The COVID-19 infodemic does not affect vaccine acceptance

How does information consumption affect behaviour in the context of the COVID-19 pandemic? A popular hypothesis states that the so-called infodemics has substantial impact on orienting individual decisions [1, 2]. A competing hypothesis stresses that exposure to vast amounts of even contradictory information has little effect on personal choices [3, 4]. We analyse the vaccine infodemics on Twitter and Facebook by looking at 146M contents produced by 20M accounts between 1 January 2020 and 30 April 2021. We find that vaccine-related news triggered huge interest through social media, affecting attention patterns and the modality in which information was spreading. However, we find that such a tumultuous information landscape translated in only minimal variations in vaccine acceptance as measured by Facebook’s daily COVID-19 World Symptoms Survey [5] on a sample of 1.6M users. This finding supports the hypothesis that altered information consumption patterns are not a reliable predictor of behavioural change. Instead, wider attention on social media seems to resolve in polarisation, with the vaccine-prone and the vaccine-hesitant maintaining their positions. 1 ar X iv :2 10 7. 07 94 6v 1 [ ph ys ic s. so cph ] 1 6 Ju l 2 02 1 Main Social media platforms have radically changed how we access information and are often used as a proxy to understand the social mood and opinion trends. Users online tend to acquire information adhering to their beliefs and ignore dissenting information [6, 7, 8]. Such a process, combined with the unprecedented amount of information available online, has fostered the emergence of groups of like-minded individuals framing and reinforcing a shared narrative (i.e., echo chambers) [9, 10, 11, 12, 13]. Furthermore, recent studies provided evidence for the effect of feed algorithms in bursting polarisation of social dynamics [14, 15]. Such a scenario may be considered a fertile environment for misinformation spreading, and an eventual threat for democracies [16, 17]. However, the interplay between online information diffusion and offline users’ behaviour is still an open scientific question, with fundamental societal implications especially during the ongoing pandemic [1, 18]. Indeed, the World Health Organization raised concerns about the effects of the so-called “infodemics”, defined as “overabundance of information some accurate and some not that occurs during an epidemic” [19], on global health. Two main hypotheses compete in accounting for the impact of information consumption on human behaviour [1]. The first states that infodemics has a substantial impact on orienting individual decisions [1, 2]. The second view stresses that exposure to vast amounts of even contradictory information has little effect on personal choices [3, 4]. Here, we analyse the news diet of 20M unique users on Facebook and Twitter over 16 months (from 1 January 2020 to 30 April 2021) to investigate the relationship between online discussions about COVID-19 vaccines and offline vaccine acceptance rate, in 6 European countries: Denmark, France, Germany, Italy, Spain, and United Kingdom. To measure the offline effect in these countries, we consider Facebook’s daily COVID-19 World Symptoms Survey [5] on a sample of 1.6M users. Considering the overall observation period, we find that vaccine announcements triggered users’ engagement on social media massively. Meanwhile, we do not observe significant variations in vaccine acceptance rates. Such a finding is validated by considering the vaccine acceptance rate in other 38 countries worldwide. Focusing on the temporary suspension of the AstraZeneca (now Vaxzevria) vaccine issued by several EU countries, our analysis shows only minimal variations in the vaccine acceptance curves. Evolution of the Vaccine Debate We analyse the social media debate around vaccine-related topics on Facebook and Twitter, collecting a large corpus of posts selected via keyword search (see Methods). First, we perform topic modelling on the dataset, employing a Deep Learning based approach (see Methods). In this way, we can assign posts to different arguments while studying their evolution over time. In Figure 1AB we report the most debated topics. Consistently with the keyword search, most of them are related to the vaccines and the general vaccination campaign.