{
  "id": 6527,
  "url": "https://arxiv.org/abs/2604.19787v1",
  "title": "LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans",
  "summary": "Social media platforms mediate how billions form opinions and engage with public discourse. As autonomous AI agents increasingly participate in these spaces, understanding their behavioral fidelity becomes critical for platform governance and democratic resilience. Previous work demonstrates that LLM-powered agents can replicate aggregate survey responses, yet few studies test whether agents can predict specific individuals' reactions to specific content. This study benchmarks LLM-based agents' ",
  "authors": "Ljubisa Bojic, Alexander Felfernig, Bojana Dinic, Velibor Ilic, Achim Rettinger, Vera Mevorah et al.",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-31T19:27:59.000Z",
  "fetched_at": "2026-07-14T16:32:33.101Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6527",
  "original_url": "https://arxiv.org/abs/2604.19787v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}