{"id":2195,"date":"2024-06-25T18:06:51","date_gmt":"2024-06-25T16:06:51","guid":{"rendered":"https:\/\/newws.leibniz-hbi.de\/hbi-researchprograms\/media-research-methods-lab\/"},"modified":"2024-06-27T16:11:26","modified_gmt":"2024-06-27T14:11:26","slug":"media-research-methods-lab","status":"publish","type":"hbi-researchprograms","link":"https:\/\/leibniz-hbi.de\/en\/hbi-researchprograms\/media-research-methods-lab\/","title":{"rendered":"Media Research Methods Lab"},"content":{"rendered":"

The Media Research Methods Lab (MRML)<\/span> bundles the methodological expertise of the HBI in an organizational unit that combines established social science methods with innovative digital processes.<\/p>\n

The Media Research Methods Lab (MRML) is designed as a method-oriented lab that combines established social science methods (survey, observation, content analysis, experiment) with innovative digital methods from the field of computational social science (e.g. automated content analysis, network analysis, log data analysis, experience sampling) across topics and disciplines. The integration of established and new methods and data sources promises the best results for the empirical investigation of current challenges and developments in media change.<\/p>\n

The availability of communication data and the possibilities of computer-aided analysis, simulation and visualization of complex interaction systems are increasing. This manifests itself, for example, in easier access to digital traces and easy-to-create online surveys. The “truth” suggested by catchy figures and visualizations is often gratefully taken up, especially in politics and journalistic reporting, without questioning how the data was generated, collected and evaluated and what reliability is associated with it.<\/p>\n

Classifying and correcting misleading or misunderstood results is laborious and usually does not achieve the same level of public attention. But it is scientific methodology that distinguishes reliable findings from unsystematic everyday assertions. This makes ensuring quality in data collection and analysis essential in communication science as well.<\/p>\n

Goals<\/h2>\n

Specifically, the MRML pursues three interlinked objectives:<\/p>\n

    \n
  1. Advancing the development of media research methods
    \nThe MRML is driving forward the development of methods through its own research. The Institute’s core competencies in communication science and regulatory science form the frame of reference. A central project of the MRML in the period 2020-2024 will be the establishment and supervision of the (Social) Media Observatory (SMO) within the Research Institute for Social Cohesion (RISC)<\/a>.
    \nWithin the framework of the SMO, the MRML will not only deal with the collection of data from journalistic and social media, but also with the exploration, development and transfer of innovative methods for their analysis.
    \nThe DFG-funded project A Framework for Argument Mining and Evaluation (FAME), together with researchers from the Department of Computer Science at Leipzig University, is investigating how argument mining in the field of natural language processing can be fruitfully integrated with formal argument evaluation in the field of logic for empirical analyses of argument usage patterns.<\/li>\n
  2. Methodological support within the institute
    \nThe MRML bundles the methodological expertise available at the institute in order to promote the integration of classical and novel methods of data collection and processing. The MRML supports other projects and program areas of the HBI by providing advice and methodological infrastructure, for example in the field of digital trace analysis. The MRML is also expanding methodological expertise at the institute through regular staff training.<\/li>\n
  3. Reflection and impetus for the scientific community and non-scientific stakeholders
    \nOn the one hand, the MRML aims to stimulate discussion within the scientific community and thus generate more attention for methodological aspects. On the other hand, it would like to contribute to public discussions on current studies and, in particular, address non-scientific stakeholders who are interested in a methodological classification.<\/li>\n<\/ol>\n

    Sprecher: Dr. Gregor Wiedemann,<\/a> Dr. Sascha H\u00f6lig<\/a><\/p>\n

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