In this research, we contribute to increasing knowledge about how social circles impact opinion dynamics and we provide new insights on how opinion dynamics might contribute to automatically identifying social circles. To this end, we use the majority model as baseline model. We introduce a deterministic social circle approach to include social circle information into opinion dynamics simulation models. To evaluate the impact of social circles on the final distribution of opinions and the correlation between the final distribution of opinions and social circles, we introduce a metric of social consensus that translates the final level of agreement between individuals. We found that adding social circle information to opinion dynamics models results in a significantly lower final level of consensus than the baseline model which does not use social circle information. In addition, we conclude that there is a correlation between the final distribution of opinions and social circles. By comparing the mean level of consensus per social circle and per community, we could affirm that the correlation between the final distribution of opinions within social circles is higher than within communities. With this research we extend the knowledge of the role of communities in opinion dynamics to social circles.