Element 68Element 45Element 44Element 63Element 64Element 43Element 41Element 46Element 47Element 69Element 76Element 62Element 61Element 81Element 82Element 50Element 52Element 79Element 79Element 7Element 8Element 73Element 74Element 17Element 16Element 75Element 13Element 12Element 14Element 15Element 31Element 32Element 59Element 58Element 71Element 70Element 88Element 88Element 56Element 57Element 54Element 55Element 18Element 20Element 23Element 65Element 21Element 22iconsiconsElement 83iconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsElement 84iconsiconsElement 36Element 35Element 1Element 27Element 28Element 30Element 29Element 24Element 25Element 2Element 1Element 66

Media Research Methods Lab (MRML)

The Media Research Methods Lab (MRML) is designed as a method-oriented lab, which focuses on linking established social science methods (surveys, observations, content analysis, experiments) with new 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 as well as new data sources promises the best results for the empirical investigation of current challenges and developments in media change.

Communication data and the opportunities for computer-aided analysis, simulation and visualisation of complex interaction systems are increasing. This can be seen in the easier access to digitally left traces and in easy-to-create online surveys. The "truth" suggested by numbers and visualisations is often gratefully taken up, especially in politics and journalistic reporting, without questioning how the data was created, collected and evaluated and what resilience is associated with it.

Classifying and correcting misleading or misunderstood results is tedious and usually does not achieve the same public attention. However, only scientific methodology can distinguish reliable findings from unsystematic everyday assertions. Hence, ensuring quality in data collection and analysis is also essential in communication science.


Objectives

The MRML has three interrelated objectives:

1. To advance the development of methods in media research
The MRML promotes method development 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 from 2020-2024 will be the establishment and support of the (Social) Media Observatory (SMO) within the Research Institute Social Cohesion (RISC).

Within the SMO, the MRML will not only deal with the collection of data from journalistic and social media, but also with the exploration and development as well as the transfer of innovative methods for their analysis.

Together with researchers from the Department of Computer Science at the University of Leipzig, the DFG-funded project A Framework for Argument Mining and Evaluation (FAME) investigates how argument mining in natural language processing in combination with formal argument evaluation in the field of logic can be used for empirical analyses of argument usage patterns.

2. Methodological support within the institute
The 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 programme areas of the HBI through consulting and methodological infrastructure, for example in the field of digital trace analysis. In addition, the MRML expands the methodological competence at the institute through regular training of the staff.

3. Reflection and impulses for the scientific community and non-scientific actors
On the one hand, the MRML aims to stimulate discussion in the scientific community and thus generate more attention for methodological aspects. On the other hand, it aims to contribute to public discussions on current research and in particular address non-scientific actors who are interested in methodological classification.

Spokesperson
Dr. Gregor Wiedemann
Dr. Sacha Hölig

Researchers
Dr. Felix Münch
Jan Rau

Newsletter

Subscribe to our newsletter and receive the Institute's latest news via email.

SUBSCRIBE!