The CCL will be hosting the two researchers from the division for Computational Communication Research at LMU Munich this fall, Johanna Schindler and Patrick Parschan. During their visits in late September and early October, both will share their research in our lab meetings, offering their perspectives on the complex dynamics of political communication and media studies. Their work explores critical issues at the intersection of communication, media, and politics, with Johanna focusing on political polarization and Patrick on online content consumption and media monitoring. Below, you can find more information about their upcoming presentations and their impressive academic backgrounds.

• Date of presentation: October 2, 15:00 (SR 10, Kolingasse 14-16) 
• External participants need to register with Sarah Epp-Kampl and can also join via Zoom.

Johanna Schindler (Visiting from September 24 to October 3):
Title: The Differential Impact of Disagreement on Political Polarization. Conceptualization and Simulation Across Interpersonal, Mass Media, and Social Media Contexts (together with Mario Haim)
Abstract:
Political polarization is regarded as a major challenge to modern democracies. Research has shown that crosscutting exposure can play an ambivalent role in this process, acting as both a mitigating and reinforcing factor. We argue that the extent of disagreement is critical, with moderate disagreement holding a special potential to reduce polarization, while strong disagreement tends to exacerbate it. This differentiation is particularly relevant within multi-party systems. We propose a theoretical framework that integrates the role of different (dis-)agreement levels within interpersonal communication, mass media, and social media contexts as key settings for contact with political opinions. Our framework provides a foundation to simulate polarization dynamics through agent-based modeling. With our work, we aim to better understand the influence of specific factors such as media system characteristics or the design of social media platforms in polarization processes.

Bio:
Dr. Johanna Schindler is a postdoctoral researcher and research associate at the division for Computational Communication Research at LMU Munich. She received her Ph.D. from Leipzig University in 2022 with a dissertation on the collective processing of media content. Her research focuses on group phenomena in communication contexts, especially related to media use and effects, digital communication, and political communication.

Patrick Parschan (Visiting from September 30 to October 4):
Title: Introducing the Munich Media Monitoring (M3) project
Abstract:
Understanding the intricate online content consumption and user interaction is crucial in the dynamic world of online media and algorithmically curated media environments. The Munich Media Monitoring (M3) offers a nuanced resource to social science by providing an up-to-date analysis of textual online media content through regularly updated patterns of online media use.

M3 is divided into three core pillars:

• The content pillar collects and analyzes media content from news outlets and social media platforms, providing comprehensive views of the media landscape.
• The use pillar categorizes media-use patterns of the broader population, using surveys and tracking data to understand user behavior and preferences.
• The encounter pillar connects content and users, simulating digital agents to describe media content exposure patterns based on various usage factors.

Given this background, the project emphasizes building a social-scientific research infrastructure from the ground up, serving multiple use cases such as regular monitoring reports, early detection systems, and open-source repositories.

Bio:
Patrick Parschan is a Ph.D. student in Computational Communication Research at the Department of Media and Communication at Ludwig-Maximilians University in Germany. He is the Student and Early Career Representative of the Computational Methods Division of the International Communication Association. In his research, he combines spatial models of politics with modern Natural Language Processing to measure the issue positions of political actors.