Advancing media history by transparent automatic genre classification
Online and mobile news consumption leaves digital traces that are used to personalize news supply, possibly creating filter bubbles where people are exposed to a low diversity of issues and perspectives that match their preferences. Filter bubbles can be detrimental for the role of journalism in democracy and are therefore subject to considerable debate among academics and policymakers alike.
The existence and impact of filter bubbles are difficult to study because of the need to gather the digital traces of individual news consumption; to automatically measure issues and perspectives in the consumed news content; and to combine and analyse these heterogeneous data streams.
We will develop a mobile app to trace individual news consumption and gather user data (WP1); create a custom NLP pipeline for automatically identifying a number of indicators of news diversity in the news content (WP2); integrate and analyze the resulting heterogeneous data sets (WP3); and use the resulting rich data set to determine the extent to which news recommender algorithms and selective exposure leads to a lower diversity of issues and perspectives in the filter bubbles formed by news supplied to and consumed by different groups of users (WP4).
This allows us to determine the impact of biased and homogenous news diets on political knowledge and attitudes. The software developed in this project will be open source and re-usable outside the scope of this project by scholars interested in mobile behavior and news production and consumption.