. 2021 Oct 16;139:104957. doi: 10.1016/j.compbiomed.2021.104957

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).