. JMIR Formative Research 2023;7:e41269 View
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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
  4. Gilbert J, Niu J, de Montigny S, Ng V, Rees E. AI for Disease Surveillance and Pandemic Intelligence. View