What Are the Risk on Social Media Influence Decisions on Anti Vaccination Children
PLoS 1. 2021; 16(iii): e0247642.
The anti-vaccination infodemic on social media: A behavioral analysis
Federico Germani
Constitute of Biomedical Ethics and History of Medicine, University of Zurich, Zürich, Switzerland
Nikola Biller-Andorno
Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zürich, Switzerland
Luigi Lavorgna, Editor
Received 2020 Dec eighteen; Accepted 2021 February 10.
- Supplementary Materials
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S1 File: (PDF)
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S2 File: Pro-vaccination network. (PDF)
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S3 File: Anti-vaccination network. (PDF)
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Attachment: Submitted filename: Response to reviewers.pdf
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- Data Availability Statement
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All relevant data are inside the paper and its Supporting information files.
Abstruse
Vaccinations are without doubt 1 of the greatest achievements of modern medicine, and there is hope that they can constitute a solution to halt the ongoing COVID-xix pandemic. Nevertheless, the anti-vaccination movement is currently on the rising, spreading online misinformation most vaccine safety and causing a worrying reduction in vaccination rates worldwide. In this historical time, it is imperative to understand the reasons of vaccine hesitancy, and to find effective strategies to dismantle the rhetoric of anti-vaccination supporters. For this reason, we analyzed the behavior of anti-vaccination supporters on the platform Twitter. Hither we identify that anti-vaccination supporters, in comparison with pro-vaccination supporters, share conspiracy theories and make use of emotional language. We demonstrate that anti-vaccination supporters are more engaged in discussions on Twitter and share their contents from a pull of stiff influencers. We show that the movement's success relies on a stiff sense of community, based on the contents produced by a minor fraction of profiles, with the community at large serving equally a sounding board for anti-vaccination discourse to circulate online. Our information demonstrate that Donald Trump, before his contour was suspended, was the main commuter of vaccine misinformation on Twitter. Based on these results, we welcome policies that aim at halting the circulation of faux information about vaccines by targeting the anti-vaccination customs on Twitter. Based on our data, nosotros also propose solutions to improve the advice strategy of wellness organizations and build a community of engaged influencers that back up the dissemination of scientific insights, including issues related to vaccines and their safety.
Introduction
Vaccinations are a dandy medical achievement of the terminal century, given their fundamental contribution to lowering the presence of otherwise widespread diseases in the population and thus in greatly reducing mortality. Despite the available evidence and the scientific consensus on the necessity and the safety of vaccines, an anti-vaccination motion has been growing over the by decades [1], with a consequent turn down in vaccination rates and the possible resurgence of diseases such equally measles [2]. This move, which has gained momentum after the infamous publication of Andrew Wakefield's study linking vaccines to autism in 1998 [three], has been lately growing its strength, taking advantage of social media as communication channels [four, v]. In a postmodern world in which medical expertise is beingness questioned [six, 7], the growing grip of the anti-vaccination movement on the general public is of not bad concern, specially amidst a global pandemic that could be solved by the development of safe and effective vaccines. Therefore, while we navigate through the COVID-19 pandemic and the concomitant infodemic, presenting proper information concerning vaccines to the public is of utmost importance.
In order to tackle the vaccination result, the causes of the success of the anti-vaccination movements need to exist carefully analyzed. Until now, it has been shown that vaccination pick is influenced past the belief in alternative medicine, the belief in conspiracy theories, past morality, religion and personal credo, the emotive appeals or the lack of trust in authorities [8], as well as by the readability and engagement of pro- versus anti-vaccination articles [9]. Most studies primarily focus on two aspects, the psychological attitude connected to vaccination choice [10–12] and the function of the Cyberspace and in particular social media [8, 13–18]. In fact, anti-vaccination supporters find fertile ground in item on Facebook and Twitter [17, 19, 20], every bit these platforms offer a digital space for people to share any kind of content, including science-related or medically sensitive contents, which have the potential to reach a vast audience. Studies have particularly focused on the relevance of the Internet and social media in shaping personal or parental choice almost vaccination [xiii, 14, 17]. For instance, parents who make up one's mind non to vaccinate their children tend to shape their opinions after having been in contact with online data on the topic [21], and the bulk of individuals does non consider the credibility of the source of information [22–25]. In improver, anti-vaccination profiles and groups online have been shown to generate content that is based on personal experiences and opinions, whereas pro-vaccination groups and institutions accept the trend to quote experts and cite scientific literature when sharing their views online [9, 23]. Therefore, the adopted linguistic communication, the frequency of use of social media, the blazon of content that is generated, and their emotional appeal, could all found factors that determine the success of the anti-vaccination movement online. Furthermore, a contempo report suggested Twitter data could be a valid tool to measure beliefs among the general public concerning public wellness [26] and vaccine hesitancy [27]. Therefore, in order to place strategies to decrease the spread of vaccine misinformation online and to place potential communication strategies to exist used past healthcare organizations and professionals, we decided to quantitatively analyze the online behavior of Twitter users, after having determined whether they back up or dissimilarity vaccination programmes.
A recent study has identified that former U.s. President Donald Trump was likely to exist the largest driver of the COVID-nineteen misinformation infodemic [28]. This is relevant considering fake news, of any kind, have been shown to have affected diverse democratic votes, including the 2016 Usa elections and Brexit [29–31]. For instance, earlier the 2016 U.s. elections, simulated news stories favoring Trump were shared 30 one thousand thousand times on Facebook, against eight one thousand thousand times for those favoring Clinton [29]. For some politicians, social media and imitation news, including those concerning vaccines, could therefore exist instrumental to hold on power and determining the time to come course of our global society. Vaccination policies are not excluded from the aspects that tin determine and shape balloter results, especially during a pandemic that could be solved through the use of vaccines. In fact, both vaccine hesitancy and political populism are driven past the distrust in expertise and ideas of a bottom-up society [32], and political views play an important part in shaping vaccination choice [33].
Results
Anti-vaccination supporters tweet less, only appoint more in discussion
In order to empathize whether the success of the anti-vaccination discourse is due to a particularly pronounced activity of anti-vaccination supporters online, between September and December 2020, nosotros measured the number of Twitter actions on average in a month for each profile belonging to the control, anti-vaccination and pro-vaccination group (Fig 1A). Control profiles were selected for the utilize of randomly chosen hashtags (#command). Anti-vaccination users were identified for their use of the #vaccineskill and #vaccinesharm hashtags, which are widely used by the community. Finally, pro-vaccination communicators were identified for their utilise of the #vaccineswork hashtag (S1 Fig in S1 File). Nosotros divers Twitter actions as the sum of tweets, replies and retweets in a given month (Fig 1B). As expected, anti-vaccination profiles were the almost active on Twitter, with 536 actions per month, compared with an average of 277 actions for the control group and only 144 actions for the pro-vaccination group (Fig 1C), suggesting the latter is not engaged plenty, and highlighting a beginning pitfall in the pro-vaccine advice strategy online. However, in one case we calculated the number of tweets per calendar month, we were surprised to larn that anti-vaccination supporters were those tweeting the least (42 tweets per month), when compared with control and pro-vaccination profiles (123 and 93 tweets per calendar month, respectively) (Fig 1D). This was largely compensated by the engagement of the anti-vaccination group in discussions, be it through replies or retweets. Anti-vaccination profiles replied xiii-times more than control and pro-vaccination profiles (Fig 1E), retweeted 7.iv times more than their pro-vaccination counterparts, and 31.3 times more than control profiles (Fig 1F). Equally already pointed out past these data, the anti-vaccination group scored the highest number of retweets per Tweet (S2 Fig in S1 File), highlighting that the vast majority of anti-vaccination supporters act as an echo chamber for the pool of content generated by a small-scale fraction of users. Behavioral outliers, which were excluded with 0.1% confidence interval (ROUT, Q = 0.1%), suggest that a small fraction of users belonging to this grouping are producing the majority of the content, which is and then shared by the community at large. Information besides suggest that pro-vaccination individuals and groups are more prone to generate new content and are not very engaged with a broader community with similar interests.
Anti-vaccination supporters are more than engaged on Twitter.
We analyzed the behavior of three different groups: control (grey), anti-vaccination (carmine) and pro-vaccination (blue) (A). We calculated the number of tweets, replies and retweets per calendar month (B). The anti-vaccination group scored the highest number of total Twitter deportment (the sum of tweets, replies and retweets) per month (C). Anti-vaccination supporters tweeted less than control and pro-vaccination individuals (D), but they engaged in more discussion via an increased number of replies (Eastward) and Retweets (F). Ordinary one-way ANOVA; **p<0.01; ****p<0.0001; Outliers were excluded with ROUT, Q = 0.i%; n = l.
Anti-vaccination back up on Twitter is associated with a general belief in conspiracy theories and emotional behaviors
As we have seen, the anti-vaccination community constitutes an repeat bedchamber for misinformed views about vaccines generated by a smaller number of profiles. In order to empathise whether these dynamics are established by factors previously associated with vaccine hesitancy [viii, ix, 23], we quantified the number of conspiracy theory (CT)-associated contents (tweets and retweets), as well every bit the number of emotional contents (either depicting emotional situations or adopting emotional language) shared by control, anti-vaccination and pro-vaccination profiles. Furthermore, we calculated how dedicated the different groups were to share scientific and vaccines-related contents. Nosotros found that both pro- and anti-vaccination profiles shared a larger number of science- and vaccines-related contents when compared with control profiles (for scientific content: 2.five, 3.4 and 0 per month, respectively; for vaccines-related content: 1.2; 1.5 and 0 per month, respectively) (Fig 2A and 2B). Normalization of the aforementioned data for the total number of contents on whatever given topic indicates that the pro-vaccination group was the most interested in science and vaccines, when compared with anti-vaccination and control groups (Fig 2A' and 2B'). Additionally, the anti-vaccination grouping was the only ane circulating conspiracy theories (with an average of 2 contents per month). (Fig 2C and 2C'). About conspiracy theory-related tweets were associated with faux news concerning ruling elites, masonries and techniques of population command–ofttimes associated to public figures such every bit Bill Gates or to ongoing COVID-19 pandemic–, flat earth ideology or pedophilia scandals such equally 'pizzagate', but too more bizarre ones. The anti-vaccination group shared a larger number of emotional contents per month (and/or content with emotional linguistic communication) when compared with the pro-vaccination group and command group (1.five, 0.iv and 0 per calendar month, respectively) (Fig 2D). The normalization of these data for the total number of contents on any given topic shows that anti-vaccination supporters adopted emotional language and/or published content containing emotional information in 25% of the cases, whereas the pro-vaccination group in but 0.3% of the cases (Fig second'). In line with what was previously reported [9, 23], this suggests that the emotional sphere, which is also connected to the belief in conspiracy theories, is a predominant character of individuals supportive of the anti-vaccination movement. In order to understand whether anti-vaccination contents are associated with conspiracy theories, we calculated the normalized number of vaccines-related contents and correlated information technology with the number of CT-related contents. Every bit a positive control, we calculated whether the normalized number of science-related contents is correlated with the number of vaccines-related contents published by profiles associated with either the anti- or pro-vaccination groups. As expected, being vaccines-related contents considerable equally scientific contents themselves, in both cases at that place was a clear correlation between the aforementioned factors (R2 = 0.4654; p<0.0001**** and Rii = 0.5924; p<0.0001****, respectively) (S3 Fig in S1 File). For the anti-vaccine group, at that place was a strong and pregnant correlation betwixt the number of published contents against the use of vaccination and the number of published contents concerning conspiracy theories (R2 = 0.7479; p<0.00001****) (S4A Fig in S1 File), suggesting that anti-vaccination support tin can be seen equally a part of a bigger problem connected to beliefs in unsubstantiated claims. As pro-vaccine supporters did not share conspiracy theories on Twitter, at that place was no correlation between these contents and vaccines-related contents (S4A' Fig in S1 File). While performing the analysis, we further realized that a large portion of anti-vaccination profiles were sharing contents associated to children, not necessarily in relations to vaccination. For this reason, we decided to quantify the number of children-related content produced in the 3 groups. In comparison to the control, both anti- and pro-vaccination groups shared a college number of contents associated with children (control: 0; anti-vaccine: 1.2; pro-vaccine: 0.6 contents per calendar month. 0%, v.7% and 7.iii% of the contents business organization children, respectively) (S5 Fig in S1 File). Notwithstanding, we noticed a substantial difference in the communication strategy and topics associated with children in the pro- and anti-vaccination groups. Pro-vaccination supporters more often than not shared contents depicting happy children after having received a shot, whereas anti-vaccination supporters often shared agonizing images of suffering children, or citations of discredited or non-existing physicians about the dangers of vaccines for children. Further, children-related content in this group is also associated with other conspiracy theories about pedophilia scandals, or more than by and large virtually sexual and psychological abuses of children.
Anti-vaccination supporters are active science and vaccine communicators, share conspiracy theories and emotional content.
Both anti- (crimson) and pro-vaccination profiles (blue) share a larger number of science- and vaccine-related content per month, when compared with control profiles (grey) (A, B). We calculated the number of science- and vaccines-related content (tweets and retweets) published in the 24 hours before data analysis and normalized information technology for the total number of tweets published on average during a single day. 100 pct indicates that all generated contents are estimated to be science- or vaccines-related (A', B'). Natural fluctuations above 100 percent are due to a variable Twitter activeness during the 24 hours prior to data assay compared to an average day. Anti-vaccination supporters publish conspiracy theories, whereas control and pro-vaccination individuals do not publish this blazon of material (C, C'). Anti-vaccination supporters share a larger number of tweets and retweets with emotional contents (and with emotional language) compared with the pro-vaccination and command groups (D, D'). Ordinary 1-way ANOVA; ****p<0.0001; Outliers were excluded with ROUT, Q = 0.1%; due north = 50.
Emotional linguistic communication could help the success of vaccination campaigns
As we previously described, anti-vaccination supporters share emotional contents with the utilise of emotional language. In club to understand whether this language is necessary for the success of the move, we decided to perform an analysis of the most used words by the three different groups. We considered the 5 most used words for each private profile and calculated the nigh used words for each individual group. Following normalization against the words predominantly used by command profiles, nosotros identified a list of ten words strongly associated with anti- and pro-vaccination groups (Fig 3A and 3A'). As expected, the word "vaccine(south)" was the most represented in both groups, confirming that our initial criteria for inclusion were reasonable. To further highlight the differences between the two groups, we normalized the nigh used words in the two groups against each other (Fig 3B). Hither we institute that the most relevant words in the anti-Vaccination group were "President", "God", "People", and "Masks". In contrast, pro-vaccination profiles preferentially included words such equally "Help", "Health", "Thank you" or "Research". In club to meliorate determine the interests of the different groups, we clustered words according to topics, and found that anti-vaccination profiles were the nigh engaged in political discussion, with nearly a 6-fold increase compared with the pro-vaccination grouping (Fig 3C). Finally, we analyzed whether the use of emotional contents and language was associated with increased engagement, measured as the sum of likes, replies and retweets on each individual tweet, but constitute no significant correlation between the two factors for the anti-vaccination grouping (Fig 3D). On the reverse, the pro-vaccine group showed a significant correlation betwixt the two aforementioned factors (Fig 3D'), suggesting that the use of emotional language could aid the success of the pro-vaccination advice strategy online.
The anti-vaccination group utilizes emotional language, merely this does not determine the success of their tweets (engagement).
Nigh used words on Twitter by the anti- (cerise) and pro-vaccination groups (blueish) normalized against the words predominantly used by the command-group (grey). Asterisks* indicate that words take been clustered (e.g. "vaccine" and "vaccines" are scored equally a single word). n(profiles analyzed) = 42. Max = xviii indicates that particular word is used 18-times more in that specific grouping, when compared with the control. (A, A'). Most used words past anti- and pro-vaccination profiles normalized against each other. Asterisks* betoken clustered words. due north(profiles analyzed) = 42 (B). Words are amassed for topic and normalized, with the value of 1 existence assigned to the group utilizing the cluster of words the most. The nigh relevant clusters are shown. Words related to politics are profoundly enriched in the anti-vaccination group; words related to health and medicine are predominantly used by pro- and anti-vaccination profiles, when compared with the control; phrasal words are underrepresented in the pro-vaccination group. Asterisks* indicate amassed words. (C). For the anti-vaccination grouping, the normalized number of emotional contents (relative to the total number of contents generated by a given profile) does non correlate with the number of engagements received on average for a single tweet (R2 = 1.293*10−vi; p = 0.99); due north = 50 (D). Conversely, pro-vaccination profiles tweeting emotional content produce more engaging contents (R2 = 0.2378; p = 0.003); north = 50 (D').
Pro-vaccination supporters are more interested in their own education and profession
Previous studies showed that instruction might increase conviction in vaccine importance and effectiveness [34]. Yet, different studies reached dissimilar conclusions on whether education plays a office in shaping vaccination choice [35, 36]. We therefore decided to quantify the number of profiles associated with the three groups that declared their pedagogy or profession status. This assay does non determine whether education plays a factor in shaping vaccination choice. Nevertheless, it determines whether holding a position in the vaccination debate is associated with a self-perceived relevance of pedagogy. To determine whether the source of information is of relevance in this context, nosotros scored the number of profiles publicly declaring their name and surname, together with a seemingly real contour flick. Here we bear witness that the great majority of pro-vaccine profiles declared their identity when compared with the command (64% vs thirty%, respectively), and that anti-vaccination supporters were particularly reluctant to do so (but 16%) (S6A Fig in S1 File). Similarly, instruction and/or profession in the Twitter headline was alleged 32% of the times in the pro-vaccination group, compared with 10% and six% in the control and anti-vaccination group, respectively (S6B Fig in S1 File).
The pro-vaccination group produces the near engaging contents
Every bit we have discussed so far, the success of the anti-vaccination bulletin is not adamant by a larger production of original contents, and the use of emotional language is a structural component of this grouping that does not influence date. Here we show that the pro-vaccination group produced the most engaging contents, whereas the anti-vaccination group produced the to the lowest degree engaging contents (Pro-vaccine: xv.2 engagement per tweet; command: three.7; anti-vaccine: 0.eight) (Fig 4A). The boilerplate engagement per tweet was 19.9 times college in the pro-vaccination group when compared with the anti-vaccination grouping (and 5.5 times higher when compared with the command grouping) (Fig 4B). On average, pro-vaccination profiles were also those with a larger number of followers, when compared with control and anti-vaccination groups (mean: 1841; 605 and 338 followers, respectively) (Fig 4C). Hither we show that contents published past the pro-vaccination group were more engaging than contents produced past the bulk of anti-vaccination profiles. In light of this results, nosotros hypothesized that the success of the anti-vaccination movement is probable driven by a stronger sense of community, built around common interests (as well vaccines), and based on personal beliefs and emotional language. We therefore hypothesized the being, in this community, of a pull of influencers producing the nearly engaging contents, with the vast majority of anti-vaccination profiles functioning as the recipient and echo chamber for these messages, whereas novel contents produced by these profiles receive little attention when compared with contents generated by an boilerplate pro-vaccination supporter (illustrative scheme in Fig 4D).
Pro-vaccination profiles accept more followers and produce more than engaging content.
Pro-vaccination profiles (blue) generate more appointment in one day when compared with the control (grayness) and anti-vaccination groups (red) (A), and normalization shows they produce more engaging content irrespectively of the number of contents generated in a given day (B). Pro-vaccination profiles have a larger number of followers when compared with the control and anti-vaccination groups (C). Hypothetical model to illustrate the results described and then far. Anti-vaccination supporters are more than engaged on Twitter, as they retweet contents more often than control and pro-vaccination profiles. They also share emotional content, although they generally produce less engaging content than their pro-vaccination counterparts. Despite the use of emotions as a tool to convey their message, given the lower engagement of anti-vaccination tweets, we hypothesized that a sense of community driven by mutual interest is key for the success of the anti-vaccination movement online (D). Ordinary one-way ANOVA; ***p<0.001; ****p<0.0001; Outliers were excluded with ROUT, Q = 0.1%; n = 50.
Anti-vaccination supporters are engaged in a virtual community led past Donald Trump and other influencers
In order to determine whether the success of the anti-vaccination movement is due to the being of a community of engaged individuals driven past a pull of influencers with large follows, nosotros retrieved, for each individual profile of both the anti- and pro-vaccination group (n = 42 each), the 10 nigh retweeted profiles, and included them in our analysis. We scored the number of connections (edges; E) they established with each other by building a Twitter spider web with Cytoscape [37]. The pro-vaccination (Fig 5A) and anti-vaccination Twitter webs (Fig 5B), scaled 1:1, testify the extent of the ramifications of the latter in comparison with the sometime (Fig 5A and 5B). The size of each node (profile) is scaled linearly depending on the number of edges. Color is too indicative of the number of edges, and thus of the relevance of the node in the web (no colour: Due east<ii; yellow: 2≤E≤4; orange: 5≤E ≤9; cherry-red: Eastward ≥10). Close ups (not equally scaled, for better readability) testify the nigh relevant sections of the pro- and anti-vaccination webs (Fig 5A' and 5B'). The average number of neighbors in the web was 1.45-folds higher in the anti-vaccination spider web when compared with the pro-vaccination spider web (two.viii and two neighbors, respectively). The clustering coefficient was likewise higher in the anti-vaccination web (0.021 and 0.007, respectively), as well equally the density of the network (0.005 vs 0.003) and the characteristic path length (one.6 vs 1.4) (Fig 5C). In add-on, the pro-vaccination web had a like number of nodes and edges, whereas the anti-vaccination web had a larger number of edges than nodes. Therefore, the number of edges per nodes, which indicates the number of existing connections for each individual profile in the web, was much larger in the anti-vaccination group when compared with the pro-vaccination group (one.51 vs 1.02 connections per contour, respectively) (S7 Fig in S1 File), confirming that anti-vaccination supporters are well-connected in a community. Furthermore, with an E≥5 cut-off, nosotros identified only one large influencer in the pro-vaccination web (the World Wellness Organisation, Due east = 5) (Fig 5D), whereas, according to the aforementioned criterium, nosotros identified xiv large influencers, with the largest 1 being sometime US President Donald Trump (E = 26), 5.2 times more relevant than the World Health Organization in the pro-vaccine web. Other influencers included Trump's family members, politicians and public figures known to back up his presidency, as well as individuals and unverified popular profiles that are fully committed to the anti-vaccination crusade (Fig 5E). Therefore, here we identified the pull of relevant influencers that are probable to make up one's mind the stance about vaccine of a large number of people. These influencers include Trump–who is himself a proven anti-vaccination supporter, and others, such every bit activist Charlie Kirk or vaccine-denier Eileen Iorio.
Anti-vaccination profiles establish a well-connected community sharing contents produced by a pull of influencers, whose most prominent exponent is Donald Trump.
The pro-vaccination Twitter spider web (A). Close up of the nearly relevant portion of the pro-vaccination web, which highlights the Globe Health System every bit the main influencer for the pro-vaccination grouping (A'). The anti-vaccination Twitter spider web (B). Close up of the most relevant portion of the anti-vaccination web, which highlights Donald Trump, his political entourage and public figures supporting his presidency every bit the main influencers for the anti-vaccination grouping (B'). The pro-vaccination and anti-vaccination Twitter webs are scaled 1:i (A, B). For better readability, shut upwardly representations of the pro- and anti-vaccination webs are not every bit scaled. Yellow color represents Twitter profiles (nodes) with two to 4 anti-vaccination profiles preferentially retweeting their contents within the top 10 most retweeted users (edges; 2≤ Eastward ≤4; n = 42). Orange nodes stand for profiles with 5 to 9 edges (5≤ Eastward ≤9; north = 42), whereas red nodes signal profiles with more than 10 connecting edges (E ≥10; northward = 42). Size of the nodes is linearly scaled depending on the number of edges connecting the node (A-B'). The average number of neighbors in the web, the clustering coefficient, the density of the network and the characteristic path length of the anti-vaccination (red) web is greater than the pro-vaccination analogue (blue) (C). Graphical representation and web parameters were generated with Cytoscape. Graphical representation of the main influencers in the pro- and anti-vaccination Twitter webs (threshold: E ≥5; n = 42). The size of the name tag assigned to the Twitter contour are linearly scaled for the number of edges. The Pro-vaccination influencer cloud only contains ane profile (Earth Wellness Organization) (D), whereas the anti-Vaccination cloud contains 14 profiles, with onetime Us President Donald Trump beingness the largest influencer (E).
Discussion
The anti-vaccination community and political implications
In this paper we show that anti-vaccination supporters produce fewer original contents on Twitter simply share more contents than users belonging to the pro-vaccination or control group. However, we also bear witness that the average date, calculated as the sum of comments, likes and retweets received past an anti-vaccination tweet, is extremely depression when compared with tweets published by pro-vaccination profiles. This indicates that the majority of anti-vaccination supporters is unlikely to influence vaccination choice for a large number of individuals. Instead, we show that the success of the anti-vaccination movement online is probable based on common beliefs and interests, through which users establish a well-connected customs and constitute an echo chamber for contents generated past a smaller fraction of profiles. We define these latter users as anti-vaccination influencers. We place former US President Donald Trump as the main influencer in the anti-vaccination web. Despite him not having published directly anti-vaccination tweets in recent times, Donald Trump consistently shared anti-vaccine contents in the past, frequently associating vaccines to autism. Likewise Trump, we identify his son Donald Trump Jr, Charlie Kirk, a popular evangelical Christian and Republican activist who supported Trump's presidency, James Woods, a popular thespian and producer who is also a potent supporter of Trump–to be among the largest influencers in the anti-vaccination network. Amongst others, there are likewise profiles fully dedicated to spread the anti-vaccination message online, including authors of books on the dangers of vaccines, and not-verified profiles including Catturd2, a 'cat' who defines itself as "The MAGA turd who talks shit". Interestingly, in a recent report Trump was identified as the largest driver of the COVID-19 infodemic [28], underlining the necessity of a scientific movement that prompts politicians to base their campaigns on prove-driven policies.
The polarization of the anti-vaccine debate
Hither we demonstrate that anti-vaccination supporters share conspiracy theories, and that anti-vaccine letters can exist for a substantial part be considered as conspiracy theories themselves. This procedure is likely driven by the polarization of social media feed, where users are exposed to information, news and views identified by algorithms every bit close to their interests. In fact, a contempo study observed an increasing polarization of anti-vaccination contents on social media [38]. Conspiracy theories of various kinds, as well as anti-vaccination beliefs and political extremism tend to be associated with each other [39, 40]. Every bit nosotros previously mentioned, Donald Trump, despite being an anti-vaccination supporter, has not discussed vaccination bug in similar terms during his presidency. Notwithstanding, prior to the suspension of his Twitter profile, he retained the indirect ability to influence the peachy majority of individuals associated with the anti-vaccination motion. Due to the polarization of the debate on social media, sharing or reading conservative tweets could increment the chance that a hesitant person gets in touch with anti-vaccination beliefs. In line with this, information technology was previously shown that anti-vaccine users grade a polarized network with little to no interaction with outsiders, in which users strengthen their positions past sharing each other's contents [41–43]. We therefore strongly encourage social media to change the polarized way they nowadays information to users to halt the anti-vaccination infodemic and increase debate between communities. Nosotros welcome initiatives to suspend profiles that clearly share misinformation about scientific topics and are probable to have significant negative effects on club. Anti-vaccination influencers could yet be targeted in other means, too. These deportment include 'shadow bans' for science-based contents–which could force a tweet's organic accomplish to drop (i.e. a modest number of people would read the content); info banners for tweets containing unverified data near medically-sensitive topics could too be effective tools to limit the spread of misinformation about vaccines. Finally, we encourage social media and the scientific community to discuss the possible introduction of science knowledge tests, which could be required for users that intend to share contents containing medically-sensitive data. These tests could inform users almost vaccines and other scientific topics, thus probable reducing the amount of circulating fake news, without imposing an a priori brake of individual freedom of voice communication. Furthermore, as the force of the anti-vaccination movement relies on the construction of its customs and the existence of social media as a tool, health organizations should consider restructuring decisional pathways to place solutions in line with the times. These could include involving citizens in decision-making processes, thus edifice a more engaged community when information technology comes to public health policy. Straight involvement of citizens in these processes could be complicated but they should at least be given the risk to voice their concerns and influence decision-making. Furthermore, wellness organizations could foyer 'indirect' anti-vaccination influencers to become active pro-vaccination communicators. The value of positive influencers has been proven in a pilot study using a social network for Multiple Sclerosis patients [44], and their presence could counteract problems related to the lack of editorial review and fact-checking on social media [45]. Positive influencers should include celebrities, as they tin can influence online searches of health-related information [46], and their voices could aid public wellness efforts, including vaccination campaigns [47]. A combination of the aforementioned approaches could transform social media from sources of misinformation to valuable tools to gather trustworthy, relevant news and knowledge.
Towards a amend advice strategy for vaccinations
Finally, here we testify that the apply of people-centered, first-person narratives with emotional linguistic communication could aid the advice strategy of pro-vaccine health organizations and individuals. The ability of first-person narratives over population-based statistical evidence could be due to an effect known in psychology as "psychic numbing", according to which the higher the number of people involved in a disaster and the least people feel empathic near it. Personal stories, involving get-go person narratives, are more attractive and stimulate empathic responses more than efficiently [48–50]. Given that this type of communication seems to be a structural component within the anti-vaccination community, it may be required for users to build stiff connections. We therefore encourage health organizations to adopt a less sterile, technical language when communicating with the general public. This language should exist scientifically sound, but also unproblematic, emotional and understandable. At the same time, adopting a pro-active long-term strategy for increasing the full general public's science literacy and ability to read and understand at least basic scientific information will be an important complementary strategy.
Supporting information
S2 File
Pro-vaccination network.
(PDF)
S3 File
Anti-vaccination network.
(PDF)
Funding Statement
FG and NBA were funded by the Swiss National Scientific discipline Foundation, NCCR Molecular Systems Engineering grant.
Data Availability
All relevant information are within the paper and its Supporting data files.
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PONE-D-20-39758
The anti-vaccination infodemic on social media: a
behavioral analysis
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Reviewer #one: The anti-vaccination infodemic on social media: a behavioral analysis
This article is well-written, informative and based on a robust methodology.
I have post-obit comments:
- Authors may further hash out their results relying on recent studies on the same topic: Vaccines (Basel). 2021 Jan seven;ix(i):E28. and PLoS I. 2020 October 8;15(10):e0239826.
- Authors can also refer to and hash out other infodemiological studies related to attitudes towards vaccines: J Prev Med Hyg. 2016;57(1):E47-l. and Hum Vaccin Immunother. 2017 Feb;13(ii):464-469.
- Authors could advise some ways to fight confronting unsubstantiated vaccin hesitancy and "faux news", and to provide more than accurate informations (the aforementioned social media could be exploited as a source of accurate wellness-related informations, as shown in other fields; see for instance: Mult Scler Relat Disord. 2018 Oct;25:175-178. - Mult Scler. 2018 Nov;24(13):1657-1664 - Interact J Med Res. 2017 Sep 27;half dozen(2):e18. - An Acad Bras Cienc. 2019 Feb 14;91(suppl i):e20180149)
- Incidentally, regarding the influence of Donald Trump on vaccine misinformation on Twitter, something similar has been observed in other fields and is worth mentioning (Clin Colon Rectal Surg. 2017 Sep;thirty(4):270-276. ). It is very well known that celebrities´statements on wellness can deeply influence the online searches for health-related data; this is not necessarily bad, but could (and should) exist used to improve public wellness (for instance by involving these celebrities as testimonals: J Public Health (Oxf). 2015 Sep;37(3):555-6
Reviewer #ii: Really expert and interesting work! I suggest merely specify Donald Trump as ex President, co-ordinate to last results from presidential election in U.s.a. recently concluded. Please, add together some comments focused on possible bear upon of social media on institutional decisional pathway
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Reviewer #2:Yes:Giovanna Borriello
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Author response to Decision Letter 0
10 February 2021
Response to reviewers
Nosotros would similar to thank both reviewers for their positive comments on our manuscript, and for providing additional resources to improve our text.
Reviewer #1
"- Authors may farther discuss their results relying on recent studies on the same topic: Vaccines (Basel). 2021 Jan 7;9(i):E28. and PLoS One. 2020 Oct 8;fifteen(10):e0239826.
- Authors can also refer to and discuss other infodemiological studies related to attitudes towards vaccines: J Prev Med Hyg. 2016;57(1):E47-50. and Hum Vaccin Immunother. 2017 February;13(two):464-469."
The first paper (Vaccines (Basel). 2021 Jan 7;9(ane):E28) suggests the polarization of contents on social media has been increasing over the past decade, and information technology is an important addition to our discussion on the contribution of the polarization of contents on social media to the anti-vaccination debate. The post-obit ii papers (PLoS 1. 2020 Oct 8;15(10):e0239826 and J Prev Med Hyg. 2016;57(1):E47-fifty) were discussed in the introduction section, as they depict how Twitter is a valuable tool to study trends on vaccine hesitancy and obtain public wellness data of relevance. They serve equally a justification for the option to use Twitter in our study.
"- Authors could propose some means to fight against unsubstantiated vaccine hesitancy and "fake news", and to provide more than accurate informations (the same social media could be exploited as a source of accurate health-related informations, as shown in other fields; encounter for instance: Mult Scler Relat Disord. 2018 Oct;25:175-178. - Mult Scler. 2018 November;24(13):1657-1664 - Collaborate J Med Res. 2017 Sep 27;6(2):e18. - An Acad Bras Cienc. 2019 Feb 14;91(suppl one):e20180149)"
We cited the first of the suggested studies as an case of the importance of influencers in shaping the quality of contents on social media and their accurateness from a scientific standpoint.
"- Incidentally, regarding the influence of Donald Trump on vaccine misinformation on Twitter, something like has been observed in other fields and is worth mentioning (Clin Colon Rectal Surg. 2017 Sep;30(4):270-276. ). It is very well known that celebrities´statements on health tin securely influence the online searches for health-related information; this is not necessarily bad, but could (and should) be used to ameliorate public health (for instance past involving these celebrities every bit testimonals: J Public Health (Oxf). 2015 Sep;37(3):555-6"
Nosotros particularly thank the reviewer for this comment, as nosotros fully agree influencers and celebrities could play a key role as trustworthy sources of data on social media. We discussed this in the section entitled "The polarization of the anti-vaccine debate" in the discussion and mentioned both papers suggested by the reviewer (Clin Colon Rectal Surg. 2017 Sep;30(four):270-276 and J Public Health (Oxf). 2015 Sep;37(3):555-6). The kickoff report does non demonstrate that Trump is a social media influencer in other contexts other than vaccines, although for this we already cited a recent assay from Cornell identifying Trump as the major source of COVID-nineteen related misinformation on social media (Evanega et al 2020). The Clin Colon Rectal Surg. 2017 Sep;30(iv):270-276 paper is all the same a valuable source which nosotros included in our manuscript equally it points to the problem of the lack of editorial review and fact-checking on social media. In this context, influencers could be of use as suggested by the reviewer and discussed in this newspaper: Mult Scler Relat Disord. 2018 October;25:175-178.
Reviewer #2
"I propose simply specify Donald Trump as ex President, co-ordinate to last results from presidential election in Usa recently ended. Delight, add some comments focused on possible impact of social media on institutional decisional pathway"
Nosotros changed all formulations referring to Trump from "US president" to "old US President", and included comments on the possible bear on of social media on controlling processes within public health institutions (also suggesting a couple of potential solutions).
Zipper
Submitted filename: Response to reviewers.pdf
Decision Letter 1
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The anti-vaccination infodemic on social media: a behavioral analysis
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15 February 2021
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The anti-vaccination infodemic on social media: a behavioral analysis
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