{
  "id": 7617,
  "url": "https://arxiv.org/abs/2603.05972v2",
  "title": "THETA: A Textual Hybrid Embedding-based Topic Analysis Framework and AI Scientist Agent for Scalable Computational Social Science",
  "summary": "The explosion of big social data has created a scalability trap for traditional qualitative research, as manual coding remains labor-intensive and conventional topic models often suffer from semantic thinning and a lack of domain awareness. This paper introduces Textual Hybrid Embedding based Topic Analysis (THETA), a novel computational paradigm and open-source tool designed to bridge the gap between massive data scale and rich theoretical depth. THETA moves beyond frequency-based statistics by",
  "authors": "Zhenke Duan, Xin Li",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-06T07:12:05.000Z",
  "fetched_at": "2026-07-14T16:33:21.049Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7617",
  "original_url": "https://arxiv.org/abs/2603.05972v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}