The echo-chamber effect is a common term in opinion dynamic modeling, where it describes how person's opinion might be artificially enhanced as it is reflected back at her through social interactions.Here, we study the existence of this effect in statistical mechanics models which are commonly used to study opinion dynamics. By exploring the influence of the network on a spin, we find that this influence can be described using an effective field which is independent of the external field on the spin itself. Using this effective field approach, we then show that there are no echo-chambers in the Ising model, but that this is a consequence of a special symmetry which is not common in other models. We then distinguish between three types of models: (i) those with a strong echo-chamber symmetry, that have no echo-chamber effect at all; (ii) models with a weak echo-chamber symmetry that can have echo-chambers only if there are no external fields in the system, and (iii) models without echo-chamber symmetry that generally include echo-chambers. The effective field method is then used to construct an efficient algorithm to find the mean magnetization in tree networks. We apply this algorithm to study two systems: phase transitions in the random field Ising model on a Bethe lattice, and the influence optimization problem in social networks.