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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050) Infodemiology and Infoveillance (1369) Public (e)Health, Digital Epidemiology and Public Health Informatics (750) Infectious Diseases (non-STD/STI) (1592) Influenza and Influenza-Like-Illness (ILI) Surveillance (219)

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 (51) Tweetations (10) 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 2022;18(1):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;30(01):200 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
  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
  29. Alkouz B, Al Aghbari Z, Al-Garadi M, Sarker A. Deepluenza: Deep learning for influenza detection from Twitter. Expert Systems with Applications 2022;198:116845 View
  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
  31. Simanjuntak L, Mahendra R, Yulianti E. We Know You Are Living in Bali: Location Prediction of Twitter Users Using BERT Language Model. Big Data and Cognitive Computing 2022;6(3):77 View
  32. Pilipiec P, Samsten I, Bota A, Rocha L. Surveillance of communicable diseases using social media: A systematic review. PLOS ONE 2023;18(2):e0282101 View
  33. Zhou X, Lee E, Wang X, Lin L, Xuan Z, Wu D, Lin H, Shen P. Infectious diseases prevention and control using an integrated health big data system in China. BMC Infectious Diseases 2022;22(1) View
  34. McKitrick M, Schuurman N, Crooks V. Collecting, analyzing, and visualizing location-based social media data: review of methods in GIS-social media analysis. GeoJournal 2022;88(1):1035 View
  35. Bartal A, Jagodnik K. Role-Aware Information Spread in Online Social Networks. Entropy 2021;23(11):1542 View
  36. Benis A, Chatsubi A, Levner E, Ashkenazi S. Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study. JMIR Infodemiology 2021;1(1):e31983 View
  37. Kanchan S, Gaidhane A. Social Media Role and Its Impact on Public Health: A Narrative Review. Cureus 2023 View
  38. Unnikrishnan R, S. S, V.S. A. Efficient parameter tuning of neural foundation models for drug perspective prediction from unstructured socio-medical data. Engineering Applications of Artificial Intelligence 2023;123:106214 View
  39. Wang A, Dara R, Yousefinaghani S, Maier E, Sharif S. A Review of Social Media Data Utilization for the Prediction of Disease Outbreaks and Understanding Public Perception. Big Data and Cognitive Computing 2023;7(2):72 View
  40. Tian Y, Zhang W, Duan L, McDonald W, Osgood N. Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada. Frontiers in Digital Health 2023;5 View
  41. Thakur N, Patel K, Poon A, Shah R, Azizi N, Han C. A Comprehensive Analysis and Investigation of the Public Discourse on Twitter about Exoskeletons from 2017 to 2023. Future Internet 2023;15(10):346 View
  42. Bojić L, Mitrović-Dankulov M, Pantelić N. Humidity and air temperature predict post count on Twitter in 10 countries: Weather changes & LIWC psychological categories. Ekonomika preduzeca 2023;71(3-4):213 View
  43. Yang J, Eloundou-Enyegue P, Tong Y, Gao J, Zhou M, Zhou Y, Su C, Kolancali P, Zhang N, Li F, Allegrini J, Al Azmeh Z, Wang A, Zheng M, Zhang X, Mai Y, Donaldson J. Investigate the impact of media on public understanding of health and medical science in China. SHS Web of Conferences 2023;180:01008 View
  44. Deiner M, Deiner N, Hristidis V, McLeod S, Doan T, Lietman T, Porco T. Use of Large Language Models to Assess the Likelihood of Epidemics From the Content of Tweets: Infodemiology Study. Journal of Medical Internet Research 2024;26:e49139 View
  45. Hanny D, Resch B. Clustering-Based Joint Topic-Sentiment Modeling of Social Media Data: A Neural Networks Approach. Information 2024;15(4):200 View
  46. Gautam A, Raza Z. Disease outbreak prediction using natural language processing: a review. Knowledge and Information Systems 2024;66(11):6561 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
  4. Gilbert J, Niu J, de Montigny S, Ng V, Rees E. AI for Disease Surveillance and Pandemic Intelligence. View