Optimal Engagement-Diversity Tradeoffs in Social Media
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Abstract (arXiv)
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 chambersemdash 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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2023.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2026Social physics in the age of artificial intelligencecited by 4
- 2023Rebalancing Social Feed to Minimize Polarization and Disagreementcited by 2
- 2025Dynamics of Algorithmic Content Amplification on TikTokcited by 1
- 2023Disincentivizing Polarization in Social Networkscited by 0
- 2026Social physics in the age of artificial intelligence4 Mar 2026
- 2025Dynamics of Algorithmic Content Amplification on TikTok26 Mar 2025
- 2023Rebalancing Social Feed to Minimize Polarization and Disagreement28 Aug 2023
- 2023Disincentivizing Polarization in Social Networks23 May 2023