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2020 | OriginalPaper | Buchkapitel

Riding the Wave of Misclassification: How We End up with Extreme YouTube Content

verfasst von : Christian Stöcker, Mike Preuss

Erschienen in: Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis

Verlag: Springer International Publishing

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Abstract

Content recommendations on big internet platforms such as YouTube are supported by crowd-based recommender systems. Users are presented with what other users in comparable situations spent time on. There are several examples, however, where sequences of suggested content quickly degenerated towards only slightly related, sometimes problematic content that we define as contextually inappropriate. We try to identify the basis of this effect from both the user and the technical side, and set up a simple simulation system in order to better understand the interactions. Simulation results provide evidence that autoplay is an especially problematic feature, but that completely preventing inappropriate suggestions is technically very hard if not infeasible because it is the nature of a recommendation system to take into account feedback from the user and adapt to it. We also propose some possible measures to mitigate the problem.

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Vorheriges Kapitel Catfishing: A Look into Online Dating and Impersonation

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Titel: Riding the Wave of Misclassification: How We End up with Extreme YouTube Content
verfasst von: Christian Stöcker
Mike Preuss
Verlag: Springer International Publishing
Buch: Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis
Print ISBN: 978-3-030-49569-5

Electronic ISBN: 978-3-030-49570-1

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-49570-1_25

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