"I Came Across a Junk": Understanding Design Flaws of Data Visualization from the Public's Perspective
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Abstract (arXiv)
The visualization community has a rich history of reflecting upon flaws of visualization design, and research in this direction has remained lively until now. However, three main gaps still exist. First, most existing work characterizes design flaws from the perspective of researchers rather than the perspective of general users. Second, little work has been done to infer why these design flaws occur. Third, due to problems such as unclear terminology and ambiguous research scope, a better framework that systematically outlines various design flaws and helps distinguish different types of flaws is desired. To address the above gaps, this work investigated visualization design flaws through the lens of the public, constructed a framework to summarize and categorize the identified flaws, and explored why these flaws occur.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Jul 2024.
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Citing papers per year: 2025: 5; 2026: 4.
9 papers.
- 2025Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answeringcited by 16
- 2025Protecting multimodal large language models against misleading visualizationscited by 8
- 2025The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Modelscited by 7
- 2025Is this chart lying to me? Automating the detection of misleading visualizationscited by 6
- 2025VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inferencecited by 3
- 2026Navigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answeringcited by 1
- 2026ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generationcited by 0
- 2026When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizationscited by 0
- 2026Chart Deception in Vision-Language Models: From Vulnerability to Mitigationcited by 0
- 2026Chart Deception in Vision-Language Models: From Vulnerability to Mitigation12 Jun 2026
- 2026Navigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answering30 Mar 2026
- 2026When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations23 Mar 2026
- 2026ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation19 Jan 2026
- 2025VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference21 Dec 2025
- 2025Is this chart lying to me? Automating the detection of misleading visualizations29 Aug 2025
- 2025The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models13 Aug 2025
- 2025Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering23 Mar 2025
- 2025Protecting multimodal large language models against misleading visualizations27 Feb 2025