Researchers have cracked how the successive recommendations generated by Google-owned Youtube’s algorithm sometimes “confine” us in a bubble of similar content.
The researchers at the Centre Marc Bloch-Franco-German Research Centre for the Social Sciences in Germany, studied this phenomenon by exploring recommendations from a thousand videos on different subjects, thereby running through half a million recommendations.
“We focused on YouTube which has become a major online content provider but where confinement has until now been little-studied in a systematic manner.”
“We aim to contribute to the above literature by showing whether recommendation on YouTube exhibits phenomena typical of filter bubbles, tending to lower the diversity of consumed content,” they added in a paper published in the journal PLOS ONE.
The findings showed that contrary to the algorithms of other platforms, which seem to promote the exploration of novelty and serendipity, YouTube’s is actually an exception, generating a number of confinement phenomena.
A user’s navigation based on recommendations can be seen as a movement within a network of interconnected videos: by starting out from a particular video, the recommendation network is more or less closed, in other words, it leads to content that is more or less similar and redundant..
In addition, the content that leads to the most confined recommendation networks also seems to revolve around the most viewed videos or the ones with the longest viewing time.
“So far, this work appears to concur on the fact that platforms rather tend to expand the navigation landscape of users. Here, the mean-field of YouTube recommendations shows up as an exception,” the researchers noted.
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