{
  "id": 5174,
  "url": "https://arxiv.org/abs/2604.27624v1",
  "title": "Mapping how LLMs debate societal issues when shadowing human personality traits, sociodemographics and social media behavior",
  "summary": "Large Language Models (LLMs) can strongly shape social discourse, yet datasets investigating how LLM outputs vary across controlled social and contextual prompting remain sparse. Cognitive Digital Shadows (CDS) is a 190,000-record synthetic corpus supporting analyses of LLM-generated discourse. Each CDS record is generated by one of 19 LLMs, prompted to shadow either a human persona or an AI-assistant role. CDS contains LLM responses on 4 controversial societal topics: vaccines/healthcare, socia",
  "authors": "Ali Aghazadeh Ardebili, Massimo Stella",
  "category": "research",
  "topics": "healthcare,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-30T09:13:08.000Z",
  "fetched_at": "2026-07-14T16:31:35.572Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5174",
  "original_url": "https://arxiv.org/abs/2604.27624v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}