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MediaFutures Seminar: Modeling News Flows: How Feedback Loops Influence Citizens’ Beliefs and Shape Societies. Assoc. Prof. Damian Trilling.
2 December, 2021 @ 12:00 - 13:00
Damian Trilling, Associate Professor at the University of Amsterdam, will give a seminar on 2 December, at 12:00.
TITLE: Modeling News Flows: How FeedbackLoops Influence Citizens’ Beliefs and Shape Societies
WHEN: Thursday 2 December, 12:00-13:00
WHERE: Zoom : https://uib.zoom.us/j/62205578637?pwd=azNkZ21UY3V6cE5KYTVJRGxmclZOQT09#success
Meeting ID: 622 0557 8637
Password: YT2sX3MZ
ABSTRACT:
In both public and scientific debates, many worry that the architecture of our current (online) media environment leads to so-called “echo chambers” or “filter bubbles”. In this talk, I will challenge these metaphors and argue that they distract us from a deeper understanding of the underlying, reinforcing processes. First, I will discuss the role of recommender systems and other algorithmic systems in news exposure and dissemination, and under which circumstances the feedback loops they produce can indeed have detrimental consequences for society. Second, I will discuss feedback loops as they occur between different outlets and domains. This results in the outline a research agenda. I will end on an optimistic note, though, and argue how a stronger role of communication science can help developing beneficial tools for our society.
BIO:
Damian Trilling is Associate Professor at the Department of Communicaton Science at the University of Amsterdam), where he is Co-Director of the Communication in the Digital Society Initiative and member of the program group Political Communication and Journalism. He is also one of the co-founders of the Computational Communication Science Amsterdam Lab. Next to general courses in political communication, he teaches intensively in the area of Computational Communication Science. His research focuses on the question how news spreads in today’s media landscape and how people follow the news. In particular, he is interested in reinforcing processes, such as the question in how far exposure and distribution re-inforce each other, and how the users and algorithms interact in such processes.