Rens Vliegenthart, Professor of Strategic Communication at WUR, ODISSEI 

Anne Kroon, Associate Professor Corporate Communication at UvA, ODISSEI

Roadmap task leaders

There are many obstacles to digital text analysis. On the one hand, copyright and GDPR restrictions make it very hard to share media content data by providing a workbench for researchers where they can share data and analyses with strict rights management without the need to read or export the data themselves. On the other hand, recent initiatives to facilitate retrieval, storage, and analysis of media content require considerable programming skills and are currently tailored to a limited set of applications. The ODISSEI Media Content Analysis Lab (MCAL) tackles these challenges and aims to improve accessibility to data and tools in the field of communication science. 

The MCAL team built the MCALentory, a searchable inventory of publications on media content analysis published in various communication science journals since 2001. At the moment the inventory covers all studies which (partially) focus on the Netherlands and have been published in the 30 highly ranked communication journals. Aside from generic metadata such as journal title/ISSN, publication date and author information, it presents information on the time period studied in the article, as well as the type of material that is studied (e.g. newspapers, television, online (social) media), and the countries that are included. As there are many different aspects to media content that can be analyzed, the dataset also provides an indication of what content features are studied, and a classification of the research question/goal (e.g. descriptive research, effects research, explaining the content that is found). Details on the used methods are also present in the MCALentory, such as whether the study uses qualitative, quantitative (with manual coding), or computational methods. Additionally, details are provided on the measures used to evaluate intercoder reliability, and the availability of replication materials, such as (raw) data, code and algorithms.

With the collaboration of the PDI-SSH FAIR Expertise Hub and computer scientists at VU Amsterdam, the MCALentory dataset has been transformed into and published as a knowledge graph on the Triply platform. This allows users to easily query the data and to create “data stories” to provide insights into the field. Moreover, a few controlled vocabularies have been created in the process and will be made available soon via the SSHOC-NL vocabulary registry. Overall, this effort contributes to improving the uptake of the FAIR principles in the field. 

The information provided by the MCALentory facilitates researchers when conducting literature reviews of content analysis studies, but is also capable of informing them where gaps in our knowledge of (news) media content can be found. As such, the goal of MCAL is to keep track of the multitude of content analysis studies that have been and continue to be conducted by scholars all around the world. We believe this is relevant especially in a context where not only the amount of content itself, but also the (computational) tools available for analysis lead to unparalleled options for researchers to conduct new studies. In the coming period, the inventory will be regularly updated and extended with studies beyond the Dutch context. The work done on the MCALentory will facilitate the integration of Media Content Analysis tools, data and workflows within the wider ODISSEI ecosystem.

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