{
  "id": 6719,
  "url": "https://arxiv.org/abs/2603.25061v1",
  "title": "Auditing Algorithmic Personalization in TikTok Comment Sections",
  "summary": "Personalization algorithms are ubiquitous in modern social computing systems, yet their effects on comment sections remain underexplored. In this work, we conducted an algorithmic auditing experiment to examine comment personalization on TikTok. We trained sock-puppet accounts to exhibit left-leaning or right-leaning preferences and successfully validated 17 of them by analyzing the videos recommended on their For You Pages. We then scraped the comment sections shown to these trained partisan ac",
  "authors": "Yueru Yan, Siqi Wu",
  "category": "research",
  "topics": "transparency",
  "orgs": "bytedance",
  "regions": null,
  "published_at": "2026-03-26T05:52:41.000Z",
  "fetched_at": "2026-07-14T16:32:41.667Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6719",
  "original_url": "https://arxiv.org/abs/2603.25061v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}