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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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Mahsa Amiri & Maryam Yaghtin & Hajar Sotudeh, 2024. "How do tweeters feel about scientific misinformation: an infoveillance sentiment analysis of tweets on retraction notices and retracted papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(1), pages 261-287, January.

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