Google Recommendations: A Digital Jail Cell?
May 5, 2020
A team of researchers in at the Centre Marc Bloch in Berlin have closely studied filter bubbles (scientifically called “confinement”) on YouTube. While the phenomenon of filter bubbles across the Web has been a topic of study for several years, scientists Camille Roth, Antoine Mazieres, and Telmo Menezes felt the role of the recommendation algorithm on YouTube had been under-examined. In performing research to plug this gap, they found the dominant video site may produce the most confining bubbles of all. The team shares their main results in “Tubes and Bubbles: Topological Confinement of Recommendations on YouTube.” They summarize:
“Contrarily to popular belief about so-called ‘filter bubbles’, several recent studies show that recommendation algorithms generally do not contribute much, if at all, to user confinement; in some cases, they even seem to increase serendipity [see e.g., 1, 2, 3, 4, 5, 6]. Our study demonstrates however that this may not be the case on YouTube: be it in topological, topical or temporal terms, we show that the landscape defined by non-personalized YouTube recommendations is generally likely to confine users in homogeneous clusters of videos. Besides, content for which confinement appears to be most significant also happens to garner the highest audience and thus plausibly viewing time.”
The abstract to the team’s paper on the study describes their approach:
“Starting from a diverse number of seed videos, we first describe the properties of the sets of suggested videos in order to design a sound exploration protocol able to capture latent recommendation graphs recursively induced by these suggestions. These graphs form the background of potential user navigations along non-personalized recommendations. From there, be it in topological, topical or temporal terms, we show that the landscape of what we call mean-field YouTube recommendations is often prone to confinement dynamics.”
To read about the study in great, scientific detail, complete with illustrations, turn to the full paper published at the PLOS ONE peer-reviewed journal site. Established in 2012, The Centre Marc Bloch’s Computational Social Science Team enlists social scientists alongside computer scientists and modelers to study the social dynamics of today’s digital landscapes. If you are curious what that means, exactly, their page includes an interesting five-minute video describing their work.
Cynthia Murrell, May 5, 2020
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