Demographic Background Prompting Does Not Affect Linguistic Features on LLM-Generated News Texts

We explored if implicit demographic information in prompts for large language models (LLMs) influences the linguistic features of generated text. Two LLMs were prompted to write news articles based on a title and summary, with prompts including demographic details like age, income, or nationality. The models were instructed not to explicitly reference these details. A total of 28,080 articles were generated by varying the demographics and topics. We calculated various linguistic metrics (e.g., sentence length, type-token ratio) and performed ANOVA, treating linguistic metrics as dependent variables and demographic categories as independent variables. Results indicate that demographic attributes do not significantly impact the linguistic metrics.