Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation
Large Language Models (LLMs) are increasingly deployed to curate and rank human-created content, yet the nature and structure of their biases in these tasks remains poorly understood: which biases are robust across providers and platforms, and which can be mitigated through prompt design. We present a controlled simulation study mapping content selection biases across three major LLM providers (OpenAI, Anthropic, Google) on real social media datasets from Twitter/X, Bluesky, and Reddit, using six prompting strategies (\textit{general}, \textit{popular}, \textit{engaging}, \textit{informative}, \textit{controversial}, \textit{neutral}). Through 540,000 simulated top-10 selections from pool...
| Authors | Nicolò Pagan, Christopher Barrie, Chris Andrew Bail et al. |
|---|---|
| Primary category | cs.SI |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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