Large Language Models (LLMs) are increasingly used to replicate human responses in survey data, yet it remains unclear whether they perform equally well for social minorities and majorities. Using a unique survey conducted in South Korea that provided identical questionnaires to adolescents from multicultural and monocultural families, we examine group-level differences in the accuracy of LLM-generated synthetic data. We construct personas that mirror individual-level demographic profiles and prompt two LLMs, llama3:latest and gpt-4o, to answer 77 survey questions across 11 question domains. We find marginal differences in LLM performance between minority and majority groups. Instead, replication performance varies by whether question prompts contain socially stigmatized words and by which LLM model we use. These findings highlight both the potential and the limitations of using LLMs to generate synthetic survey data targeting social minorities.

In this study, we investigate how author affiliation shapes academic discourse, proposing it as an effective proxy for author perspective in understanding what topics are studied, how nations are framed, and whose realities are prioritised. Using Palestine as a case study, we apply BERTopic and Structural Topic Modelling (STM) to 29,536 English-language academic articles collected from the OpenAlex database. We find that domestic authors focus on practical, local issues like healthcare, education, and the environment, while foreign authors emphasise legal, historical, and geopolitical discussions. These differences, in our interpretation, reflect lived proximity to war and crisis. We also note that while BERTopic captures greater lexical nuance, STM enables covariate-aware comparisons, offering deeper insight into how affiliation correlates with thematic emphasis. We propose extending this framework to other underrepresented countries, including a future study focused on Gaza post-October 7.