IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0157734.html
   My bibliography  Save this article

Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza

Author

Listed:
  • Chris Allen
  • Ming-Hsiang Tsou
  • Anoshe Aslam
  • Anna Nagel
  • Jean-Mark Gawron

Abstract

Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical scales. In this paper, we introduce an improved framework for monitoring influenza outbreaks using the social media platform Twitter. Relying upon techniques from geographic information science (GIS) and data mining, Twitter messages were collected, filtered, and analyzed for the thirty most populated cities in the United States during the 2013–2014 flu season. The results of this procedure are compared with national, regional, and local flu outbreak reports, revealing a statistically significant correlation between the two data sources. The main contribution of this paper is to introduce a comprehensive data mining process that enhances previous attempts to accurately identify tweets related to influenza. Additionally, geographical information systems allow us to target, filter, and normalize Twitter messages.

Suggested Citation

  • Chris Allen & Ming-Hsiang Tsou & Anoshe Aslam & Anna Nagel & Jean-Mark Gawron, 2016. "Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-10, July.
  • Handle: RePEc:plo:pone00:0157734
    DOI: 10.1371/journal.pone.0157734
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0157734
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0157734&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0157734?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Andrea Freyer Dugas & Mehdi Jalalpour & Yulia Gel & Scott Levin & Fred Torcaso & Takeru Igusa & Richard E Rothman, 2013. "Influenza Forecasting with Google Flu Trends," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    2. Declan Butler, 2013. "When Google got flu wrong," Nature, Nature, vol. 494(7436), pages 155-156, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Amir Hassan Zadeh & Hamed M. Zolbanin & Ramesh Sharda & Dursun Delen, 2019. "Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis," Information Systems Frontiers, Springer, vol. 21(4), pages 743-760, August.
    2. Victor Olsavszky & Mihnea Dosius & Cristian Vladescu & Johannes Benecke, 2020. "Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database," IJERPH, MDPI, vol. 17(14), pages 1-17, July.
    3. Jungyoon Kim & Jihye Lim, 2021. "A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
    4. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    5. Songhee Cheon & Jungyoon Kim & Jihye Lim, 2019. "The Use of Deep Learning to Predict Stroke Patient Mortality," IJERPH, MDPI, vol. 16(11), pages 1-12, May.
    6. Siqing Shan & Qi Yan & Yigang Wei, 2020. "Infectious or Recovered? Optimizing the Infectious Disease Detection Process for Epidemic Control and Prevention Based on Social Media," IJERPH, MDPI, vol. 17(18), pages 1-25, September.
    7. Sameer Kumar & Chong Xu & Nidhi Ghildayal & Charu Chandra & Muer Yang, 2022. "Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 823-851, December.
    8. Ghasem Javadi & Mohammad Taleai, 2020. "Integration of User Generated Geo-contents and Official Data to Assess Quality of Life in Intra-national Level," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 205-235, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zeynep Ertem & Dorrie Raymond & Lauren Ancel Meyers, 2018. "Optimal multi-source forecasting of seasonal influenza," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-16, September.
    2. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    3. Victor Olsavszky & Mihnea Dosius & Cristian Vladescu & Johannes Benecke, 2020. "Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database," IJERPH, MDPI, vol. 17(14), pages 1-17, July.
    4. Linying Yang & Teng Zhang & Peter Glynn & David Scheinker, 2021. "The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE)," Health Care Management Science, Springer, vol. 24(2), pages 375-401, June.
    5. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    6. Steven Heston & Nitish R. Sinha, 2016. "News versus Sentiment : Predicting Stock Returns from News Stories," Finance and Economics Discussion Series 2016-048, Board of Governors of the Federal Reserve System (U.S.).
    7. Jose L Herrera & Ravi Srinivasan & John S Brownstein & Alison P Galvani & Lauren Ancel Meyers, 2016. "Disease Surveillance on Complex Social Networks," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-16, July.
    8. Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.
    9. Mansour Ebrahimi & Parisa Aghagolzadeh & Narges Shamabadi & Ahmad Tahmasebi & Mohammed Alsharifi & David L Adelson & Farhid Hemmatzadeh & Esmaeil Ebrahimie, 2014. "Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-14, May.
    10. Jiachen Sun & Peter A. Gloor, 2021. "Assessing the Predictive Power of Online Social Media to Analyze COVID-19 Outbreaks in the 50 U.S. States," Future Internet, MDPI, vol. 13(7), pages 1-13, July.
    11. Daniel Alejandro Gónzalez-Bandala & Juan Carlos Cuevas-Tello & Daniel E. Noyola & Andreu Comas-García & Christian A García-Sepúlveda, 2020. "Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics," IJERPH, MDPI, vol. 17(12), pages 1-20, June.
    12. Daniel E. O'Leary & Veda C. Storey, 2020. "A Google–Wikipedia–Twitter Model as a Leading Indicator of the Numbers of Coronavirus Deaths," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(3), pages 151-158, July.
    13. Jun, Seung-Pyo & Park, Do-Hyung, 2016. "Consumer information search behavior and purchasing decisions: Empirical evidence from Korea," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 97-111.
    14. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    15. Martina Halouskov'a & Daniel Stav{s}ek & Mat'uv{s} Horv'ath, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Papers 2205.05985, arXiv.org, revised Aug 2022.
    16. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    17. Jun, Seung-Pyo & Yoo, Hyoung Sun & Lee, Jae-Seong, 2021. "The impact of the pandemic declaration on public awareness and behavior: Focusing on COVID-19 google searches," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    18. Jose Ramon Albert & Arturo Martinez Jr. & Katrina Miradora & Jan Arvin Lapuz & Marymell Martillan & Criselda De Dios & Iva Sebastian-Samaniego, 2019. "Readiness of National Statistical Systems in Asia and the Pacific for Leveraging Big Data to Monitor the SDGs," Working Papers id:13017, eSocialSciences.
    19. Woloszko, Nicolas, 2024. "Nowcasting with panels and alternative data: The OECD weekly tracker," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1302-1335.
    20. Katsikopoulos, Konstantinos V. & Şimşek, Özgür & Buckmann, Marcus & Gigerenzer, Gerd, 2022. "Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?," International Journal of Forecasting, Elsevier, vol. 38(2), pages 613-619.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0157734. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.