Skip to main content

Digital Information Seeking and Sharing Behaviour During the COVID-19 Pandemic in Pakistan

  • Conference paper
  • First Online:
  • 617 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13545 ))

Abstract

Studies on digital interaction in emergent users’ population are rare. We analyse the electronic data generated by users from Pakistan on Google Search Engine and WhatsApp to understand their information-seeking behaviour during the first wave of the Covid-19 pandemic. We study how the Pakistani public developed their understanding about the disease, (its origin, cures, and preventive measures to name a few) through digital media. Understanding this information seeking behaviour will allow corrective actions to be taken by health policymakers to better inform the public in future health crises through electronic media, as well as the digital media platforms and search engines to address misinformation among the users in the emergent markets.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://p.dw.com/p/3eeAj.

References

  1. Coronavirus attitude tracker survey report - wave 8 (2020). https://gallup.com.pk/wp/wp-content/uploads/2020/10/Gallup-Pakistan-Coronavirus-Attitude-Tracker-Survey-Wave-8-.pdf

  2. NCC rules out comlete lockdown (2020). https://www.dawn.com/news/1588511. Accessed 11 Nov 2021

  3. Second wave (2020). https://www.dawn.com/news/1583507/second-wave. Accessed 11 Nov 2021

  4. Ayyoubzadeh, S.M., Ayyoubzadeh, S.M., Zahedi, H., Ahmadi, M., Kalhori, S.R.N.: Predicting COVID-19 incidence through analysis of google trends data in Iran: data mining and deep learning pilot study. JMIR Public Health Surveill. 6(2), e18828 (2020)

    Article  Google Scholar 

  5. Badell-Grau, R.A., Cuff, J.P., Kelly, B.P., Waller-Evans, H., Lloyd-Evans, E.: Investigating the prevalence of reactive online searching in the COVID-19 pandemic: infoveillance study. J. Med. Internet Res. 22(10), e19791 (2020)

    Article  Google Scholar 

  6. Bento, A.I., Nguyen, T., Wing, C., Lozano-Rojas, F., Ahn, Y.Y., Simon, K.: Evidence from internet search data shows information-seeking responses to news of local COVID-19 cases. Proc. Natl. Acad. Sci. 117(21), 11220–11222 (2020)

    Article  Google Scholar 

  7. Bilal, A., Rextin, A., Kakakhel, A., Nasim, M.: Analyzing emergent users’ text messages data and exploring its benefits. IEEE Access 7, 2870–2879 (2018)

    Article  Google Scholar 

  8. Blandizzi, C., Scarpignato, C.: Gastrointestinal drugs. In: Side Effects of Drugs Annual, vol. 33, pp. 741–767. Elsevier (2011)

    Google Scholar 

  9. Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006)

    Article  Google Scholar 

  10. Chen, L., Wang, X., Peng, T.Q.: Nature and diffusion of gynecologic cancer-related misinformation on social media: analysis of tweets. J. Med. Internet Res. 20(10), e11515 (2018)

    Article  Google Scholar 

  11. Chou, W.Y.S., Oh, A., Klein, W.M.: Addressing health-related misinformation on social media. JAMA 320(23), 2417–2418 (2018)

    Article  Google Scholar 

  12. Datta, S.S., et al.: Progress and challenges in measles and rubella elimination in the who European region. Vaccine 36(36), 5408–5415 (2018)

    Article  Google Scholar 

  13. Davis, M.: Habituation and sensitization of a startle-like response elicited by electrical stimulation at different points in the acoustic startle circuit. In: Sensory Functions, pp. 67–78. Elsevier (1981)

    Google Scholar 

  14. Denworth, L.: Overcoming psychological biases is the best treatment against COVID-19 yet (2020). https://www.scientificamerican.com/article/overcoming-psychological-biases-is-the-best-treatment-against-covid-19-yet/. Accessed 11 Nov 2021

  15. Depoux, A., Martin, S., Karafillakis, E., Preet, R., Wilder-Smith, A., Larson, H.: The pandemic of social media panic travels faster than the COVID-19 outbreak (2020)

    Google Scholar 

  16. Dewsbury, D.A.: Effects of novelty of copulatory behavior: the coolidge effect and related phenomena. Psychol. Bull. 89(3), 464 (1981)

    Article  Google Scholar 

  17. Filia, A., Bella, A., Del Manso, M., Baggieri, M., Magurano, F., Rota, M.C.: Ongoing outbreak with well over 4,000 measles cases in Italy from January to end August 2017 - what is making elimination so difficult? Eurosurveillance 22(37), 30614 (2017)

    Article  Google Scholar 

  18. Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012–1014 (2009)

    Article  Google Scholar 

  19. Gluck, M.A., Mercado, E., Myers, C.E.: Learning and Memory: From Brain to Behavior. Worth Publishers, New York (2008)

    Google Scholar 

  20. Gupta, L., Gasparyan, A.Y., Misra, D.P., Agarwal, V., Zimba, O., Yessirkepov, M.: Information and misinformation on COVID-19: a cross-sectional survey study. J. Korean Med. Sci. 35(27) (2020)

    Google Scholar 

  21. Hernández-García, I., Giménez-Júlvez, T.: Assessment of health information about COVID-19 prevention on the internet: infodemiological study. JMIR Public Health Surveill. 6(2), e18717 (2020)

    Article  Google Scholar 

  22. Hu, D., et al.: More effective strategies are required to strengthen public awareness of COVID-19: evidence from google trends. J. Global Health 10(1) (2020)

    Google Scholar 

  23. Husnayain, A., Fuad, A., Su, E.C.Y.: Applications of google search trends for risk communication in infectious disease management: a case study of COVID-19 outbreak in Taiwan. Int. J. Infect. Dis. 95, 221–223 (2020)

    Article  Google Scholar 

  24. Joshi, A.: Technology adoption by ‘emergent’ users: the user-usage model. In: Proceedings of the 11th Asia Pacific Conference on Computer Human Interaction, pp. 28–38 (2013)

    Google Scholar 

  25. Kim, K.D., Hossain, L.: Towards early detection of influenza epidemics by using social media analytics. In: DSS, pp. 36–41 (2014)

    Google Scholar 

  26. Kurian, S.J., et al.: Correlations between COVID-19 cases and google trends data in the united states: a state-by-state analysis. In: Mayo Clinic Proceedings, pp. 2370–2381. Elsevier (2020)

    Google Scholar 

  27. Kušen, E., Strembeck, M.: Politics, sentiments, and misinformation: an analysis of the twitter discussion on the 2016 Austrian presidential elections. Online Soc. Netw. Media 5, 37–50 (2018)

    Article  Google Scholar 

  28. Liu, M., Caputi, T.L., Dredze, M., Kesselheim, A.S., Ayers, J.W.: Internet searches for unproven COVID-19 therapies in the United States. JAMA Internal Med. 180(8), 1116–1118 (2020)

    Article  Google Scholar 

  29. Malani, A.N., Sherbeck, J.P., Malani, P.N.: Convalescent plasma and COVID-19. JAMA 324(5), 524 (2020)

    Article  Google Scholar 

  30. Malcuit, G., Bastien, C., Pomerleau, A.: Habituation of the orienting response to stimuli of different functional values in 4-month-old infants. J. Exp. Child Psychol. 62(2), 272–291 (1996)

    Article  Google Scholar 

  31. Moyer, M.W.: People drawn to conspiracy theories share a cluster of psychological features (2019). https://www.scientificamerican.com/article/people-drawn-to-conspiracy-theories-share-a-cluster-of-psychological-features/. Accessed 11 Nov 2021

  32. Pennycook, G., McPhetres, J., Zhang, Y., Lu, J.G., Rand, D.G.: Fighting COVID-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychol. Sci. 31(7), 770–780 (2020)

    Article  Google Scholar 

  33. Polgreen, P.M., Chen, Y., Pennock, D.M., Nelson, F.D., Weinstein, R.A.: Using internet searches for influenza surveillance. Clin. Infect. Dis. 47(11), 1443–1448 (2008)

    Article  Google Scholar 

  34. Post, S., Bienzeisler, N., Lohöfener, M.: A desire for authoritative science? How citizens’ informational needs and epistemic beliefs shaped their views of science, news, and policymaking in the COVID-19 pandemic. Public Underst. Sci. 30(5), 496–514 (2021). https://doi.org/10.1177/09636625211005334

  35. Rathore, F.A., Farooq, F.: Information overload and infodemic in the COVID-19 pandemic. JPMA J. Pak. Med. Assoc. 70(5), S162–S165 (2020)

    Google Scholar 

  36. Rovetta, A., Bhagavathula, A.S.: COVID-19-related web search behaviors and infodemic attitudes in Italy: infodemiological study. JMIR Public Health Surveill. 6(2), e19374 (2020)

    Article  Google Scholar 

  37. Shah, M.: The failure of public health messaging about COVID-19 (2020. https://www.scientificamerican.com/article/the-failure-of-public-health-messaging-about-covid-19/. Accessed 11 Nov 2021

  38. Sharma, K., Seo, S., Meng, C., Rambhatla, S., Dua, A., Liu, Y.: Coronavirus on social media: analyzing misinformation in twitter conversations. arXiv preprint arXiv:2003.12309 (2020)

  39. Teng, Y., et al.: Dynamic forecasting of zika epidemics using google trends. PLoS ONE 12(1), e0165085 (2017)

    Article  Google Scholar 

  40. Thapen, N., Simmie, D., Hankin, C., Gillard, J.: Defender: detecting and forecasting epidemics using novel data-analytics for enhanced response. PLoS ONE 11(5), e0155417 (2016)

    Article  Google Scholar 

  41. Thompson, R.F., Spencer, W.A.: Habituation: a model phenomenon for the study of neuronal substrates of behavior. Psychol. Rev. 73(1), 16 (1966)

    Article  Google Scholar 

  42. Walker, A., Hopkins, C., Surda, P.: The use of google trends to investigate the loss of smell related searches during COVID-19 outbreak. In: International Forum of Allergy & Rhinology. Wiley Online Library (2020)

    Google Scholar 

  43. Wang, C., et al.: Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in china. Int. J. Environ. Res. Public Health 17(5), 1729 (2020)

    Article  Google Scholar 

  44. Waszak, P.M., Kasprzycka-Waszak, W., Kubanek, A.: The spread of medical fake news in social media-the pilot quantitative study. Health Policy Technol. 7(2), 115–118 (2018)

    Article  Google Scholar 

  45. Wolf, M.S., et al.: Awareness, attitudes, and actions related to COVID-19 among adults with chronic conditions at the onset of the us outbreak: a cross-sectional survey. Ann. Internal Med. 173(2), 100–109 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mehwish Nasim .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fatima, M., Rextin, A., Nasim, M., Yusuf, O. (2022). Digital Information Seeking and Sharing Behaviour During the COVID-19 Pandemic in Pakistan. In: Spezzano, F., Amaral, A., Ceolin, D., Fazio, L., Serra, E. (eds) Disinformation in Open Online Media. MISDOOM 2022. Lecture Notes in Computer Science, vol 13545 . Springer, Cham. https://doi.org/10.1007/978-3-031-18253-2_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-18253-2_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18252-5

  • Online ISBN: 978-3-031-18253-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics