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DFG Project „Public Communication about Transnational Competitive Events in Social Media and its Predictive Power"“

Institution:

Division Digitalization and Participation
in collaboration with the Research Group Digital Communication and Transformation at University Duisburg-Essen

Research Team:

Student Assistants:

  • Lisa Maria Hölker
  • Sara Rigotti
Funding:

DFG Sachbeihilfen

Contact Person:
Florian Buhl

Research Focus of the Past Project Phases:

Within the framework of interdisciplinary cooperation between communication studies and computer science, in the past project phases the topic dynamics in the Internet public sphere using automated and manual methods was investigated. By "topic dynamics" we mean the diffusion and processing of topics. The Internet creates new conditions for these topic dynamics and has greater transparency than older media, which simplifies empirical analysis. Patterns of topic dynamics were first examined using the example of the microblogging service Twitter, which lent itself as a research subject for technical and structural reasons. Individual topics were identified with the help of keyword lists, which were used to continuously collect and evaluate communication on specific topics. During the second phase of the project, the scope of observation was extended to include professional journalistic websites, which made it possible to analyze references and spill-over effects between the two platforms.

Research Focus of the Current Project Phase:

In the context of the current project phase 3, an extension in two directions will take place: First, the observational focus will be extended to Facebook and blogs, and second, public communication about transnational, periodic competitive events in three social subsystems will be analyzed, with predictive models developed based on the data.

Duration of the research project: 2012 - November 2022

Keywords

  • Internet public sphere, social media, topic setting, transnational public communication, online journalism, media diffusion processes, predictive models.