mlsanityData to 7 Oct 2026

Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media

arXiv 2305.16941 ↗First posted 26 May 2023cs.SI cs.CYcited by 30

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Abstract (arXiv)

In a pre-registered algorithmic audit, we found that, relative to a reverse-chronological baseline, Twitter's engagement-based ranking algorithm amplifies emotionally charged, out-group hostile content that users say makes them feel worse about their political out-group. Furthermore, we find that users do not prefer the political tweets selected by the algorithm, suggesting that the engagement-based algorithm underperforms in satisfying users' stated preferences. Finally, we explore the implications of an alternative approach that ranks content based on users' stated preferences and find a reduction in angry, partisan, and out-group hostile content, but also a potential reinforcement of pro-attitudinal content. The evidence underscores the necessity for a more nuanced approach to content ranking that balances engagement and users' stated preferences.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 May 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 9; 2026: 17.

30 papers.

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  2. 2024AI Alignment with Changing and Influenceable Reward Functionscited by 33
  3. 2025How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfarecited by 20
  4. 2024HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twittercited by 12
  5. 2025Meaning Is Not A Metric: Using LLMs to make cultural context legible at scalecited by 10
  6. 2026Agents in the Wild: Safety, Society, and the Illusion of Sociality on Moltbookcited by 10
  7. 2025Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Valuecited by 6
  8. 2026Position: Anthropomorphic Misalignment Research Needs Stronger Evidencecited by 6
  9. 2025Value Alignment of Social Media Ranking Algorithmscited by 4
  10. 2025CTRL-Rec: Controlling Recommender Systems With Natural Languagecited by 4
  11. 2025LLMs Can Get "Brain Rot": A Pilot Study on Twitter/Xcited by 2
  12. 2024Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Spherecited by 1
18 more
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  2. 2025The Problem of Algorithmic Collisions: Mitigating Unforeseen Risks in a Connected Worldcited by 1
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  4. 2026Measuring and Mitigating Persona Distortions from AI Writing Assistancecited by 1
  5. 2026Solipsistic Superintelligence is Unlikely to be Cooperativecited by 1
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  7. 2026Measuring Narrative Polarization in Online Discoursecited by 0
  8. 2026Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategiescited by 0
  9. 2026When Nobody Around Is Real: Exploring Public Opinions and User Experiences On the Multi-Agent AI Social Platformcited by 0
  10. 2026Recommender system in X inadvertently profiles ideological positions of userscited by 0
  11. 2026LLM Active Alignment: A Nash Equilibrium Perspectivecited by 0
  12. 2026FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Healthcited by 0
  13. 2026Attention Asymmetry in AI Layoff Discourse on X: A Computational Analysis of Capital vs Labour Amplificationcited by 0
  14. 2026LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systemscited by 0
  15. 2026Position: AI Must Become Planet-Centered, Not Just Human-Centeredcited by 0
  16. 2026Using AI Agents to Automate Black-Box Audits of Personalization Algorithms at Scalecited by 0
  17. 2026Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systemscited by 0
  18. 2026Agent-based Modeling: Equilibrium, Echo Chambers, and Efficiency in Hybrid Coevolutionary Opinion Gamescited by 0