IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/yndb7.html
   My bibliography  Save this paper

Digital Pathways, Pandemic Trajectories. Using Google Trends to Track Social Responses to COVID-19

Author

Listed:
  • Beytía, Pablo
  • Infante, Carlos Cruz

Abstract

We already know which countries have controlled the spread of COVID-19 better and what “good practices” have enabled them to do so. Eventually, some of these policies could be replicated in other countries. But it is not enough to make a well-informed public intervention. We also need quickly available indicators of how actively populations are responding to the virus threat because current changes in social behaviour could mean significant differences in the spread of the COVID-19 in two weeks (after the incubation period). In this article, Pablo Beytía Reyes and Carlos Cruz Infante explore the potential of Google Trends to quickly track social responses to the pandemic. In all the countries that have reached a downward changepoint in the COVID-19 contagion, an “information saturation peak” preceded it: people were massively searching for information on the subject over 2 to 5 days, and about a week after the peak of searches was reached, a decline in the growth trend of coronavirus confirmed cases could be observed. Does it make sense to associate a Google search boom with a decrease in transmission trends? The authors propose that the frequency of searches is a quick indicator of 1) people’ concerns on the virus, 2) the development of a more informed citizenry on how to avoid transmission and 3) active social response to the virus spread, which generally lead to a downward change in the contagion trend.

Suggested Citation

  • Beytía, Pablo & Infante, Carlos Cruz, 2020. "Digital Pathways, Pandemic Trajectories. Using Google Trends to Track Social Responses to COVID-19," SocArXiv yndb7, Center for Open Science.
  • Handle: RePEc:osf:socarx:yndb7
    DOI: 10.31219/osf.io/yndb7
    as

    Download full text from publisher

    File URL: https://osf.io/download/5e8dc333d697350194bda664/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/yndb7?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. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    2. Lynn Wu & Erik Brynjolfsson, 2015. "The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 89-118, National Bureau of Economic Research, Inc.
    3. Vanja Dukic & Hedibert F. Lopes & Nicholas G. Polson, 2012. "Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1410-1426, December.
    4. Qinneng Xu & Yulia R Gel & L Leticia Ramirez Ramirez & Kusha Nezafati & Qingpeng Zhang & Kwok-Leung Tsui, 2017. "Forecasting influenza in Hong Kong with Google search queries and statistical model fusion," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
    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. Caselli, Mauro & Fracasso, Andrea & Scicchitano, Sergio, 2020. "From the lockdown to the new normal: An analysis of the limitations to individual mobility in Italy following the Covid-19 crisis," GLO Discussion Paper Series 683, Global Labor Organization (GLO).

    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. Abay,Kibrom A. & Hirfrfot,Kibrom Tafere & Woldemichael,Andinet, 2020. "Winners and Losers from COVID-19 : Global Evidence from Google Search," Policy Research Working Paper Series 9268, The World Bank.
    2. 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.
    3. Ming, Yaxin & Deng, Huixin & Wu, Xiaoyue, 2022. "The negative effect of air pollution on people's pro-environmental behavior," Journal of Business Research, Elsevier, vol. 142(C), pages 72-87.
    4. Christoph Zimmer & Reza Yaesoubi & Ted Cohen, 2017. "A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-21, January.
    5. Petter Arnesen & Odd A. Hjelkrem, 2018. "An Estimator for Traffic Breakdown Probability Based on Classification of Transitional Breakdown Events," Transportation Science, INFORMS, vol. 52(3), pages 593-602, June.
    6. Dehler-Holland, Joris & Schumacher, Kira & Fichtner, Wolf, 2021. "Topic Modeling Uncovers Shifts in Media Framing of the German Renewable Energy Act," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 2(1).
    7. Malte Willmes & Katherine M Ransom & Levi S Lewis & Christian T Denney & Justin J G Glessner & James A Hobbs, 2018. "IsoFishR: An application for reproducible data reduction and analysis of strontium isotope ratios (87Sr/86Sr) obtained via laser-ablation MC-ICP-MS," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-15, September.
    8. Fan Li & Hao Zhou & De-Sheng Huang & Peng Guan, 2020. "Global Research Output and Theme Trends on Climate Change and Infectious Diseases: A Restrospective Bibliometric and Co-Word Biclustering Investigation of Papers Indexed in PubMed (1999–2018)," IJERPH, MDPI, vol. 17(14), pages 1-14, July.
    9. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2022. "Does online salience predict charitable giving? Evidence from SMS text donations," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 134-149.
    10. Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.
    11. Sutirtha Bagchi, 2018. "A Tale of Two Cities: An Examination of Medallion Prices in New York and Chicago," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 53(2), pages 295-319, September.
    12. 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.
    13. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    14. Tasadduq Imam, 2021. "Model selection for one‐day‐ahead AUD/USD, AUD/EUR forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1808-1824, April.
    15. Raputsoane, Leroi, 2018. "Temporal homogeneity between financial stress and the economic cycle," MPRA Paper 91119, University Library of Munich, Germany.
    16. Feng, Jingxue & Wang, Liangliang, 2024. "A switching state-space transmission model for tracking epidemics and assessing interventions," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
    17. Masha Krupenkin & David Rothschild & Shawndra Hill & Elad Yom-Tov, 2019. "President Trump Stress Disorder: Partisanship, Ethnicity, and Expressive Reporting of Mental Distress After the 2016 Election," SAGE Open, , vol. 9(1), pages 21582440198, March.
    18. Hui Zhang & Minna Väliranta & Graeme T. Swindles & Marco A. Aquino-López & Donal Mullan & Ning Tan & Matthew Amesbury & Kirill V. Babeshko & Kunshan Bao & Anatoly Bobrov & Viktor Chernyshov & Marissa , 2022. "Recent climate change has driven divergent hydrological shifts in high-latitude peatlands," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    19. M. Hubert & P. Rousseeuw & K. Vakili, 2014. "Shape bias of robust covariance estimators: an empirical study," Statistical Papers, Springer, vol. 55(1), pages 15-28, February.
    20. Stig Vinther Møller & Thomas Pedersen & Erik Christian Montes Schütte & Allan Timmermann, 2024. "Search and Predictability of Prices in the Housing Market," Management Science, INFORMS, vol. 70(1), pages 415-438, January.

    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:osf:socarx:yndb7. 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: OSF (email available below). General contact details of provider: https://arabixiv.org .

    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.