Twitter/X

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.

What happens to information environments when democracies ban social media platforms? While a large literature examines information control under authoritarianism, democratic governments have increasingly intervened in major online platforms. We study a prominent case: Brazil’s 2024 national ban on the social media platform X. Using an event-study design, we estimate the causal effects of the ban and examine how partisan identity shaped responses. Drawing on a large sample of politically engaged users and ideal-point estimates of ideology, we find strong partisan asymmetries. Conservative users not aligned with the government were more likely to circumvent the ban, and right-leaning news domains became markedly more prevalent on the platform. We describe this dynamic as a “sorting ratchet”: the ban segmented the digital public sphere along partisan lines, with effects that persisted even after restrictions were lifted. Platform bans in democratic settings may therefore deepen polarization and durably reshape information environments