IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v280y2021ics0277953621003890.html
   My bibliography  Save this article

Stuck in the past or living in the present? Temporal focus and the spread of COVID-19

Author

Listed:
  • Barnes, Stuart J.

Abstract

Research has shown that the temporal focus of individuals can have a real effect on behavior. In the context of the COVID-19 pandemic, this study posits that temporal focus will affect adherence behavior regarding health control measures, such as social distancing, hand washing and mask wearing, which will be manifested through the degree of spread of COVID-19. It is suggested that social media can provide an indicator of the general temporal focus of the population at a particular time. In this study, we examine the temporal focus of Twitter text data and the number of COVID-19 cases in the US over a 317-day period from the inception of the pandemic, using text analytics to classify the temporal content of 0.76 million tweets. The data is then analyzed using dynamic regression via advanced ARIMA modelling, differencing the data, removing weekly seasonality and creating a stationary time series. The result of the dynamic regression finds that past orientation does indeed have an effect on the growth of COVID-19 cases in the US. However, a present focus tends to reduce the spread of COVID cases. Future focus had no effect in the model. Overall, the research suggests that detecting and managing temporal focus could be an important tool in managing public health during a pandemic.

Suggested Citation

  • Barnes, Stuart J., 2021. "Stuck in the past or living in the present? Temporal focus and the spread of COVID-19," Social Science & Medicine, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:socmed:v:280:y:2021:i:c:s0277953621003890
    DOI: 10.1016/j.socscimed.2021.114057
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953621003890
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.socscimed.2021.114057?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Maciej Stolarski & Gerald Matthews & Sławomir Postek & Philip Zimbardo & Joanna Bitner, 2014. "How We Feel is a Matter of Time: Relationships Between Time Perspectives and Mood," Journal of Happiness Studies, Springer, vol. 15(4), pages 809-827, August.
    2. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    3. Shipp, Abbie J. & Edwards, Jeffrey R. & Lambert, Lisa Schurer, 2009. "Conceptualization and measurement of temporal focus: The subjective experience of the past, present, and future," Organizational Behavior and Human Decision Processes, Elsevier, vol. 110(1), pages 1-22, September.
    4. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    5. Sobol, Małgorzata & Blachnio, Agata & Przepiórka, Aneta, 2020. "Time of pandemic: Temporal perspectives related to compliance with public health regulations concerning the COVID-19 pandemic," Social Science & Medicine, Elsevier, vol. 265(C).
    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. Staupe-Delgado, Reidar & Rubin, Olivier, 2022. "Living through and with the global HIV/AIDS pandemic: Distinct ‘pandemic practices’ and temporalities," Social Science & Medicine, Elsevier, vol. 296(C).

    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. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    4. Shafiqah Azman & Dharini Pathmanathan & Aerambamoorthy Thavaneswaran, 2022. "Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter," Mathematics, MDPI, vol. 10(17), pages 1-15, September.
    5. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    6. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844.
    7. Han Lin Shang, 2017. "Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 36(1), pages 55-84, February.
    8. Leonardo Di Gangi & M. Lapucci & F. Schoen & A. Sortino, 2019. "An efficient optimization approach for best subset selection in linear regression, with application to model selection and fitting in autoregressive time-series," Computational Optimization and Applications, Springer, vol. 74(3), pages 919-948, December.
    9. Ghufran Ahmad & Furqan Ahmed & Muhammad Suhail Rizwan & Javed Muhammad & Syeda Hira Fatima & Aamer Ikram & Hajo Zeeb, 2021. "Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    10. Lange, Steffen & Pütz, Peter & Kopp, Thomas, 2018. "Do Mature Economies Grow Exponentially?," Ecological Economics, Elsevier, vol. 147(C), pages 123-133.
    11. Rice, William L. & Park, So Young & Pan, Bing & Newman, Peter, 2019. "Forecasting campground demand in US national parks," Annals of Tourism Research, Elsevier, vol. 75(C), pages 424-438.
    12. Saša Obradoviæ & Lela Ristiæ & Nemanja Lojanica, 2018. "Are unemployment rates stationary for SEE10 countries? Evidence from linear and nonlinear dynamics," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 36(2), pages 559-583.
    13. Schipfer, Fabian & Kranzl, Lukas & Olsson, Olle & Lamers, Patrick, 2020. "The European wood pellets for heating market - Price developments, trade and market efficiency," Energy, Elsevier, vol. 212(C).
    14. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.
    15. Julia Wyszomirska & Monika Bąk-Sosnowska, 2023. "Time Goes Back—Time Perspective in Polish Men with Compulsive Sexual Behavior Disorder and Risky Sexual Behavior," IJERPH, MDPI, vol. 20(6), pages 1-21, March.
    16. Malliga Subramanian & Jaehyuk Cho & Sathishkumar Veerappampalayam Easwaramoorthy & Akash Murugesan & Ramya Chinnasamy, 2023. "Enhancing Sustainable Transportation: AI-Driven Bike Demand Forecasting in Smart Cities," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
    17. Bernadett Aradi & G'abor Petneh'azi & J'ozsef G'all, 2020. "Volatility Forecasting with 1-dimensional CNNs via transfer learning," Papers 2009.05508, arXiv.org.
    18. Felix Divo & Eric Endress & Kevin Endler & Kristian Kersting & Devendra Singh Dhami, 2024. "Forecasting Company Fundamentals," Papers 2411.05791, arXiv.org.
    19. Gaetano Perone, 2022. "Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries," Econometrics, MDPI, vol. 10(2), pages 1-23, April.
    20. Fröhlich Markus, 2018. "Nowcasting Austrian Short Term Statistics," Journal of Official Statistics, Sciendo, vol. 34(2), pages 503-522, June.

    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:eee:socmed:v:280:y:2021:i:c:s0277953621003890. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    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.