Media Consumption

To what extent does the YouTube recommendation algorithm push users into echo chambers, rabbit holes, or radicalization pathways? Using a novel method to estimate the ideology of YouTube videos and an original audit design, we find that YouTube users experience mild ideological echo chambers, but these appear to be driven primarily by user behavior.  We also demonstrate that the recommendation algorithm prioritizes content that is similar to what the user is currently watching, producing “content rabbit holes”. However, we do not find evidence of radicalization pathways where users are driven into increasingly extreme content rabbit holes. Instead, we find that YouTube’s recommendations move users, regardless of ideology, toward moderately conservative content and an increasingly narrow range of ideological content the longer they follow YouTube’s recommendations.

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 pools of 100 posts across 54 experimental conditions, we find that biases differ substantially in how structural and how prompt-sensitive they are. Polarization is amplified across all configurations, toxicity handling shows a strong inversion between engagement- and information-focused prompts, and sentiment biases are predominantly negative. Provider comparisons reveal distinct trade-offs: GPT-4o Mini shows the most consistent behavior across prompts; Claude and Gemini exhibit high adaptivity in toxicity handling; Gemini shows the strongest negative sentiment preference. On Twitter/X, where author demographics can be inferred from profile bios, political leaning bias is the clearest demographic signal: left-leaning authors are systematically over-represented despite right-leaning authors forming the pool plurality in the dataset, and this pattern largely persists across prompts.