Discourse analysis in the tradition of the sociology of knowledge is a research methodology for gaining an understanding of social orders and how they have emerged over time. Usually, the methodology of discourse analysis and discourse ethnography is hardly standardised and Grounded Theory procedures, such as theoretical sampling and saturation, guide the research process from the collection of materials for the data corpus to analyses of this data. In times of digital media, materials for discourse analyses are available more and more in digital formats, and at the same time in a growing and usually multitudinous number - often called big data. This raises questions about how the hermeneutic processes of discourse analysis can be supported by digital methods. The working paper firstly points out main concepts of discourse analysis and grounded theory as an important pillar within this methodology. Secondly, possibilities of automatization of qualitative methodologies and questions about the efficiency of automatizing research processes are discussed. The paper then thirdly focusses on explorations into potentials of automatizing for discourse analysis: finding and selecting relevant expressions in the discourse through crawling / data mining and supporting our coding processes, specifically open coding through structured methods of annotation and text analysis.