Business

Archaeology

Personalization

History

Filter (signal processing)

Internet privacy

Advertising

World Wide Web

Political science

Information retrieval

Data science

Diversity (politics)

Context (archaeology)

Computer science

Computer vision

Digital Journalism

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.

Burst of the Filter Bubble?