| 1 |
[46] |
Analyzing public sentiment on the Covid-19 vaccination and the aftermath of vaccination regarding health safety measures. |
US |
Tweets in English, collected in April–May 2021 |
Natural language processing and sentiment analysis techniques |
People have positive sentiments towards taking Covid-19 vaccines despite certain adverse effects of some of the vaccines. Their forecast model predicted that around 62.44% and 48% of the US population would receive at least one dose of vaccine and be fully vaccinated, respectively, by the end of July 2021. |
Not applied |
Not applied |
| 2 |
[47] |
Examining public discussions and emotions using Covid-19 related messages on Twitter. |
Worldwide |
Tweets only in English, collected from March 1 to April 21 in 2020 |
Machine learning approach, Latent Dirichlet Allocation (LDA) and sentiment analysis |
Identification of popular unigrams and topics. Real-time monitoring and assessment of the Twitter discussion and concerns raised can be promising for public health emergency responses and planning. |
Not applied |
Not applied |
| 3 |
[48] |
Understanding the public’s perception of the safety and acceptance of Covid-19 vaccines in real-time by using Twitter polls. |
Worldwide, English, Spanish, or other languages |
Two polls by using Twitter’s built-in, anonymous polling tool |
Twitter Poll Analysis |
Despite the perceived high level of uncertainty regarding the safety of the available Covid-19 vaccines, the authors observed an elevated willingness to undergo vaccination among their study sample. |
Not applied |
Not applied |
| 4 |
[49] |
Identification of the topics and sentiments in the public Covid-19 vaccine-related discussion on social media. |
Global perspective |
Twitter chatter dataset from March 11, 2020 to January 31, 2021 |
Machine learning approach, Latent Dirichlet Allocation (LDA) and sentiment analysis |
16 topics were obtained, which were grouped into 5 overarching themes. The topics mirrored the active news in the mainstream media. The positive sentiment around Covid-19 vaccines and the dominant emotion of trust shown in the social media discussion may imply a higher acceptance of vaccines. |
Not applied |
Not applied |
| 5 |
[50] |
The illustrating of public attitudes towards mask usage during the Covid-19 pandemic, from Twitter data. |
Worldwide in the English language |
Twitter data only in English, collected from March 17, 2020 to July 27, 2020 |
NLP, clustering and sentiment analysis techniques |
Topic clustering based on mask-related Twitter data offers revealing insights into societal perceptions of Covid-19 and techniques for its prevention. The volume and polarity of mask-related tweets greatly increased. |
Not applied |
Not applied |
| 6 |
[16] |
Conducting a country-specific study of real-time public awareness and behavioral responses to Covid-19 vaccines and vaccination in China. |
China |
Weibo chatter data in Chinese (Simplified Chinese and Traditional Chinese) collected from January to October 2020 |
Natural language processing and sentiment analysis techniques |
The Chinese public is divided in terms of vaccination prices and has differing expectations. Topics on Covid-19 vaccine acceptance in China include price and side effects. |
Price and side effects |
Not applied |
| 7 |
[51] |
The investigation of determinants, describing a diverse set of socio-economic characteristics, in explaining the outcome of the first wave of the coronavirus pandemic. |
Worldwide |
A review of the literature describing the social and economic factors which contribute to the spread of an epidemic. |
The Bayesian model averaging (BMA) technique |
The examination of a total of 31 potential determinants that describe a diverse ensemble of social and economic factors, including healthcare infrastructure, societal characteristics, economic performance, demographic structure, etc. |
Socio-economic determinants: the level of economic development, the population size |
Not applied |
| 8 |
[52] |
A content analysis based on the capability, opportunity, motivation–behavior (COM-B) model to characterize the determinants influencing behavioral intentions toward Covid-19 vaccines. |
Worldwide |
Tweets posted in English from November 1–22, 2020 |
A theory-based content analysis and coding of textual data |
Researchers identified tweets that contained behavioral intentions regarding Covid-19 vaccines and mapped them to constructs in the adapted model. Then, nine themes were generated that influence Twitter users’ intentions to receive Covid-19 vaccines. |
Misinformation or conspiracy theories about Covid-19; the positive value of vaccination to society; the mistrust of vaccines and the government |
COM-B model |
| 9 |
[1] |
A practical taxonomy for the determinants of vaccine uptake |
Worldwide |
Scientific research from 1970 to 2016 |
Literature review |
The 5As taxonomy facilitates a mutual understanding of the root causes of poor uptake. |
23 possible primary determinants of vaccination coverage |
5As |
| 10 |
Present work |
Revealing the main determinants of Covid-19 vaccine uptake from Twitter data |
Poland |
Tweets in Polish collected in May 2021 |
Text mining, analyzing and coding textual data |
(i) The identification of an additional sixth dimension, labeled Assurance, in the 5As taxonomy; (ii) a preliminary proof-of-concept of the 6As; (iii) a validation of the usability of textual data from public discussions in identifying and classifying determinants of vaccine uptake. |
17 determinants have been identified; they are presented in Table 4. |
6As |
