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Tubes and bubbles topological confinement of YouTube recommendations
Camille Roth1,2, Antoine Mazières1, Telmo Menezes1
1CNRS, Centre Marc Bloch, Computational Social Science team, Berlin, Germany.
Plos One
|April 22, 2020
Summary
YouTube recommendations can create filter bubbles, limiting content diversity. This study reveals confinement dynamics in non-personalized suggestions, often centered around popular videos.
Area of Science:
- Information Science
- Computer Science
- Media Studies
Background:
- The impact of recommendation algorithms on user behavior, specifically online user confinement, is a growing research area.
- Existing studies suggest filter bubbles are more prevalent in explicit (user-declared preferences) than implicit (user activity) recommendation systems.
- YouTube, a major content platform, has been understudied regarding systematic analysis of recommendation-induced confinement.
Purpose of the Study:
- To investigate whether YouTube's recommendation system exhibits filter bubble phenomena, leading to reduced content diversity.
- To analyze the properties of suggested videos and construct recommendation graphs to model potential user navigation paths.
- To assess confinement dynamics in non-personalized YouTube recommendations using topological, topical, and temporal analyses.
Main Methods:
- Exploration of video suggestion sets starting from diverse seed videos.
- Design of an exploration protocol to capture latent recommendation graphs.
- Analysis of recommendation graph properties (topological, topical, temporal) to identify confinement dynamics.
- Investigation of the relationship between confined recommendation graphs and video audience/viewing time.
Main Results:
- YouTube's mean-field recommendations demonstrate a propensity for confinement dynamics, reducing content diversity.
- Confinement is observed across topological, topical, and temporal dimensions of the recommendation landscape.
- Highly confined recommendation graphs, indicative of filter bubbles, are associated with videos attracting significant audience and viewing time.
Conclusions:
- Non-personalized recommendation algorithms on YouTube can lead to user confinement and filter bubble effects.
- The structure of YouTube's recommendation system, particularly around popular content, facilitates these confinement dynamics.
- Understanding these dynamics is crucial for addressing potential limitations on content diversity and user exposure on large online platforms.
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