IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v6y2023i2d10.1007_s42001-023-00203-0.html
   My bibliography  Save this article

Perception of COVID-19 vaccination among Indian Twitter users: computational approach

Author

Listed:
  • Prateeksha Dawn Davidson

    (Model Rural Health Research Unit, ICMR)

  • Thanujah Muniandy

    (Meltwater)

  • Dhivya Karmegam

    (SRM Institute of Science and Technology)

Abstract

Vaccination has been a hot topic in the present COVID-19 context. The government, public health stakeholders and media are all concerned about how to get the people vaccinated. The study was intended to explore the perception and emotions of the Indians citizens toward COVID-19 vaccine from Twitter messages. The tweets were collected for the period of 6 months, from mid-January to June, 2021 using hash-tags and keywords specific to India. Topics and emotions from the tweets were extracted using Latent Dirichlet Allocation (LDA) method and National Research Council (NRC) Lexicon, respectively. Theme, sentiment and emotion wise engagement and reachability metrics were assessed. Hash-tag frequency of COVID-19 vaccine brands were also identified and evaluated. Information regarding ‘Co-WIN app and availability of vaccine’ was widely discussed and also received highest engagement and reachability among Twitter users. Among the various emotions, trust was expressed the most, which highlights the acceptance of vaccines among the Indian citizens. The hash-tags frequency of vaccine brands shows that Covishield was popular in the month of March 2021, and Covaxin in April 2021. The results from the study will help stakeholders to efficiently use social media to disseminate COVID-19 vaccine information on popular concerns. This in turn will encourage citizens to be vaccinated and achieve herd immunity. Similar methodology can be adopted in future to understand the perceptions and concerns of people in emergency situations.

Suggested Citation

  • Prateeksha Dawn Davidson & Thanujah Muniandy & Dhivya Karmegam, 2023. "Perception of COVID-19 vaccination among Indian Twitter users: computational approach," Journal of Computational Social Science, Springer, vol. 6(2), pages 541-560, October.
    • Handle: RePEc:spr:jcsosc:v:6:y:2023:i:2:d:10.1007_s42001-023-00203-0
    DOI: 10.1007/s42001-023-00203-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-023-00203-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-023-00203-0?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Ramy Shaaban & Ramy Mohamed Ghazy & Fawzia Elsherif & Nancy Ali & Youssef Yakoub & Maged Osama Aly & Rony ElMakhzangy & Marwa Shawky Abdou & Bonny McKinna & Amira Mohamed Elzorkany & Fatimah Abdullah , 2022. "COVID-19 Vaccine Acceptance among Social Media Users: A Content Analysis, Multi-Continent Study," IJERPH, MDPI, vol. 19(9), pages 1-14, May.
    2. Sinnenberg, L. & Buttenheim, A.M. & Padrez, K. & Mancheno, C. & Ungar, L. & Merchant, R.M., 2017. "Twitter as a tool for health research: A systematic review," American Journal of Public Health, American Public Health Association, vol. 107(1), pages 1-8.
    3. Mahsa Dalili Shoaei & Meisam Dastani, 2020. "The Role of Twitter During the COVID-19 Crisis: A Systematic Literature Review," Acta Informatica Pragensia, Prague University of Economics and Business, vol. 2020(2), pages 154-169.
    4. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    5. repec:aph:ajpbhl:10.2105/ajph.2016.303512_4 is not listed on IDEAS
    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. Thilagavathi Ramamoorthy & Bagavandas Mappillairaju, 2023. "Tweet topics on cancer among Indian Twitter users—computational approach using latent Dirichlet allocation topic modelling," Journal of Computational Social Science, Springer, vol. 6(2), pages 1033-1054, October.
    2. Konstantinos Charmanas & Klairi Filippou & Nikolaos Mittas & Lefteris Angelis, 2025. "Exploring the role of information security news descriptions on retweet proneness and user interactions," Journal of Computational Social Science, Springer, vol. 8(3), pages 1-31, August.
    3. Hussain, Atif & Shafiq, Adnan & Awan, Muhammad Usman & Hashmi, Junaid Iqbal, 2025. "A text mining study of online reviews to understand intercity bus service quality," Transport Policy, Elsevier, vol. 162(C), pages 325-335.

    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. Luis-Millán González & José Devís-Devís & Maite Pellicer-Chenoll & Miquel Pans & Alberto Pardo-Ibañez & Xavier García-Massó & Fernanda Peset & Fernanda Garzón-Farinós & Víctor Pérez-Samaniego, 2021. "The Impact of COVID-19 on Sport in Twitter: A Quantitative and Qualitative Content Analysis," IJERPH, MDPI, vol. 18(9), pages 1-20, April.
    2. Meng Hsiu Tsai & Yingfeng Wang, 2021. "Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19," IJERPH, MDPI, vol. 18(12), pages 1-14, June.
    3. Amir Haghighati & Kamran Sedig, 2020. "VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data," Data, MDPI, vol. 5(1), pages 1-25, February.
    4. Nisar, Sobia & Shafiq, Muhammad, 2019. "Framework for efficient utilisation of social media in Pakistan's healthcare sector," Technology in Society, Elsevier, vol. 56(C), pages 31-43.
    5. Pablo Madriaza & Ghayda Hassan & Sébastien Brouillette‐Alarie & Aoudou Njingouo Mounchingam & Loïc Durocher‐Corfa & Eugene Borokhovski & David Pickup & Sabrina Paillé, 2025. "Exposure to hate in online and traditional media: A systematic review and meta‐analysis of the impact of this exposure on individuals and communities," Campbell Systematic Reviews, John Wiley & Sons, vol. 21(1), March.
    6. Deborah Kanda & Jingjing Yin & Xinyan Zhang & Hani Samawi, 2025. "Efficient regression analyses with zero-augmented models based on ranking," Computational Statistics, Springer, vol. 40(2), pages 601-632, February.
    7. Han, Chunjia & Yang, Mu & Piterou, Athena, 2021. "Do news media and citizens have the same agenda on COVID-19? an empirical comparison of twitter posts," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    8. Anni Arumsari Fitriany & Piotr J. Flatau & Khoirunurrofik Khoirunurrofik & Nelly Florida Riama, 2021. "Assessment on the Use of Meteorological and Social Media Information for Forest Fire Detection and Prediction in Riau, Indonesia," Sustainability, MDPI, vol. 13(20), pages 1-13, October.
    9. Guangyu Hu & Xueyan Han & Huixuan Zhou & Yuanli Liu, 2019. "Public Perception on Healthcare Services: Evidence from Social Media Platforms in China," IJERPH, MDPI, vol. 16(7), pages 1-10, April.
    10. Umar Ali Bukar & Fatimah Sidi & Marzanah A. Jabar & Rozi Nor Haizan Nor & Salfarina Abdullah & Iskandar Ishak & Mustafa Alabadla & Ali Alkhalifah, 2022. "How Advanced Technological Approaches Are Reshaping Sustainable Social Media Crisis Management and Communication: A Systematic Review," Sustainability, MDPI, vol. 14(10), pages 1-26, May.
    11. Boonyanit Mathayomchan & Viriya Taecharungroj & Walanchalee Wattanacharoensil, 2023. "Evolution of COVID-19 tweets about Southeast Asian Countries: topic modelling and sentiment analyses," Place Branding and Public Diplomacy, Palgrave Macmillan, vol. 19(3), pages 317-334, September.
    12. Elanor Colleoni & Nuccio Ludovico & Illia Laura & Ravindran Kiron, 2021. "Does Sharing Economy Have a Moral Capital? Comparing Semantic Networks in Social Media and News Media," Journal of Management and Sustainability, Canadian Center of Science and Education, vol. 11(2), pages 1-1, December.
    13. Nanath, Krishnadas & Balasubramanian, Sreejith & Shukla, Vinaya & Islam, Nazrul & Kaitheri, Supriya, 2022. "Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    14. Cindy Cheng & Joan Barceló & Allison Spencer Hartnett & Robert Kubinec & Luca Messerschmidt, 2020. "COVID-19 Government Response Event Dataset (CoronaNet v.1.0)," Nature Human Behaviour, Nature, vol. 4(7), pages 756-768, July.
    15. Bruce Forrester, 2020. "Authentic chatter," Computational and Mathematical Organization Theory, Springer, vol. 26(4), pages 382-411, December.
    16. Greyling, Talita & Rossouw, Stephanie & Adhikari, Tamanna, 2020. "Happiness-lost: Did Governments make the right decisions to combat Covid-19?," GLO Discussion Paper Series 556, Global Labor Organization (GLO).
    17. Hongzhou Shen & Yue Ju & Zhijing Zhu, 2023. "Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    18. Gaspar, Rui & Yan, Zheng & Domingos, Samuel, 2019. "Extreme natural and man-made events and human adaptive responses mediated by information and communication technologies' use: A systematic literature review," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 125-135.
    19. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    20. repec:plo:pone00:0242550 is not listed on IDEAS
    21. Jiayin Pei & Guang Yu & Xianyun Tian & Maureen Renee Donnelley, 2017. "A new method for early detection of mass concern about public health issues," Journal of Risk Research, Taylor & Francis Journals, vol. 20(4), pages 516-532, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:jcsosc:v:6:y:2023:i:2:d:10.1007_s42001-023-00203-0. 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: http://www.springer.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.