Analysis of the Anti-Vaccine Movement in Social Networks: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Eligibility Criteria
2.2. Information Sources and Search
2.3. Study Selection and Risk of Bias
2.4. Data Collection, Variables and Data Analysis
- Variables about the characteristics of the sample: year of publication, country of study, study design, number of tweets, Facebook or Instagram comments or YouTube videos.
- Variables about the study: aim and main results of each article.
3. Results
3.1. Study Selection
3.2. Study Characteristics
3.3. Twitter and Vaccine Information
3.4. Facebook and YouTube and Vaccine Infringement
4. Discussion
Limitations
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Author, Year, Country of Study. | Study Design | Sample Size | Objective | Main Results | Level of Evidence/Degree of Recommendation |
---|---|---|---|---|---|
Gunaratne et al, 2019, Canada. [12] | Descriptive cross-sectional study. | 1,637,712 tweets | To reflect the temporal trends of discussions for and against vaccines on Twitter and determine the extent of communication between the two groups. | The study reveals an increase in speeches in favor of vaccines from 2014. Despite this, the opposite group continues to grow in size, and communication between both is minimal. 576,695 tweets (35%) were anti-vaccine. The hashtags #cd-cwhistleblower, #vaxxed, #hearthiswell, #novax and #cd-cfraud were the most used by the anti-vaccines groups. From 291,747 users, 12% posted only anti-vaccine hashtags, increasing from 8.1 in 2015 to 16% in 2018. | 3/D |
Blankenship et al, 2018, United States. [13] | Descriptive cross-sectional study | 1545 tweets | To investigate the level of participation that tweets with different opinions about vaccines attract. | They analyzed two hashtags: #vaccine and #vaccineworks selecting a random sample. From 1344 analyzed tweets with the hashtag #vaccine, 24.2% were about the anti-vaccine sentiment and 59.1% of them had links to websites. Anti-vaccine tweets were focused on risk and dangers and distrust of pharmaceutical industry, science or government. The 201 tweets with the hashtag #vaccineswork did not have information about anti-vaccine movement. Tweets against vaccines were more likely to interact (retweets) than those expressing feelings for them. These two, in addition, had greater participation than the tweets that were neutral. | 3/D |
Shah et al, 2019, Australia. [14] | Descriptive cross-sectional study. | 6,591,566 tweets | To characterize the potential scope of shared vaccine websites on Twitter in relation to credibility. | Among shared websites, 11.86% maintained low credibility and generated 112,225 retweets (14.68%). Of these, it is estimated that 100 Most Viewed were visited by between 2 and 10 million Twitter users. The low-credibility web pages linked in twitter were related to individual stories and autonomy. | 3/D |
Kang et al, 2017, United States [15] | Descriptive cross-sectional study. | 50 websites shared on Twitter. | To know the opinion about vaccines of Twitter users through the semantics of the web pages they share. | Of the web pages shared, 23 pages showed positive feelings towards vaccines, 21 were negative and 6 were neutral. The pages that spoke positively treated it from the point of view of childhood, adolescence and adults, but the negative ones only talked about childhood. The most commonly used concepts in the negative pages were: children, mercury, autism, industrialization of vaccines, ingredients of vaccines. | 3/D |
Love et al, 2013, United States. [16] | Descriptive cross-sectional study. | 2580 tweets. | To analyze the content provided by Twitter on vaccination, taking into account the medical reliability to know the type of information about which users are interested and thus lead educational campaigns. | Of the tweets analyzed, 33% were positive for immunization, 54% maintained a neutral position and 13% were against. Those in favour made contributions promoting their administration, the neutrals reported experiences on the subject and the negatives consisted of claims about vaccine damage. | 3/D |
Broniatowski et al, 2018, United States. [17] | Descriptive cross-sectional study. | 899 tweets from bots and trolls and 9895 tweets from actual Twitter users. | To understand how Russian bots and trolls promote health content on Twitter. | Accounts identified as trolls or bots were more likely to tweet about vaccine content than normal users and were less likely to create preventable disease content. Bot accounts tended to post more anti-vaccine content than normal users but their message was less polarized. From the analysis of 253 tweets with the hashtag #vaccinateUS, 38% were anti-vaccine. These were usually related to conspiracy theories and risks. | 3/D |
Hoffman et al, 2019, United States. [18] | Descriptive cross-sectional study. | 197 Facebook users | To systematically evaluate people who express negative feelings related to vaccines on Facebook. | Most of them were female (89%) and parents (78%). These publications were taken from anti-vaccination groups that referred to themselves as "pro-information" or “pro-science”. Their anti-vaccination posts were about their risks and damages, indicating that vaccines caused diseases and death, personal stories and conspiracy theories. | 3/D |
Tustin et al, 2018, Canada. [19] | Descriptive cross-sectional study. | 117 comments from Facebook | To analyze the content of Facebook users’ comments to find out the main feelings towards vaccinations. | Of the 85 commentators 77% were female. Of all comments about vaccinations, 43.6% were positive, 35% negative and the rest were ambiguous. Negative comments included misperceptions of risk, inaccurate knowledge, distrust of pharmacists or health care providers, negative experiences with vaccines or beliefs. Almost 40% of positive reviews spoke of the risks of non-vaccination and judged the level of knowledge of anti-vaccination. | 3/D |
Jamison et al, 2019, USA. [20] | Descriptive cross-sectional study. | 309 vaccine-related ads on Facebook | To analyze the new Facebook tool to publish ads and the reliability of these related vaccines. | Of all ads, 53% were pro-vaccines and 47% anti-vaccine. However, only 27 people were anti-vaccine ad buyers, while 83 were pro-vaccine. The pro-vaccine announcements were divided into five themes: vaccine promotion, philanthropic work, advocacy of vaccination policies, news and anti-vaccine views. Anti-vaccines were more unified, described the damage done, promoted the choice of vaccine and revealed an alleged institutional fraud. | 3/D |
Faasse et al, 2016, Australia. [21] | Descriptive cross-sectional study. | 1489 comments from Facebook. | To investigate the language used by people for and against immunization in the same conversation in order to provide an optimal education. | Comments in favor of vaccines received more "likes" than those against and neutrals, the latter being the least receiving of the three. The positive comments were more truthful than the negative ones and these in turn contained less family-related words than the other two groups. Anti-vaccination comments were based on risks and causation words and fewer positive emotion words. They also had lower authenticity and references. | 3/D |
Schmidt et al, 2018, Italy. [22] | Descriptive cross-sectional study. | 243 Facebook pages. | To assess whether the conduct of users about immunization is polarized and how it evolves over time. | Most users were active in one group, for or against vaccines, but not in both. The anti-vaccine group consumed information from a more diverse set of pages than pro-vaccine. Anti-vaccine were more committed to their post consumption. The ant-vaccine community grew in a more cohesive manner on the social network, with less fragmentation. | 3/D |
Yiannakoulias et al, 2019, Canada. [23] | Descriptive cross-sectional study. | 206 YouTube videos | To report strategies that increase useful information on YouTube regarding pro and anti-vaccine content. | The most frequent searches were about personal stories rather than about the benefits or how vaccines worked, so videos from public health agencies had fewer views. In addition, the search terms are very similar for both pro-vaccine and anti-vaccine content. Anti-vaccine videos contained more target words and had higher likeability. The words mercury, syringe, cheical and toxic were more used in anti-vaccine videos. | 3/D |
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Ortiz-Sánchez, E.; Velando-Soriano, A.; Pradas-Hernández, L.; Vargas-Román, K.; Gómez-Urquiza, J.L.; Cañadas-De la Fuente, G.A.; Albendín-García, L. Analysis of the Anti-Vaccine Movement in Social Networks: A Systematic Review. Int. J. Environ. Res. Public Health 2020, 17, 5394. https://doi.org/10.3390/ijerph17155394
Ortiz-Sánchez E, Velando-Soriano A, Pradas-Hernández L, Vargas-Román K, Gómez-Urquiza JL, Cañadas-De la Fuente GA, Albendín-García L. Analysis of the Anti-Vaccine Movement in Social Networks: A Systematic Review. International Journal of Environmental Research and Public Health. 2020; 17(15):5394. https://doi.org/10.3390/ijerph17155394
Chicago/Turabian StyleOrtiz-Sánchez, Elvira, Almudena Velando-Soriano, Laura Pradas-Hernández, Keyla Vargas-Román, Jose L. Gómez-Urquiza, Guillermo A. Cañadas-De la Fuente, and Luis Albendín-García. 2020. "Analysis of the Anti-Vaccine Movement in Social Networks: A Systematic Review" International Journal of Environmental Research and Public Health 17, no. 15: 5394. https://doi.org/10.3390/ijerph17155394
APA StyleOrtiz-Sánchez, E., Velando-Soriano, A., Pradas-Hernández, L., Vargas-Román, K., Gómez-Urquiza, J. L., Cañadas-De la Fuente, G. A., & Albendín-García, L. (2020). Analysis of the Anti-Vaccine Movement in Social Networks: A Systematic Review. International Journal of Environmental Research and Public Health, 17(15), 5394. https://doi.org/10.3390/ijerph17155394