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      Device-Dependent Biases in Mobile Online News

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          Abstract

          One in four German internet users claims that search engines are their main gateway to news and a majority of Germans reports to primarily use their smartphone over their laptop/desktop computer to access news online. Yet, search-engine providers such as Google have repeatedly pointed out to actively favor specific forms of technical content optimization for mobile devices (e.g., Accelerated Mobile Pages), raising the question of whether this preference results in biases toward mobile-optimized content. In light of regulatory changes, this study investigates source diversity and source prominence in news items related to a range of issues presented to users when searching for news-related subjects via a smartphone or laptop/desktop computer in Germany. Using agent-based testing, 75,767 searches were performed on Google in mid-2020, applying a dynamic set of search terms on a range of different topics. Results indicate significant discrepancies in source diversity and source prominence between smartphone and laptop/desktop computers that can largely be attributed to mobile-optimized content likely to reach primarily younger news consumers who favor mobile devices. Overall, however, source diversity is equally high on both types of devices, highlighting the necessity for future research to focus on algorithmic influences on societies shared understandings of relevance beyond source diversity.

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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
          : 1
          Affiliations
          LMU Munich
          ZeMKI, University of Bremen
          Article
          10.5117/CCR2023.1.14.HAIM
          10.5117/CCR2023.1.14.HAIM
          208df0b0-503f-419a-86f5-8da166be4eb2
          © 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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          Article

          agent-based testing,outlet similarity,source diversity,news,search engines,online journalism,media use

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