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Analysis of Public Perceptions Towards the COVID-19 Vaccination Drive: A Case Study of Tweets with Machine Learning Classifiers

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Disease Control Through Social Network Surveillance

Part of the book series: Lecture Notes in Social Networks ((LNSN))

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Abstract

In the realm of contemporary soft computing practices, analysis of public perceptions and opinion mining (OM) have received considerable attention due to the easy availability of colossal data in the form of unstructured text generated by social media, e-commerce portals, blogs, and other similar web resources. The year 2020 witnessed the gravest epidemic in the history of mankind, and in the present year, we stand amidst a global, massive and exhaustive vaccination movement. Since the inception of the COVID-19 vaccines and their applications, people across the globe, from the ordinary public to celebrities and VIPs have been expressing their fears, doubts, experiences, expectations, dilemmas and perceptions about the current COVID-19 vaccination program. Being very popular among a large class of modern human society, the Twitter platform has been chosen in this research to study public perceptions about this global vaccination drive. More than 112 thousand Tweets from users of different countries around the globe are extracted based on hashtags related to the affairs of the COVID-19 vaccine. A three-tier framework is being proposed in which raw Tweets are extracted and cleaned first, visualized and converted into numerical vectors through word embedding and N-gram models next, and finally analyzed through a few machine learning classifiers with the standard performance metrics, accuracy, precision, recall, and F1-measure. The Logistic Regression (LR) and Adaptive Boosting (AdaBoost) classifiers attended the highest accuracies of 87% and 89% with the Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) word embedding models respectively. Overall, the BoW model achieved slightly better average classification accuracy (78.33%) than that of the TF-IDF model (77.89%). Moreover, the experimental results show that most of the people have a neutral attitude towards the current COVID-19 vaccination drive and people favoring the COVID-19 vaccination program are greater in number than those who doubt it and its consequences.

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Notes

  1. 1.

    The reader can download complete dataset from here: https://drive.google.com/drive/folders/1AjA4PhZL7kAWfY_WItnQwJGxBUoLfP-W?usp=sharing

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Author information

Authors and Affiliations

  1. Sikh National College, Qadian, Guru Nanak Dev University, Amritsar, Punjab, India

    Koushal Kumar

  2. Department of Computer Applications, LSM Government PG College, Pithoragarh, Uttarakhand, India

    Bhagwati Prasad Pande

Authors
  1. Koushal Kumar
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  2. Bhagwati Prasad Pande
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Editor information

Editors and Affiliations

  1. Multispectral Imagery Lab—MILAB, ECE, University of Georgia, Athens, GA, USA

    Thirimachos Bourlai

  2. Department of Informatics and Computers, Hellenic Air Force Academy, Acharnes Attica, Greece

    Panagiotis Karampelas

  3. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

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Kumar, K., Pande, B.P. (2022). Analysis of Public Perceptions Towards the COVID-19 Vaccination Drive: A Case Study of Tweets with Machine Learning Classifiers. In: Bourlai, T., Karampelas, P., Alhajj, R. (eds) Disease Control Through Social Network Surveillance. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-031-07869-9_1

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  • DOI: https://doi.org/10.1007/978-3-031-07869-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07868-2

  • Online ISBN: 978-3-031-07869-9

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