IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v11y2024i1d10.1057_s41599-024-03589-y.html
   My bibliography  Save this article

Leveraging social media data for pandemic detection and prediction

Author

Listed:
  • Boyang Shi

    (South China University of Technology)

  • Weixiang Huang

    (South China University of Technology)

  • Yuanyuan Dang

    (South China University of Technology)

  • Wenhui Zhou

    (South China University of Technology)

Abstract

Governments and healthcare institutions are increasingly recognizing the value of leveraging social media data to address disease outbreaks. This is due to the rapid dissemination and rich content of social media data, which includes real-time reactions and calls for help from people. However, current research on which social media data can be utilized for information support, as well as the underlying reasons why social media data can be utilized for information support, remains limited. This study aims to make up for the aforementioned limitation by investigating which social media information is more likely to reflect the severity of an outbreak through empirical and prediction models, while also elucidating why social media data has the ability to reflect pandemic through content analysis. The COVID-19 outbreak was utilized as a case example in this study because it has the advantage of enhancing the universality of results and promoting the validation of the model with multiple waves of data. The empirical model results indicate that social media activity from public users is more likely to reflect the ground truth during pandemic. In particular, it was found that negative sentiment expressed in blog posts by public users during pandemic aligns more closely with the severity of disease outbreak. Then, a prediction model was proposed to further validate these findings of the empirical model. Finally, a content analysis was conducted based on the conclusions drawn from empirical model and prediction model. The content analysis revealed that the predictive capability of social media data for pandemic originates from individual self-reporting of illness. This study provides contributions and insights into which types of information can be used for pandemic monitoring and forecasting. The findings of our research have significant implications for governments and healthcare institutions in leveraging social media data for pandemic monitoring and forecasting.

Suggested Citation

  • Boyang Shi & Weixiang Huang & Yuanyuan Dang & Wenhui Zhou, 2024. "Leveraging social media data for pandemic detection and prediction," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03589-y
    DOI: 10.1057/s41599-024-03589-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-024-03589-y
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-024-03589-y?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. Kason Ka Ching Cheung & Ho-Yin Chan & Sibel Erduran, 2023. "Communicating science in the COVID-19 news in the UK during Omicron waves: exploring representations of nature of science with epistemic network analysis," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    2. Simon, Tomer & Goldberg, Avishay & Adini, Bruria, 2015. "Socializing in emergencies—A review of the use of social media in emergency situations," International Journal of Information Management, Elsevier, vol. 35(5), pages 609-619.
    3. Haibing Gao & Subodha Kumar & Yinliang (Ricky) Tan & Huazhong Zhao, 2022. "Socialize More, Pay Less: Randomized Field Experiments on Social Pricing," Information Systems Research, INFORMS, vol. 33(3), pages 935-953, September.
    4. Alekh Gour & Shikha Aggarwal & Subodha Kumar, 2022. "Lending ears to unheard voices: An empirical analysis of user‐generated content on social media," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2457-2476, June.
    5. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    6. Xiangliang Zhang & Qiang Yang & Somayah Albaradei & Xiaoting Lyu & Hind Alamro & Adil Salhi & Changsheng Ma & Manal Alshehri & Inji Ibrahim Jaber & Faroug Tifratene & Wei Wang & Takashi Gojobori & Car, 2021. "Rise and fall of the global conversation and shifting sentiments during the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-10, December.
    7. Liangdong Lu & Jia Xu & Jiuchang Wei & F. LeRon Shults & Xing Lin Feng, 2024. "The role of emotion and social connection during the COVID-19 pandemic phase transitions: a cross-cultural comparison of China and the United States," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-16, December.
    8. 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.
    9. Yongjun Zhang & Hao Lin & Yi Wang & Xinguang Fan, 2023. "Sinophobia was popular in Chinese language communities on Twitter during the early COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    10. Wen Deng & Yi Yang, 2021. "Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo," IJERPH, MDPI, vol. 18(12), pages 1-20, June.
    11. Liangfei Qiu & Subodha Kumar, 2017. "Understanding Voluntary Knowledge Provision and Content Contribution Through a Social-Media-Based Prediction Market: A Field Experiment," Information Systems Research, INFORMS, vol. 28(3), pages 529-546, September.
    12. Emre Cevik & Buket Kirci Altinkeski & Emrah Ismail Cevik & Sel Dibooglu, 2022. "Investor sentiments and stock markets during the COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-34, December.
    13. Sujin Bae & Eunyoung (Christine) Sung & Ohbyung Kwon, 2021. "Accounting for social media effects to improve the accuracy of infection models: combatting the COVID-19 pandemic and infodemic," European Journal of Information Systems, Taylor & Francis Journals, vol. 30(3), pages 342-355, May.
    14. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    15. Joseph Heffner & Marc-Lluís Vives & Oriel FeldmanHall, 2021. "Anxiety, gender, and social media consumption predict COVID-19 emotional distress," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-7, December.
    Full references (including those not matched with items on IDEAS)

    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. Papadamou, Stephanos & Fassas, Athanasios P. & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2023. "Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    2. Panagiotis Papaioannou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Netnomics, Springer, vol. 14(1), pages 47-68, November.
    3. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    4. Schmidt, Christoph G. & Wuttke, David A. & Heese, H. Sebastian & Wagner, Stephan M., 2023. "Antecedents of public reactions to supply chain glitches," International Journal of Production Economics, Elsevier, vol. 259(C).
    5. David H Chae & Sean Clouston & Mark L Hatzenbuehler & Michael R Kramer & Hannah L F Cooper & Sacoby M Wilson & Seth I Stephens-Davidowitz & Robert S Gold & Bruce G Link, 2015. "Association between an Internet-Based Measure of Area Racism and Black Mortality," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    6. Mariana Oliveira & Luís Torgo & Vítor Santos Costa, 2021. "Evaluation Procedures for Forecasting with Spatiotemporal Data," Mathematics, MDPI, vol. 9(6), pages 1-27, March.
    7. Xiaoli Wang & Shuangsheng Wu & C Raina MacIntyre & Hongbin Zhang & Weixian Shi & Xiaomin Peng & Wei Duan & Peng Yang & Yi Zhang & Quanyi Wang, 2015. "Using an Adjusted Serfling Regression Model to Improve the Early Warning at the Arrival of Peak Timing of Influenza in Beijing," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    8. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    9. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    10. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    11. Shen, Yiran & Liu, Chang & Sun, Xiaolei & Guo, Kun, 2023. "Investor sentiment and the Chinese new energy stock market: A risk–return perspective," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 395-408.
    12. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    13. Markowitz, Sara & Nesson, Erik & Robinson, Joshua J., 2019. "The effects of employment on influenza rates," Economics & Human Biology, Elsevier, vol. 34(C), pages 286-295.
    14. Karimi, Majid & Zaerpour, Nima, 2022. "Put your money where your forecast is: Supply chain collaborative forecasting with cost-function-based prediction markets," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1035-1049.
    15. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
    16. Bouteska, Ahmed & Ha, Le Thanh & Bhuiyan, Faruk & Sharif, Taimur & Abedin, Mohammad Zoynul, 2024. "Contagion between investor sentiment and green bonds in China during the global uncertainties," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 469-484.
    17. Jesse T. Richman & Ryan J. Roberts, 2023. "Assessing Spurious Correlations in Big Search Data," Forecasting, MDPI, vol. 5(1), pages 1-12, February.
    18. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    19. 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.
    20. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.

    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:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03589-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    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.