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Optimal Engagement-Diversity Tradeoffs in Social Media

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  • Fabian Baumann
  • Daniel Halpern
  • Ariel D. Procaccia
  • Iyad Rahwan
  • Itai Shapira
  • Manuel Wuthrich

Abstract

Social media platforms are known to optimize user engagement with the help of algorithms. It is widely understood that this practice gives rise to echo chambers\emdash users are mainly exposed to opinions that are similar to their own. In this paper, we ask whether echo chambers are an inevitable result of high engagement; we address this question in a novel model. Our main theoretical results establish bounds on the maximum engagement achievable under a diversity constraint, for suitable measures of engagement and diversity; we can therefore quantify the worst-case tradeoff between these two objectives. Our empirical results, based on real data from Twitter, chart the Pareto frontier of the engagement-diversity tradeoff.

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  • Fabian Baumann & Daniel Halpern & Ariel D. Procaccia & Iyad Rahwan & Itai Shapira & Manuel Wuthrich, 2023. "Optimal Engagement-Diversity Tradeoffs in Social Media," Papers 2303.03549, arXiv.org.
    • Handle: RePEc:arx:papers:2303.03549
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    References listed on IDEAS

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    1. Dokyun Lee & Kartik Hosanagar, 2019. "How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment," Service Science, INFORMS, vol. 30(1), pages 239-259, March.
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