Misleading Beyond Visual Tricks: How People Actually Lie with Charts
Maxim Lisnic, Cole Polychronis, Alexander Lex, Marina Kogan
摘要
Data visualizations can empower an audience to make informed decisions. At the same time, deceptive representations of data can lead to inaccurate interpretations while still providing an illusion of data-driven insights. Existing research on misleading visualizations primarily focuses on examples of charts and techniques previously reported to be deceptive. These approaches do not necessarily describe how charts mislead the general population in practice. We instead present an analysis of data visualizations found in a real-world discourse of a significant global event—Twitter posts with visualizations related to the COVID-19 pandemic. Our work shows that, contrary to conventional wisdom, violations of visualization design guidelines are not the dominant way people mislead with charts. Specifically, they do not disproportionately lead to reasoning errors in posters’ arguments. Through a series of examples, we present common reasoning errors and discuss how even faithfully plotted data visualizations can be used to support misinformation.
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引用它的顶会 Paper21
- How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?Leo Yu-Ho Lo, Huamin QuIEEE VIS 2024 · 被引用 24 次
- "Yeah, this graph doesn't show that": Analysis of Online Engagement with Misleading Data VisualizationsMaxim Lisnic, Alexander Lex, Marina KoganCHI 2024 · 被引用 16 次
- "I Came Across a Junk": Understanding Design Flaws of Data Visualization from the Public's PerspectiveXingyu Lan, Yu LiuIEEE VIS 2024 · 被引用 12 次
- V-FRAMER: Visualization Framework for Mitigating Reasoning Errors in Public PolicyLily W. Ge, Matthew W. Easterday, Matthew Kay, Evanthia Dimara 等CHI 2024 · 被引用 12 次
- Visualization Guardrails: Designing Interventions Against Cherry-Picking in Interactive Data ExplorersMaxim Lisnic, Zach Cutler, Marina Kogan, Alexander LexCHI 2025 · 被引用 9 次
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