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      Algorithmic Recommendations’ Role for the Interrelatedness of Counter-Messages and Polluted Content on YouTube – A Network Analysis

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          Abstract

          Counter-messages are used by civil education, youth prevention actors, and security agencies to counter the magnitude of polluted content. On the Internet, algorithmic operations of intermediaries affect how users encounter and receive polluted content. As counter-messages often show similar keywords, algorithms establish connections between counter-messages and polluted content, primarily because they share mutual topics. Against the background of legislative attempts to stop the spread of extremist online content, this paper aims to further investigate the interrelatedness of counter-messages and polluted content on YouTube due to the platform’s recommendation algorithm. To that end, two information network analyses were conducted based on each five seed videos of two differently designed counter-message campaigns one year after their publication on YouTube in 2019. Five thousand four hundred of the 35,982 videos of the two networks were analyzed qualitatively and manually. Results show that counter-messages are indirectly strongly connected to more polluted content. We further identify the campaigns’ design and setup on YouTube as factors that can cause the interrelatedness between counter-messages and polluted content.

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          Author and article information

          Contributors
          Journal
          CCR
          Computational Communication Research
          Amsterdam University Press (Amsterdam )
          2665-9085
          2665-9085
          2023
          : 5
          : 1
          : 109
          Affiliations
          Department of Media and Communication, LMU Munich
          Department of Media and Communication, LMU Munich
          Article
          CCR2023.1.005.ZIER
          10.5117/CCR2023.1.005.ZIER
          8316f5ff-c794-413d-a151-cb65f1049081
          © The author(s)

          This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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          Information network analysis; YouTube; Algorithms; Counter-messages; Polluted content

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