Vaccines save millions of lives every year. They are recommended by experts, trusted by the majority of people, and promoted by expensive health campaigns. Even so, people with neutral attitudes are more persuaded by people holding anti-vaccine than pro-vaccine attitudes. Our analysis of vaccine-related attitudes in more than 140 countries makes sense of this paradox by including approaches from social influence. Specifically, we show that neutral people are positioned closer to anti- than to pro-vaccine people in the opinion space, and therefore more persuadable by them. We use dynamic social simulations seeded with vaccine survey data, to show how this effect results in a drift towards anti-vaccine opinions. Linking this analysis to data from two other multi-country datasets, we found that countries in which the pro-vaccine people are less associated to the neutrals (and so less able to influence them) exhibit lower vaccination rates and stronger increase in distrust. We conclude our paper by showing how taking social influence into account in vaccine-related policy-making can possibly reduce waves of distrust towards vaccination.

Opinion dynamics models have huge potential for understanding and addressing social problems where solutions require the coordination of opinions, like anthropogenic climate change. Unfortunately, to date, most of such models have little or no empirical validation. In the present work we develop an opinion dynamics model derived from a real life experiment. In our experimental study, participants reported their opinions before and after social interaction using response options "agree" or "disagree," and opinion strength 1 to 10. The social interaction entailed showing the participant their interaction partner's agreement value on the same topic, but not their certainty. From the analysis of the data, we observed a very weak, but statistically significant influence between participants. We also noticed three important effects. (1) Asking people their opinion is sufficient to produce opinion shift and thus influence opinion dynamics, at least on novel topics.(2) About 4% of the time people flipped their opinion, while preserving their certainty level. (3) People with extreme opinions exhibited much less change than people having neutral opinions. We also built an opinion dynamics model based on the three mentioned phenomena. This model was able to produce realistic results (i.e. similar to real-world data) such as polarization from unpolarized states and strong diversity.

We apply a newly developed attitude network‐modelling technique (Response‐Item Network, or ResIN) to study attitude–identity relationships in the context of hot–button issues that polarize the current US‐American electorate. The properties of the network–method allow us to simultaneously depict differences in the structural organization of attitudes between groups and to explore the relevance of organized attitude–systems for group identity management. Individuals based on a sample of US‐American crowd workers (N = 396) and the representative 2020 ANES data set (N = 8280), we model an attitude network with two conflictive partisan belief‐systems. In the first step, we demonstrate that the structural properties of the attitude‐network provide substantial information about latent partisan identities, thereby revealing which attitudes ‘belong’ to specific groups. In a second step, we evaluate the potential of attitudes to communicate identity‐relevant information. Results from a vignette study suggest that people rely on their mental representations of attitude‐identity links to structure and evaluate their social environment. By highlighting functional interdependences between (macro level) attitude structures and identity management, the presented findings help advancing the understanding of attitude‐identity dynamics and socio‐political cleavages.

No abstract

No abstract