Political context of the European vaccine debate on Twitter

Abstract

At the beginning of the COVID-19 pandemic, fears grew that making vaccination a political (instead of public health) issue may impact the efficacy of this life-saving intervention, spurring the spread of vaccine-hesitant content. In this study, we examine whether there is a relationship between the political interest of social media users and their exposure to vaccine-hesitant content on Twitter. We focus on 17 European countries using a multilingual, longitudinal dataset of tweets spanning the period before COVID, up to the vaccine roll-out. We find that, in most countries, users' endorsement of vaccine-hesitant content is the highest in the early months of the pandemic, around the time of greatest scientific uncertainty. Further, users who follow politicians from right-wing parties, and those associated with authoritarian or anti-EU stances are more likely to endorse vaccine-hesitant content, whereas those following left-wing politicians, more pro-EU or liberal parties, are less likely. Somewhat surprisingly, politicians did not play an outsized role in the vaccine debates of their countries, receiving a similar number of retweets as other similarly popular users. This systematic, multi-country, longitudinal investigation of the connection of politics with vaccine hesitancy has important implications for public health policy and communication.

Figure 1

Vaccine Hesitancy Endorsement (VHE) score (see Algorithm 1) distribution across countries and periods. Dashed grey lines indicate scores where a user’s exposure has equal shares of vaccine-hesitant and pro-vaccine content. Score near 1 indicates more hesitant, and 0 – more pro-vaccine. Country abbreviations are ISO 3166 standard: AT Austria, BE Belgium, CZ Czech Republic, DK Denmark, FI Finland, FR France, DE Germany, GR Greece, IE Ireland, IT Italy, NL Netherlands, PL Poland, PT Portugal, SE Sweden, ES Spain, CH Switzerland, GB United Kingdom.

Figure 2

Distribution (boxplots) of OLS coefficients modeling users’ Vaccine Hesitancy Endorsement (VSE) score (see Algorithm 1) by their interest in parties, grouped by families using ParlGov, an extensive resource on political parties in parliamentary democracies. Positive (negative) coefficients indicate that users who follow these parties are more (less) likely to engage with vaccine-hesitant rather than pro-vaccine content on Twitter. Accompanying points and whiskers indicate a 99% bootstrapped confidence interval. Numbers indicate how many parties are in each group. The parties identified as Right-wing have the strongest, and most positive, relationship with the VHE score.

Figure 3

Significant OLS coefficients (at $p<0.01$ with Bonferroni correction) modeling the users’ VHE score using user interest in political parties, grouped in families using ParlGoV. Shaded areas indicate the 99% confidence intervals. Showing countries having sufficient model fit over 4 time periods. For the most part, we find the effect sign of a political party family to remain consistent over time.

Figure 4

Distribution (boxplots) of OLS coefficients modeling users’ VHE score by their interest in parties having one of four dimensions defined by ParlGov, grouped in quintiles. Accompanying points and whiskers indicate a 99% bootstrapped confidence interval. Horizontal brackets on top indicate the comparisons among quintiles 1, 3 and 5, * signifies whether one distribution is statistically greater than the other (one-sided Mann-Whitney U test at $p<0.01$ with Bonferroni correction).

Figure 5

Spearman correlation between political interest and VHE score (left) and political focus and VHE score (right) (see RQ2 Methods). Grey cells indicate country/periods having fewer than 300 users. White cells indicate a non-significant correlation.

Algorithm 1

Assigning users’ VHE score