Аннотация:In the backdrop of interests in social media news and polarization the aim of this study is to examine to what extent we are exposed to the same "news" in the News Feed? The article defines news not from a classical position but from the content that is judged relevant to the user and made visible by the algorithm. The study examines filter bubbles as information similarity and more specifically nonoverlapping content segments in a unique snapshot dataset of 14 days of personal Facebook News Feeds for 1,000 Danes mirroring the Danish Facebook population. Deploying methods to analyze both link sources and content semantics the study finds that less than 10% in the link source analysis and 27.8% in the semantic analysis are in a filter bubble. The article tests and discusses suitable conceptual and empirical thresholds for information similarity that can inspire future studies. The best significant predictors for being in a filter bubble are what we term sociality: number of page likes, group memberships and friends. The study does not find age, gender, education or area of residence isolated to be significant predictors of participants in filter bubbles.
