| Literature DB >> 35214620 |
Hüseyin Küçükali1, Ömer Ataç1, Ayşe Seval Palteki1, Ayşe Zülal Tokaç1, Osman Hayran1.
Abstract
Twitter is a useful source for detecting anti-vaccine content due to the increasing prevalence of these arguments on social media. We aimed to identify the prominent themes about vaccine hesitancy and refusal on social media posts in Turkish during the COVID-19 pandemic. In this qualitative study, we collected public tweets (n = 551,245) that contained a vaccine-related keyword and had been published between 9 December 2020 and 8 January 2021 through the Twitter API. A random sample of tweets (n = 1041) was selected and analyzed by four researchers with the content analysis method. We found that 90.5% of the tweets were about vaccines, 22.6% (n = 213) of the tweets mentioned at least one COVID-19 vaccine by name, and the most frequently mentioned COVID-19 vaccine was CoronaVac (51.2%). We found that 22.0% (n = 207) of the tweets included at least one anti-vaccination theme. Poor scientific processes (21.7%), conspiracy theories (16.4%), and suspicions towards manufacturers (15.5%) were the most frequently mentioned themes. The most co-occurring themes were "poor scientific process" with "suspicion towards manufacturers" (n = 9), and "suspicion towards health authorities" (n = 5). This study may be helpful for health managers, assisting them to identify the major concerns of the population and organize preventive measures through the significant role of social media in early spread of information about vaccine hesitancy and anti-vaccination attitudes.Entities:
Keywords: COVID-19; Twitter; content analysis; social media; vaccine hesitancy; vaccines
Year: 2022 PMID: 35214620 PMCID: PMC8876163 DOI: 10.3390/vaccines10020161
Source DB: PubMed Journal: Vaccines (Basel) ISSN: 2076-393X
Figure 1Data saturation assessment.
Characteristics of the user accounts.
|
| % | |
|---|---|---|
| Verification status | ||
| Not verified | 973 | 97.3 |
| Verified | 27 | 2.7 |
| User type | ||
| Personal or others | 887 | 88.7 |
| Organizational | 113 | 11.3 |
| Total | 1000 | 100.0 |
|
| ||
| Duration of Twitter use (year) | 4.0 (1.0–8.0) | |
| Number of followers | 276.5 (55.0–603.8) | |
| Number of tweets published | 3163.5 (1561.5–13,951.0) | |
Characteristics of the tweets.
|
| % | |
|---|---|---|
| Presence of a visual | ||
| Yes | 116 | 11.1 |
| No | 925 | 88.9 |
| Presence of an URL | ||
| Yes | 219 | 21.0 |
| No | 822 | 79.0 |
| Presence of a hashtag | ||
| Yes | 123 | 11.8 |
| No | 918 | 88.2 |
| Relevancy with the vaccine | ||
| Irrelevant | 99 | 9.5 |
| Relevant | 942 | 90.5 |
| Total | 1041 | 100.0 |
|
| ||
| Number of tweets per day | ||
| Before arrival of vaccines (11 Dec–29 Dec) | 29.0 (25.0–40.0) | |
| After arrival of vaccines (30 Dec–10 Jan) | 27.0 (24.0–29.0) | |
| Total | 29.0 (24.5–38.5) | |
Figure 2Daily numbers of tweets in the sample.
Tweet contents, including vaccine names and anti-vaccination themes.
| Contents |
| % |
|---|---|---|
| Vaccine names | ||
| Name of a COVID-19 vaccine | 213 | 22.6 |
| Name of other vaccines * | 25 | 2.7 |
| No vaccine name | 705 | 74.8 |
| Total | 942 | 100.0 |
| Anti-vaccination themes | ||
| Present in tweets | 207 | 22.0 |
| Not present in tweets | 735 | 78.0 |
| Total | 942 | 100.0 |
* Influenza, pneumonia, rabies, multiple sclerosis, tetanus, smallpox, polio, cholera, human papillomavirus. One tweet mentioned both COVID-19 and multiple sclerosis vaccines.
Distribution of the vaccine names in tweets that mentioned a COVID-19 vaccine.
| Names of the Vaccines | % | |
|---|---|---|
| CoronaVac mentions | 109 | 51.2 |
| Chinese vaccine | 90 | 42.3 |
| Sinovac | 19 | 8.9 |
| CoronaVac | 4 | 1.9 |
| Comirnaty mentions | 57 | 26.7 |
| Pfizer-Biontech | 54 | 25.4 |
| German vaccine | 5 | 2.3 |
| Comirnaty | 1 | 0.5 |
| Moderna mentions | 14 | 6.6 |
| Moderna | 12 | 5.6 |
| American vaccine | 4 | 1.9 |
| National (Turkish) vaccine mentions | 12 | 5.6 |
| mRNA vaccine mentions | 8 | 3.8 |
| AstraZeneca mentions | 7 | 3.3 |
| Oxford | 4 | 1.9 |
| AstraZeneca | 3 | 1.4 |
| Sputnik V mentions | 5 | 2.3 |
| Russian vaccine | 4 | 1.9 |
| Sputnik V | 1 | 0.5 |
| Other COVID-19 vaccine mentions | 14 | 6.6 |
| Total | 213 | 100.0 |
* Sum of the numbers is not equal to the total and subtotals because some tweets included more than one vaccine name.
Distribution of anti-vaccination themes.
| Themes |
| % |
|---|---|---|
| Poor scientific process | 45 | 21.7 |
| Conspiracy theories | 34 | 16.4 |
| Suspicion towards manufacturers | 32 | 15.5 |
| Suspicion towards health authorities | 27 | 13.0 |
| Undirected distrust | 26 | 12.6 |
| Violation of autonomy | 25 | 12.1 |
| Unsafety | 23 | 11.1 |
| Non necessity | 21 | 10.1 |
| Ineffectiveness | 17 | 8.2 |
| Influential people | 14 | 6.8 |
| Pandemic denial | 11 | 5.3 |
| Financial interests of manufacturers | 9 | 4.3 |
| China’s oppression of Uighurs | 8 | 3.9 |
| Religious beliefs | 3 | 1.4 |
| Total * | 207 | 100.0 |
* Sum of the numbers is not equal to the total because 71 tweets include more than one theme (See also Figure 3).
Figure 3Co-occurrence of themes among tweets that contained more than one theme.
Figure 4Synthesis of vaccine hesitancy attitudes in tweets.