Social media today is a dominant communication tool, which structures not only our social interactions but also filter the information users are getting displayed. The big social media platforms use our interaction data to analyse our behaviour and sell the data for commercial interest. But not only the pure interaction data is valuable for these platforms. Also hidden information, which can be derived from our interactive networks, about our social structures, social classifications and social status are gathered and monetised. This research attempts on the one hand to uncover some of these methods used by social media platforms, and on the other hand, also wants to show how useful these new methods can be for research on social phenomena. Therefore, this study goes beyond the confining limits of traditional sociology, where either qualitative or quantitative methods are applied. Following the idea of Critical Realism, the positivist and constructivist methods are applied in combination in order to provide thick accounts of the studied material. In this study, varying socioeconomic classification systems (like the Sinus-Milieu models) are investigated and evaluated against the background of Bourdieu’s ideas on cultural and social forms of capital. The present study uses a mixed method approach (Social Network Analysis and Sentiment Analysis) to analyse quantitative data from Twitter conversations which were collected during the Austrian EU Election 2019. In conclusion, one could say that the overall purpose of this study is to demonstrate the usefulness of Critical Realism for social media research, since this approach can create a thicker account of the studied material than other, more traditional methods. This undertaking is demonstrated by the findings of the study. These findings are the building of specific sub-clusters of EU candidates which are not related to the same political background and traditional demographics but whose relation can be detected and described using Bourdieu’s concepts of social and cultural capital. As a mean for gathering empirical data, Twitter turned out to be a useful and accessible tool for this study.