Personalization and Diversity in Algorithmic News Recommendations

Algorithms are playing a growing role in determining which news stories reach audiences as they increasingly assist or replace humans in the distribution and curation of news. This development begs the question: What kind of information landscapes are algorithmic platforms creating for individuals and communities as they direct millions of people to news via recommendation engines and search interfaces? Indeed, such engines and interfaces are currently among the main sources of traffic to news sites, making them crucial objects for study.

This study will compare thousands of real-world news searches conducted by a large and diverse set of participants across different digital services (e.g., Google, YouTube and Facebook) in order to gain insight into patterns of news distribution on the most popular algorithmically driven gatekeeping platforms. It will examine if news search algorithms promote filter bubbles and fragmented audiences as feared by some scholars and in popular media—or, alternatively, if they perhaps construct relatively homogeneous and uniform news landscapes online.

The authors' previous study with the Tow Center indicated that, on Google News, people of different political leanings and backgrounds were recommended highly similar news diets regarding the 2016 U.S. presidential election, sourced primarily from a small number of mainstream national outlets. This challenged the assumption that algorithms invariably encourage echo chambers while disrupting power structures within media industries. This follow-up project expands these research questions into a broader set of platforms and topics on the news, paying special attention to the role that local news sources are assigned in these environments.

Project leads: Seth C. Lewis, Efrat Nechushtai, Rodrigo Zamith