IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v72y2023ics0160791x23000039.html
   My bibliography  Save this article

From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes

Author

Listed:
  • Polyzos, Efstathios
  • Fotiadis, Anestis
  • Huan, Tzung-Cheng

Abstract

This paper examines the effects of online campaigns celebrating frontline workers on COVID-19 outcomes regarding new cases, deaths, and vaccinations, using the United Kingdom as a case study. We implement text and sentiment analysis on Twitter data and feed the result into random regression forests and cointegration analysis. Our combined machine learning and econometric approach shows very weak effects of both the volume and the sentiment of Twitter discussions on new cases, deaths, and vaccinations. On the other hand, established relationships (such as between stringency measures and cases/deaths and between vaccinations and deaths) are confirmed. On the contrary, we find adverse lagged effects from negative sentiment to vaccinations and from new cases to negative sentiment posts. As we assess the knowledge acquired from the COVID-19 crisis, our findings can be used by policy makers, particularly in public health, and prepare for the next pandemic.

Suggested Citation

  • Polyzos, Efstathios & Fotiadis, Anestis & Huan, Tzung-Cheng, 2023. "From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes," Technology in Society, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:teinso:v:72:y:2023:i:c:s0160791x23000039
    DOI: 10.1016/j.techsoc.2023.102198
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X23000039
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techsoc.2023.102198?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. Fotiadis, Anestis K. & Stylos, Nikolaos, 2017. "The effects of online social networking on retail consumer dynamics in the attractions industry: The case of ‘E-da’ theme park, Taiwan," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 283-294.
    2. Thomas Hale & Noam Angrist & Rafael Goldszmidt & Beatriz Kira & Anna Petherick & Toby Phillips & Samuel Webster & Emily Cameron-Blake & Laura Hallas & Saptarshi Majumdar & Helen Tatlow, 2021. "A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)," Nature Human Behaviour, Nature, vol. 5(4), pages 529-538, April.
    3. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    4. Fulian Yin & Meiqi Ji & Zhongliang Yang & Zhaoliang Wu & Xinyu Xia & Tongtong Xing & Yuwei She & Zhiwen Hu, 2022. "Exploring the determinants of global vaccination campaigns to combat COVID-19," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-13, December.
    5. Polyzos, Efstathios & Wang, Fang, 2022. "Twitter and market efficiency in energy markets: Evidence using LDA clustered topic extraction," Energy Economics, Elsevier, vol. 114(C).
    6. Trivedi, Shrawan Kumar, 2020. "A study on credit scoring modeling with different feature selection and machine learning approaches," Technology in Society, Elsevier, vol. 63(C).
    7. Jung, Sang Hoon & Jeong, Yong Jin, 2020. "Twitter data analytical methodology development for prediction of start-up firms’ social media marketing level," Technology in Society, Elsevier, vol. 63(C).
    8. Liu, Hongfei & Liu, Wentong & Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2021. "COVID-19 information overload and generation Z's social media discontinuance intention during the pandemic lockdown," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Lian, Ying & Dong, Xuefan, 2021. "Exploring social media usage in improving public perception on workplace violence against healthcare workers," Technology in Society, Elsevier, vol. 65(C).
    10. Francisco Arroyo-Marioli & Francisco Bullano & Simas Kucinskas & Carlos Rondón-Moreno, 2021. "Tracking R of COVID-19: A new real-time estimation using the Kalman filter," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-16, January.
    11. Laor, Tal, 2022. "My social network: Group differences in frequency of use, active use, and interactive use on Facebook, Instagram and Twitter," Technology in Society, Elsevier, vol. 68(C).
    12. Edouard Mathieu & Hannah Ritchie & Esteban Ortiz-Ospina & Max Roser & Joe Hasell & Cameron Appel & Charlie Giattino & Lucas Rodés-Guirao, 2021. "A global database of COVID-19 vaccinations," Nature Human Behaviour, Nature, vol. 5(7), pages 947-953, July.
    13. Dong, Xuefan & Lian, Ying, 2021. "A review of social media-based public opinion analyses: Challenges and recommendations," Technology in Society, Elsevier, vol. 67(C).
    14. Xing, Yunfei & Wang, Xiwei & Qiu, Chengcheng & Li, Yueqi & He, Wu, 2022. "Research on opinion polarization by big data analytics capabilities in online social networks," Technology in Society, Elsevier, vol. 68(C).
    15. Phillips, Peter C B & Ouliaris, S, 1990. "Asymptotic Properties of Residual Based Tests for Cointegration," Econometrica, Econometric Society, vol. 58(1), pages 165-193, January.
    16. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    17. Siyam, Nur & Alqaryouti, Omar & Abdallah, Sherief, 2020. "Mining government tweets to identify and predict citizens engagement," Technology in Society, Elsevier, vol. 60(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. He, Yu & Lu, Shanglin & Wei, Ran & Wang, Shixuan, 2024. "Local media sentiment towards pollution and its effect on corporate green innovation," International Review of Financial Analysis, Elsevier, vol. 94(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. Jain, Lokesh, 2022. "An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders," Technology in Society, Elsevier, vol. 70(C).
    2. Pelin ÖGE GÜNEY, 2013. "The Term Structure of Interest Rates: A Cointegration Analysis in the Non-Linear STAR Framework," Journal of Economics and Behavioral Studies, AMH International, vol. 5(12), pages 851-860.
    3. repec:ebl:ecbull:v:7:y:2006:i:4:p:1-7 is not listed on IDEAS
    4. Jeremy B. Rudd & Karl Whelan, 2002. "A note on the cointegration of consumption, income, and wealth," Finance and Economics Discussion Series 2002-53, Board of Governors of the Federal Reserve System (U.S.).
    5. Alfred A. Haug, 2002. "Temporal Aggregation and the Power of Cointegration Tests: a Monte Carlo Study," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 399-412, September.
    6. Campos, Julia & Ericsson, Neil R. & Hendry, David F., 1996. "Cointegration tests in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 70(1), pages 187-220, January.
    7. Norah Al-Ballaa, 2005. "Test for cointegration based on two-stage least squares," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(7), pages 707-713.
    8. Neeraj, & Panigrahi, Prasanta K., 2017. "Causality and correlations between BSE and NYSE indexes: A Janus faced relationship," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 284-313.
    9. Ricardo M. Sousa, 2011. "Wealth, Labour Income, Stock Returns and Government Bond Yields, and Financial Stress in the Euro Area," NIPE Working Papers 22/2011, NIPE - Universidade do Minho.
    10. Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios & James Mitchell & Aristeidis Raftapostolos, 2023. "Deep Neural Network Estimation in Panel Data Models," Working Papers 23-15, Federal Reserve Bank of Cleveland.
    11. Kollmann, Robert, 1993. "Comment on 'A Forecasting Equation for the Canada-US Dollar Real Exchange Rate'," MPRA Paper 69893, University Library of Munich, Germany.
    12. Ekong, Christopher N. & Onye, Kenneth U., 2013. "The Failure of the Monetary Exchange Rate Model for the Naira-Dollar," MPRA Paper 88238, University Library of Munich, Germany.
    13. Kuruppuarachchi, Duminda & Lin, Hai & Premachandra, I.M., 2019. "Testing commodity futures market efficiency under time-varying risk premiums and heteroscedastic prices," Economic Modelling, Elsevier, vol. 77(C), pages 92-112.
    14. Lucey, Brian M. & Voronkova, Svitlana, 2005. "Russian equity market linkages before and after the 1998 crisis : evidence from time-varying and stochastic cointegration tests," BOFIT Discussion Papers 12/2005, Bank of Finland, Institute for Economies in Transition.
    15. Felmingham, B. & Zhang, Q., 2000. "The Long Run Demand for Broad Money in Australia Subject to Regime Shifts," Papers 2000-07, Tasmania - Department of Economics.
    16. Gordon de Brouwer & Irene Ng & Robert Subbaraman, 1993. "The Demand for Money in Australia: New Tests on an Old Topic," RBA Research Discussion Papers rdp9314, Reserve Bank of Australia.
    17. Franco, G.C. & Reisen, V.A. & Alves, F.A., 2013. "Bootstrap tests for fractional integration and cointegration: A comparison study," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 87(C), pages 19-29.
    18. repec:bny:wpaper:0126 is not listed on IDEAS
    19. Álvarez-Díaz, Marcos & González-Gómez, Manuel & Otero-Giráldez, María Soledad, 2018. "Main determinants of export-oriented bleached eucalyptus kraft pulp (BEKP) demand from the north-western regions of Spain," Forest Policy and Economics, Elsevier, vol. 96(C), pages 112-119.
    20. Shahbaz, Muhammad & Khraief, Naceur & Jemaa, Mohamed Mekki Ben, 2015. "On the causal nexus of road transport CO2 emissions and macroeconomic variables in Tunisia: Evidence from combined cointegration tests," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 89-100.
    21. Bibhuti Ranjan Mishra & Asit Mohanty, 2017. "An Empirical Analysis of Aggregate Import Demand Function for India," Global Economy Journal (GEJ), World Scientific Publishing Co. Pte. Ltd., vol. 17(4), pages 1-12, December.
    22. Emily Cameron-Blake & Helen Tatlow & Bernardo Andretti & Thomas Boby & Kaitlyn Green & Thomas Hale & Anna Petherick & Toby Phillips & Annalena Pott & Adam Wade & Hao Zha, 2023. "A panel dataset of COVID-19 vaccination policies in 185 countries," Nature Human Behaviour, Nature, vol. 7(8), pages 1402-1413, August.

    More about this item

    Keywords

    Frontliner; Twitter; Sentiment analysis; Random regression forests; Cointegration;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • H12 - Public Economics - - Structure and Scope of Government - - - Crisis Management

    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:eee:teinso:v:72:y:2023:i:c:s0160791x23000039. 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: https://www.journals.elsevier.com/technology-in-society .

    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.