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Digital Media & Computational Methods

About us / Mission statement

The research group of Digital Media & Computational Methods focuses on communication platforms and the development of automated data collection and analysis methods.
We aim to understand the interaction of users’ behaviour, mediation of (online) platforms and scientific data analysis. Using computational methods for automated data collection and analysis we cross disciplines and build bridges between qualitative and quantitative research perspectives. In this context, we develop and explore research software for social sciences and humanities.

  • Staff

    Head of the group: Jun. Prof. Dr. Jakob Jünger


    Student assistants: Isabel Kleefeld, Henrieke Kotthoff, Vicky Wagemann, Pauline Müller, Manuela Hinz, Maximilian Michel

    The involved persons work at different sites such as in Münster, Mainz, Munich or Greifswald and are institutionally affiliated at the University of Münster and/or the Academy of Science and Literature in Mainz.

  • Projects

    • LIKE: When is a Like a Like? Data-Generating Processes in Online Communication
    • EPIGRAF: A platform for the curation, annotation, linking and publication of communication data.
    • FACEPAGER: An application for automated data retrieval on the web
  • Research Field Platform Logics

    Communication platforms, such as Facebook, YouTube or Telegram, mediate between users on the one hand and, on the other hand, shape the online ecosystem with technical surfaces and social rules. We deal with the question of how different forms of public and non-public communication settings emerge from coordinated behaviour. Further, we investigate how platform logics influence behaviour, for example via recommendation systems. By contrasting different functions and online platforms as well as communication setting outside the online world, we take a comparative perspective.

  • Research Field Computational Methods

    In the social sciences and te humanities, computational methods refer to procedures such as data collection via APIs, network analysis, natural language parsing, machine learning, and computer simulations. We explore the insights gained by these methods in the computational social sciences and the digital humanities. Despite the analysis of big data, we work with non-representative and small samples. A central challenge of automation is not to lose sight of individual cases. We take a datahermeneutic perspective and deal with methodological questions about data quality and with epistemological questions.