Guest Speaker - Michael Reiss

“Challenges to investigating online news consumption and online news avoidance using computational methods”

Abstract

A growing availability of digital behavioral data as well as an increase in computing power has made many online phenomena accessible for quantitative study. This also applies to online news consumption and the avoidance thereof. A first part of this talk will present such an inquiry, covering the systematic investigation of non-use of online news by combining tracking and automated text classification. Relying on tracking data of Swiss Internet users, two computational approaches were applied to identify news consumption at the domain and article level. This led to a precise picture of Swiss online news use and on the extent of non-users of online news. A special focus is on the methodical aspects of the work. Building on these insights, a second part of the talk goes beyond the article and discusses implications and challenges for the current research of news consumption as well as limitations to computational methods in the context of researching news consumption online.

Bio

Michael Reiss has been a PhD candidate in the Media Change & Innovation Division at the Department of Communication and Media Research, University of Zurich (Switzerland) since September 2018. Previously he completed the master’s programs Socio-Ecological Economics and Policy at the Vienna University of Economics (Austria) and Social Research Methods at the London School of Economics and Political Science (United Kingdom). Currently he is engaged in a Swiss National Science Foundation research project on algorithmic selection in everyday life. His research interests involve news consumption and methods of computational social science.