{"ID":6536543,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-15T00:45:46.305185675Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10523","arxiv_id":"2607.10523","title":"How Data Narratives Go Wrong: A Taxonomy of Issues Across the Data Communication Process","abstract":"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 compound. To address this gap, we introduce TIC, a Taxonomy of Issues in Data Communication, synthesized from prior literature and refined through the qualitative annotation of 700 real-world data narratives from fact-checking sites, research datasets, and controversial media. TIC organizes recurring breakdowns across six dimensions-data, analysis, visual encoding, text, reasoning, and interpretation-and situates them within a framework spanning analysis, narrative construction, and audience reception. Alongside the taxonomy and process framework, we contribute a qualitatively annotated case corpus with coding justifications and an interactive browsing interface. Collectively, these contributions provide a structured lens for diagnosing problematic data narratives and informing future sociotechnical support for trustworthy data communication.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2607.10523","url_pdf":"https://arxiv.org/pdf/2607.10523v1","authors":"[\"Yu Fu\",\"Jiawei Zhou\",\"Sichen Jin\",\"Munmun De Choudhury\",\"Cindy Xiong Bearfield\",\"John Stasko\"]","published":"2026-07-12T00:54:15Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.CY\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}