Table 3.

Taxonomy analysis.

Ref	Source Data	Volume of Data	Duration of Collection	VH Discussed	Work Applied	Analysis Applied	Taxonomy Category
[66]	•Twitter	•1,028,742 (t)	2015–2018	•Disease and vaccine	Identify Polarity in Tweets from an Imbalanced Dataset	•Machine Learning	General
[67]	•Facebook •Twitter	•58,078 (p) •82,993 (t)	Jan 2009–Aug 2016	•Pro-vaccination, expressing vaccine	Examine FB and Twitter social media discussion of vaccination in relation to measles	•Machine Learning •Statistical Analysis	Measles
[68]	•Twitter	•6000 (t)	Jul 2015–Aug 2015	•HPV vaccine	Study public opinions on human papillomavirus (HPV) vaccines on social media	•Transfer Learning	HPV
[69]	•Facebook	•84 FB (p)	May 2017–Dec 2017	•HPV vaccine	Assess how different FB posts resonate with parents hesitant about HPV vaccination.	•Opinion Mining	HPV
[70]	•Twitter	•184,214 (t)	Nov 2015–Mar 2016.	•HPV vaccine	Extract public opinions towards HPV vaccines	•Machine Learning	HPV
[71]	•Twitter	•287,100 (t)	Jan 2008–Dec 2017	•HPV vaccine	Analyze the opinions on HPV vaccination	•NLP Framework	HPV
[72]	•Scholarly Journals	•44 (a)	Before Dec 2018	•HPV vaccine	Examine how social media may impact HPV vaccine	•Content Analyses	HPV
[73]	•Twitter	•68,000 (t)	Mar 2020–Apr 2020	•COVID-19 vaccine	Discover what topical issues relating to the COVID-19 pandemic and what impacts these issues	•Topic Modelling	COVID-19
[33]	•Twitter	•31,100 (t)	Jan–Oct 2020	•COVID-19 vaccine	Extract topics and sentiments relating to COVID-19 vaccination	•Machine Learning	COVID-19
[74]	•Twitter •Google	•637 (t) •569 (n)	Feb–May 2020	•COVID-19 vaccine	Understand the prevailing sentiments regarding COVID-19 vaccines	•Machine Learning •Artificial Intelligence	COVID-19
[75]	•Twitter	•319,524 (t)	Jan–May 2020	•COVID-19 vaccine	Investigate people's reactions and concerns about COVID-19	•Topic Modelling	COVID-19
[76]	•Twitter	•73,760 (t)	Different Months in 2020	•COVID-19 vaccine	Analyze the major concerns about COVID-19 vaccines	•Machine Learning	COVID-19
[56]	•Facebook •Twitter	•23,571 (p) •40,268 (t)	Mar–Nov 2020	•COVID-19 vaccine	Understand public attitude and concerns regarding COVID-19 vaccines	•Natural Language Processing, •Deep Learning	COVID-19
[78]	•Twitter	•75,797,822 (t)	Jan–Aug 2020	•COVID-19 vaccine	Identify anti-vaccination tweets	•Stance analysis •Machine learning	COVID-19
[79]	•Twitter	•318,371 (t)	posted in 2018,	•COVID-19 vaccine	Propose procedures for testing for disorientation	•Sentiment analysis	COVID-19
[80]	•Web and Social Media	•2,207,167 (c)	Oct 2015–Aug 2018	•Pro vaccine •Anti vaccine •Free Vaccine	Propose an in-depth analysis of the emerging social debate	•Natural Language Processing, •Social Business Intelligence	Misinformation
[81]	•Twitter	•1.8 million (t)	2014–2017	•Vaccine Misinformation	Adapt and extend an existing typology of vaccine misinformation	•Topic Modelling	Misinformation
[82]	•Twitter	•27,534 (t)	Jan 2012–Feb 2017	•Vaccine Misinformation	Developed a system that automatically classify stance towards vaccination	•Sentiment Analysis, •Machine Learning	Misinformation
[97]	•Scholarly Journals	•69 (a)	Before Mar 2019	•Health misinformation	Identify the main health misinformation topics	•Prisma	Misinformation
[83]	•Scholarly Journals	•86 (a)	2015–2018	•negative and positive sentiments	Identify the methods most commonly used for monitoring vaccination-related	•Descriptive Analysis	Misinformation
[84]	•Survey	•58-practice	Jan 2015–Jan 2017	•vaccination hesitancy	compared vaccine hesitancy and beliefs about illness	•Statistical Analysis	Misinformation
[85]	•Twitter	•669,136 (t)	Feb–Mar 2015	•Pro vaccine •Anti vaccine	Investigate the communication patterns of anti- and pro-vaccine	•Sentiment Analysis, Machine Learning	Debate
[86]	•Twitter	•26,389 (t)	Apr 2015–May 2015	•Sentiment on vaccine	Examine vaccine sentiment on social media	•Semantic Network Analysis	Debate
[87]	•Social Media	•40,359 (p)	Jan–Dec 2015	•Childhood vaccine	Develop a childhood vaccination ontology	•Sentiment Analysis	Debate
[88]	•Twitter •Forums •Blogs •Comments	•209 (b) •87 (co) •1553 (n) •14143 (t)	Nov 2018–April 2019	•Pregnant women vaccine	Understand the predominant topics of maternal vaccines	•Sentiment Analysis •Stance Analysis •Discourse Analysis •Topic Analysis	Debate
[89]	•Twitter	•180,620 (t)	Sep 2016–Aug 2017	•Sentiment on vaccine	Monitor the public opinion on vaccination	•Machine Learning •Statistical Analyses •Sentiment Analysis	Opinion
[90]	•Youtube	•2780 (v)	2017–2018	•Sentiment on vaccine	Understand if and how the population's opinion has changed before and after the vaccination campaign	•Text Mining •Sentiment Analysis	Opinion
[58]	•Twitter	•1,499,227 (t)	Jun 2011–Apr 2019	•Sentiment on vaccine	Evaluate public perceptions regarding vaccination	•Sentiment Analysis	Opinion
[91]	•Twitter	•12180 (t)	Jan 2016–May 2016	•Sentiment on vaccine	Analyze the use of Twitter during broadcasts dedicated to vaccines	•Quantitative Analysis •Qualitative Analysis	Opinion
[92]	•Online News	•1788 (n)	Nov 2015–May 2020	•Sentiment on vaccine	Study the profile and vaccine sentiments of the online media news	•Descriptive Analysis	Opinion
[93]	•Twitter	•100,000 (t)	Nov 2019–May 2020	•Sentiment on vaccine	Provide solution on sentiment analysis of about 100,000 tweets	•Sentiment Analysis	Opinion
[77]	•Twitter	•1.8 million (t)	Jan–May 2020	•Sentiment on vaccine	Explore methods to characterize and classify COVID-19 conspiracy theories	•Sentiment Analysis	Opinion
[94]	•Twitter •Sina Weibo	•N/A	March–July 2020	•Sentiment on vaccine	Examine the challenges and opportunities inherent in the use of social media	•Sentiment Analysis	Opinion

Tweet (t); Post (p); Article (a); News (n); Clip (c); Blog (b); Comment (co).