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

• Main Authors: Hossain, Sheikh Md. Hanif, Sulaiman, Suriani
• Format: Book Chapter
• Language: English; English
• Published: Springer 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://irep.iium.edu.my/100631/14/100631_Detecting%20public%20outlook%20towards%20vaccination%20using%20machine%20learning%20approaches_Scopus.pdf; http://irep.iium.edu.my/100631/15/100631_Detecting%20public%20outlook%20towards%20vaccination%20using%20machine%20learning%20approaches.pdf; http://irep.iium.edu.my/100631/; https://link.springer.com/chapter/10.1007/978-3-030-98741-1_13

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.