Google News and machine gatekeepers: algorithmic personalisation and news diversity in online news search.

Abstract

Through a mixed methods research design, we address normative aspects of news recommendation engines by examining whether search personalisation and news diversity are evident on Google News in the UK. Firstly, in a quasi-experimental design, we asked a diverse set of participants (N=78) to search Google News using four search terms and report the first five articles recommended for each term. We found little evidence of news personalisation, which challenges the claim that news search algorithms contribute to weakened viewpoint diversity. We also found a high degree of homogeneity in news search results, with legacy media brands dominating. Secondly, we conducted a manual content analysis of the articles recommended by Google News for our search terms (N=192), focusing on favourability towards each term. We found that while there was little relationship between the favourability slant of the articles and political leanings of participants, there were two exceptions: self-identified right-wing participants were more likely to see unfavourable stories about 1) immigration, and 2) a left-wing politician. This reopens the question of news search engines’ contributions to polarisation and viewpoint diversity for certain news consumers.