IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i17p3199-d906642.html
   My bibliography  Save this article

Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends

Author

Listed:
  • Wajdi Aljedaani

    (Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA)

  • Eysha Saad

    (Faculty of Computer Science and Information Technology, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan)

  • Furqan Rustam

    (Department of Software Engineering, School of Systems and Technology, University of Management and Technology Lahore, Lahore 54770, Pakistan)

  • Isabel de la Torre Díez

    (Department of Signal Theory and Communications and Telematic Engineering, Unviersity of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain)

  • Imran Ashraf

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea)

Abstract

Pandemics and infectious diseases are overcome by vaccination, which serves as a preventative measure. Nevertheless, vaccines also raise public concerns; public apprehension and doubts challenge the acceptance of new vaccines. COVID-19 vaccines received a similarly hostile reaction from the public. In addition, misinformation from social media, contradictory comments from medical experts, and reports of worse reactions led to negative COVID-19 vaccine perceptions. Many researchers analyzed people’s varying sentiments regarding the COVID-19 vaccine using artificial intelligence (AI) approaches. This study is the first attempt to review the role of AI approaches in COVID-19 vaccination-related sentiment analysis. For this purpose, insights from publications are gathered that analyze the (a) approaches used to develop sentiment analysis tools, (b) major sources of data, (c) available data sources, and (d) the public perception of COVID-19 vaccine. Analysis suggests that public perception-related COVID-19 tweets are predominantly analyzed using TextBlob. Moreover, to a large extent, researchers have employed the Latent Dirichlet Allocation model for topic modeling of Twitter data. Another pertinent discovery made in our study is the variation in people’s sentiments regarding the COVID-19 vaccine across different regions. We anticipate that our systematic review will serve as an all-in-one source for the research community in determining the right technique and data source for their requirements. Our findings also provide insight into the research community to assist them in their future work in the current domain.

Suggested Citation

  • Wajdi Aljedaani & Eysha Saad & Furqan Rustam & Isabel de la Torre Díez & Imran Ashraf, 2022. "Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends," Mathematics, MDPI, vol. 10(17), pages 1-33, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3199-:d:906642
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/17/3199/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/17/3199/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Quyen G. To & Kien G. To & Van-Anh N. Huynh & Nhung T. Q. Nguyen & Diep T. N. Ngo & Stephanie J. Alley & Anh N. Q. Tran & Anh N. P. Tran & Ngan T. T. Pham & Thanh X. Bui & Corneel Vandelanotte, 2021. "Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(8), pages 1-9, April.
    2. Onnela, Jukka-Pekka & Landon, Bruce E. & Kahn, Anna-Lea & Ahmed, Danish & Verma, Harish & O'Malley, A. James & Bahl, Sunil & Sutter, Roland W. & Christakis, Nicholas A., 2016. "Polio vaccine hesitancy in the networks and neighborhoods of Malegaon, India," Social Science & Medicine, Elsevier, vol. 153(C), pages 99-106.
    3. Shalak Mendon & Pankaj Dutta & Abhishek Behl & Stefan Lessmann, 2021. "A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters," Information Systems Frontiers, Springer, vol. 23(5), pages 1145-1168, September.
    4. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    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. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    2. Marcel Lucas Chee & Marcus Eng Hock Ong & Fahad Javaid Siddiqui & Zhongheng Zhang & Shir Lynn Lim & Andrew Fu Wah Ho & Nan Liu, 2021. "Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-15, April.
    3. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    4. Miftahul Qorib & Timothy Oladunni & Max Denis & Esther Ososanya & Paul Cotae, 2023. "COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID," IJERPH, MDPI, vol. 20(10), pages 1-25, May.
    5. Sini V. Pillai & Ranjith S. Kumar, 2021. "The role of data-driven artificial intelligence on COVID-19 disease management in public sphere: a review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 48(4), pages 375-389, December.
    6. Faizeh Hatami & Shi Chen & Rajib Paul & Jean-Claude Thill, 2022. "Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
    7. Wang, Lingxiao & Hare, Brian M. & Zhou, Kai & Stöcker, Horst & Scholten, Olaf, 2023. "Identifying lightning structures via machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    8. Abhinav Kumar & Jyoti Prakash Singh & Nripendra P. Rana & Yogesh K. Dwivedi, 2023. "Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster," Information Systems Frontiers, Springer, vol. 25(4), pages 1589-1604, August.
    9. Mohammad Reza Davahli & Krzysztof Fiok & Waldemar Karwowski & Awad M. Aljuaid & Redha Taiar, 2021. "Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks," IJERPH, MDPI, vol. 18(7), pages 1-12, April.
    10. Manuel Sánchez-Montañés & Pablo Rodríguez-Belenguer & Antonio J. Serrano-López & Emilio Soria-Olivas & Yasser Alakhdar-Mohmara, 2020. "Machine Learning for Mortality Analysis in Patients with COVID-19," IJERPH, MDPI, vol. 17(22), pages 1-20, November.
    11. Ehab M. Almetwally, 2022. "The Odd Weibull Inverse Topp–Leone Distribution with Applications to COVID-19 Data," Annals of Data Science, Springer, vol. 9(1), pages 121-140, February.
    12. Mario A Quiroz-Juárez & Armando Torres-Gómez & Irma Hoyo-Ulloa & Roberto de J León-Montiel & Alfred B U’Ren, 2021. "Identification of high-risk COVID-19 patients using machine learning," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-21, September.
    13. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2023. "Which COVID-19 information really impacts stock markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    14. Anil Babu Payedimarri & Diego Concina & Luigi Portinale & Massimo Canonico & Deborah Seys & Kris Vanhaecht & Massimiliano Panella, 2021. "Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-11, April.
    15. Paras Bhatt & Naga Vemprala & Rohit Valecha & Govind Hariharan & H. Raghav Rao, 2023. "User Privacy, Surveillance and Public Health during COVID-19 – An Examination of Twitterverse," Information Systems Frontiers, Springer, vol. 25(5), pages 1667-1682, October.
    16. Yuko Murayama & Hans Jochen Scholl & Dimiter Velev, 2021. "Information Technology in Disaster Risk Reduction," Information Systems Frontiers, Springer, vol. 23(5), pages 1077-1081, September.
    17. Reich, Jennifer A., 2020. "“We are fierce, independent thinkers and intelligent”: Social capital and stigma management among mothers who refuse vaccines," Social Science & Medicine, Elsevier, vol. 257(C).
    18. Yan, Tao & Wong, Pak Kin & Ren, Hao & Wang, Huaqiao & Wang, Jiangtao & Li, Yang, 2020. "Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    19. Peng Xie, 2022. "The Interplay Between Investor Activity on Virtual Investment Community and the Trading Dynamics: Evidence From the Bitcoin Market," Information Systems Frontiers, Springer, vol. 24(4), pages 1287-1303, August.
    20. Jyoti Choudrie & Shruti Patil & Ketan Kotecha & Nikhil Matta & Ilias Pappas, 2021. "Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study," Information Systems Frontiers, Springer, vol. 23(6), pages 1431-1465, December.

    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:gam:jmathe:v:10:y:2022:i:17:p:3199-:d:906642. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.