mlsanityData to 7 Oct 2026

Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms

arXiv 2401.09804 ↗First posted 18 Jan 2024cs.GT cs.CY cs.LGcited by 13

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Abstract (arXiv)

Online content platforms commonly use engagement-based optimization when making recommendations. This encourages content creators to invest in quality, but also rewards gaming tricks such as clickbait. To understand the total impact on the content landscape, we study a game between content creators competing on the basis of engagement metrics and analyze the equilibrium decisions about investment in quality and gaming. First, we show the content created at equilibrium exhibits a positive correlation between quality and gaming, and we empirically validate this finding on a Twitter dataset. Using the equilibrium structure of the content landscape, we then examine the downstream performance of engagement-based optimization along several axes. Perhaps counterintuitively, the average quality of content consumed by users can decrease at equilibrium as gaming tricks become more costly for content creators to employ. Moreover, engagement-based optimization can perform worse in terms of user utility than a baseline with random recommendations, and engagement-based optimization is also suboptimal in terms of realized engagement relative to quality-based optimization. Altogether, our results highlight the need to consider content creator incentives when evaluating a platform's choice of optimization metric.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 26 of its 50 references that are in the map. First posted on arXiv 18 Jan 2024.

Builds on

26 of 50 references.

References · 26

  1. 2020Performative Predictioncited by 318
  2. 2015Strategic Classificationcited by 256
  3. 2018How Do Classifiers Induce Agents To Invest Effort Strategically?cited by 82
  4. 2021Strategic Classification in the Darkcited by 66
  5. 2021Information Discrepancy in Strategic Learningcited by 48
  6. 2020Maximizing Welfare with Incentive-Aware Evaluation Mechanismscited by 46
  7. 2022Modeling Content Creator Incentives on Algorithm-Curated Platformscited by 35
  8. 2018A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providerscited by 33
  9. 2022Supply-Side Equilibria in Recommender Systemscited by 33
  10. 2020Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approachcited by 32
  11. 2021What are you optimizing for? Aligning Recommender Systems with Human Valuescited by 28
  12. 2022The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimizationcited by 28
14 more
  1. 2022On classification of strategic agents who can both game and improvecited by 27
  2. 2023How Bad is Top-K Recommendation under Competing Content Creators?cited by 27
  3. 2020The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexitycited by 26
  4. 2020From Optimizing Engagement to Measuring Valuecited by 21
  5. 2021Strategic Rankingcited by 21
  6. 2023Learning with Exposure Constraints in Recommendation Systemscited by 8
  7. 2023Recommending to Strategic Userscited by 8
  8. 2023Online Learning in a Creator Economycited by 8
  9. 2023Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systemscited by 7
  10. 2023Incentivizing High-Quality Content in Online Recommender Systemscited by 6
  11. 2023Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendationcited by 6
  12. 2023Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystemscited by 5
  13. 2023Matching of Users and Creators in Two-Sided Markets with Departurescited by 5
  14. 2013Social Status and Badge Designcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 3; 2026: 5.

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  1. 2024Accounting for AI and Users Shaping One Another: The Role of Mathematical Modelscited by 11
  2. 2024How to Strategize Human Content Creation in the Era of GenAI?cited by 4
  3. 2024Contractual Reinforcement Learning: Pulling Arms with Invisible Handscited by 4
  4. 2024Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Contentcited by 3
  5. 2025Societal Impacts Research Requires Benchmarks for Creative Composition Taskscited by 3
  6. 2025The Burden of Interactive Alignment with Inconsistent Preferencescited by 1
  7. 2024Algorithmic Content Selection and the Impact of User Disengagementcited by 0
  8. 2025Bandits with Single-Peaked Preferences and Limited Resourcescited by 0
  9. 2026DREAMS: A Social Exchange Theory-Informed Modeling of Misinformation Engagement on Social Mediacited by 0
  10. 2026Emotion-Aware Clickbait Attack in Social Mediacited by 0
  11. 2026LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systemscited by 0
  12. 2026Market Design for AI: Beyond the Copyright Binarycited by 0
1 more
  1. 2026AgenticGen: Reward-Guided Agentic Video Generation for Advertisingcited by 0