Tubes and bubbles topological confinement of YouTube recommendations
| Autor: | Antoine Mazières, Telmo Menezes, Camille Roth |
|---|---|
| Přispěvatelé: | Centre Marc Bloch (CMB), Ministère de l'Europe et des Affaires étrangères (MEAE)-Bundesministerium für Bildung und Forschung-Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.)-Centre National de la Recherche Scientifique (CNRS), Centre d'Analyse et de Mathématique sociales (CAMS), École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), ANR-15-CE38-0001,ALGODIV,Algodiv: Recommandation algorithmique et diversité des informations du web(2015), European Project: 772743,Socsemics, Mazieres, Antoine, Algodiv: Recommandation algorithmique et diversité des informations du web - - ALGODIV2015 - ANR-15-CE38-0001 - AAPG2015 - VALID, Socio-semantic bubbles of internet communities - Socsemics - 772743 - INCOMING |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
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| Zdroj: | PLoS ONE, Vol 15, Iss 4, p e0231703 (2020) PLoS ONE PLoS ONE, Public Library of Science, 2020, 15 (4), pp.e0231703 PLOS ONE PLoS ONE, 2020, 15 (4), pp.e0231703 |
| ISSN: | 1932-6203 |
| Popis: | The role of recommendation algorithms in online user confinement is at the heart of a fast-growing literature. Recent empirical studies generally suggest that filter bubbles may principally be observed in the case of explicit recommendation (based on user-declared preferences) rather than implicit recommendation (based on user activity). We focus on YouTube which has become a major online content provider but where confinement has until now been little-studied in a systematic manner. 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. Moreover, the most confined recommendation graphs i.e., potential bubbles, seem to be organized around sets of videos that garner the highest audience and thus plausibly viewing time. Comment: 10 pages, 7 figures, 1 table |
| Databáze: | OpenAIRE |
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