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Skewed perspectives: examining the influence of engagement maximization on content diversity in social media feeds

Author

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  • Paul Bouchaud

    (EHESS/CAMS
    CNRS/ISCPIF)

Abstract

This article investigates the information landscape shaped by curation algorithms that seek to maximize user engagement. Leveraging unique behavioral data, we trained machine learning models to predict user engagement with tweets. Our study reveals how the pursuit of engagement maximization skews content visibility, favoring posts similar to previously engaged content while downplaying alternative perspectives. The empirical grounding of our work provides a basis for evidence-based policies aimed at fostering responsible social media platforms.

Suggested Citation

  • Paul Bouchaud, 2024. "Skewed perspectives: examining the influence of engagement maximization on content diversity in social media feeds," Journal of Computational Social Science, Springer, vol. 7(1), pages 721-739, April.
    • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-024-00255-w
    DOI: 10.1007/s42001-024-00255-w
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    References listed on IDEAS

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    3. Hossein Noorazar & Kevin R. Vixie & Arghavan Talebanpour & Yunfeng Hu, 2020. "From classical to modern opinion dynamics," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(07), pages 1-60, July.
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