{
  "id": 3043,
  "url": "https://arxiv.org/abs/2607.10523v1",
  "title": "How Data Narratives Go Wrong: A Taxonomy of Issues Across the Data Communication Process",
  "summary": "Data narratives increasingly shape public understanding, but their failures are rarely just isolated factual errors or deceptive charts. Instead, they emerge through a broader meaning-making process in which quantitative evidence is transformed into claims, representations, and arguments. While prior work has examined these failures across disparate fields (e.g., statistics, visualization, and fact-checking), the community lacks a holistic lens to explain how these issues arise, propagate, and c",
  "authors": "Yu Fu, Jiawei Zhou, Sichen Jin, Munmun De Choudhury, Cindy Xiong Bearfield, John Stasko",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-12T00:54:15.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/3043",
  "original_url": "https://arxiv.org/abs/2607.10523v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}