Detecting public outlook towards vaccination using machine learning approaches: a systematic review

Hossain, Sheikh Md. Hanif and Sulaiman, Suriani (2022) Detecting public outlook towards vaccination using machine learning approaches: a systematic review. In: Advances on Intelligent Informatics and Computing. Lecture Notes on Data Engineering and Communications Technologies, 127 . Springer, pp. 141-150. ISBN 978-3-030-98740-4

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Abstract

Vaccination is an effective measure to prevent the spread of harmful diseases. The prevalence towards vaccine hesitancy, however, has been growing throughout the years and expressed openly in various social media platforms. Research works on automating the detection of public’s opinion towards vaccination in social media has recently gained significant popularity with the rise of the COVID-19 pandemic. This paper presents a systematic review on the machine learning approaches used by researchers to detect the inclination of the public towards vaccination. We analyzed the research work conducted within the past five years and summarized their findings. Our systematic review reveals that Support Vector Machine is the most widely used machine learning technique in identifying public sentiment towards vaccination producing the best performance with an F1-score of 97.3, while Twitter is found to be the most popular platform for extracting source of data.

Item Type:	Book Chapter
Uncontrolled Keywords:	Machine learning, Vaccine hesitancy, Anti-vaccine, Deep learning, Ensemble learning, Vaccine sentiment detection
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Suriani Sulaiman
Date Deposited:	17 Oct 2022 11:03
Last Modified:	10 Mar 2023 15:42
URI:	http://irep.iium.edu.my/id/eprint/100631

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