In our recent research published in Scientific Reports (Springer Nature) we analyzed data from more than 140 countries to explore why neutrals are usually more attracted by the anti-vaccine side. Our analysis included cutting-edge methods from Social Influence, Agent-Based Modeling and Attitude Networks!
We know that the more similar two people are, the more they will influence each other. The “pro-vaccine” are too far from the neutrals. However, people in between can be close to both, connecting the two in a chain of influence.
We analyzed this idea of the chain by using attitudes networks. By exploring the network we found that countries with a weaker chain performed worse in terms of vaccination coverage and trust.
Agent-Based Model of social influence helped us in connecting the data with the theory. Using them we confirmed that social influence models would predict as well worse results when the chain is weak. Furthermore, they highlighted the dynamic nature of the process!
Indeed, countries with a weaker chain will not immediately perform worse, but only in the following year. This is due to the fact that trust needs time to spread across the system.
So if we are planning policies to improve trust we should be careful in doing so without breaking the chain. If this happens, we may observe an immediate increase in trust, but this may be quickly eroded as we left the neutrals behind.
In our paper we also simulate that it is possible to make policies which will boost trust while even enhancing the link with the neutrals!
If you are interested check out the actual paper:
