Abstract
In offering personalized content geared toward users’ individual interests, recommender systems are assumed to reduce news diversity and thus lead to partial information blindness (i.e., filter bubbles). We conducted two exploratory studies to test the effect of both implicit and explicit personalization on the content and source diversity of Google News. Except for small effects of implicit personalization on content diversity, we found no support for the filter-bubble hypothesis. We did, however, find a general bias in that Google News over-represents certain news outlets and under-represents other, highly frequented, news outlets. The results add to a growing body of evidence, which suggests that concerns about algorithmic filter bubbles in the context of online news might be exaggerated.
| Dokumententyp: | Zeitschriftenartikel |
|---|---|
| Keywords: | diversity; experiment; filter bubble; online news; personalization |
| Fakultät: | Sozialwissenschaften > Kommunikationswissenschaft |
| Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 070 Publizistische Medien, Journalismus, Verlagswesen
300 Sozialwissenschaften > 300 Sozialwissenschaft, Soziologie |
| ISSN: | 1025-9473 |
| Sprache: | Deutsch |
| Dokumenten ID: | 72018 |
| Datum der Veröffentlichung auf Open Access LMU: | 25. Sep. 2020, 06:12 |
| Letzte Änderungen: | 04. Nov. 2020, 13:52 |
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