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

arXiv: https://arxiv.org/abs/2401.09804 · HTML: https://mlsanity.com/paper/2401.09804/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 26 of 26)

- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [How Do Classifiers Induce Agents To Invest Effort Strategically?](https://mlsanity.com/paper/1807.05307/) (2018)
- [Strategic Classification in the Dark](https://mlsanity.com/paper/2102.11592/) (2021)
- [Information Discrepancy in Strategic Learning](https://mlsanity.com/paper/2103.01028/) (2021)
- [Maximizing Welfare with Incentive-Aware Evaluation Mechanisms](https://mlsanity.com/paper/2011.01956/) (2020)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers](https://mlsanity.com/paper/1806.00955/) (2018)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach](https://mlsanity.com/paper/2008.00104/) (2020)
- [What are you optimizing for? Aligning Recommender Systems with Human Values](https://mlsanity.com/paper/2107.10939/) (2021)
- [The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization](https://mlsanity.com/paper/2202.11776/) (2022)
- [On classification of strategic agents who can both game and improve](https://mlsanity.com/paper/2203.00124/) (2022)
- [How Bad is Top-K Recommendation under Competing Content Creators?](https://mlsanity.com/paper/2302.01971/) (2023)
- [The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity](https://mlsanity.com/paper/2002.00558/) (2020)
- [From Optimizing Engagement to Measuring Value](https://mlsanity.com/paper/2008.12623/) (2020)
- [Strategic Ranking](https://mlsanity.com/paper/2109.08240/) (2021)
- [Learning with Exposure Constraints in Recommendation Systems](https://mlsanity.com/paper/2302.01377/) (2023)
- [Recommending to Strategic Users](https://mlsanity.com/paper/2302.06559/) (2023)
- [Online Learning in a Creator Economy](https://mlsanity.com/paper/2305.11381/) (2023)
- [Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems](https://mlsanity.com/paper/2305.17428/) (2023)
- [Incentivizing High-Quality Content in Online Recommender Systems](https://mlsanity.com/paper/2306.07479/) (2023)
- [Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation](https://mlsanity.com/paper/2311.15647/) (2023)
- [Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems](https://mlsanity.com/paper/2309.00940/) (2023)
- [Matching of Users and Creators in Two-Sided Markets with Departures](https://mlsanity.com/paper/2401.00313/) (2023)
- [Social Status and Badge Design](https://mlsanity.com/paper/1312.2299/) (2013)

## Built on by (most cited first, 13 of 13)

- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Contractual Reinforcement Learning: Pulling Arms with Invisible Hands](https://mlsanity.com/paper/2407.01458/) (2024)
- [How to Strategize Human Content Creation in the Era of GenAI?](https://mlsanity.com/paper/2406.05187/) (2024)
- [Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content](https://mlsanity.com/paper/2405.05596/) (2024)
- [Societal Impacts Research Requires Benchmarks for Creative Composition Tasks](https://mlsanity.com/paper/2504.06549/) (2025)
- [The Burden of Interactive Alignment with Inconsistent Preferences](https://mlsanity.com/paper/2510.16368/) (2025)
- [Algorithmic Content Selection and the Impact of User Disengagement](https://mlsanity.com/paper/2410.13108/) (2024)
- [Bandits with Single-Peaked Preferences and Limited Resources](https://mlsanity.com/paper/2510.09425/) (2025)
- [DREAMS: A Social Exchange Theory-Informed Modeling of Misinformation Engagement on Social Media](https://mlsanity.com/paper/2602.01567/) (2026)
- [Emotion-Aware Clickbait Attack in Social Media](https://mlsanity.com/paper/2604.27369/) (2026)
- [LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems](https://mlsanity.com/paper/2606.02883/) (2026)
- [Market Design for AI: Beyond the Copyright Binary](https://mlsanity.com/paper/2606.12260/) (2026)
- [AgenticGen: Reward-Guided Agentic Video Generation for Advertising](https://mlsanity.com/paper/2609.09187/) (2026)

## 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.