Misleading Beyond Visual Tricks: How People Actually Lie with Charts
Maxim Lisnic, Cole Polychronis, Alexander Lex, Marina Kogan
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 74cdb651-89fc-4337-9e0f-24f8fa1875ddCited by top-tier papers21
- How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?Leo Yu-Ho Lo, Huamin QuIEEE VIS 2024 · 24 citations
- "Yeah, this graph doesn't show that": Analysis of Online Engagement with Misleading Data VisualizationsMaxim Lisnic, Alexander Lex, Marina KoganCHI 2024 · 16 citations
- "I Came Across a Junk": Understanding Design Flaws of Data Visualization from the Public's PerspectiveXingyu Lan, Yu LiuIEEE VIS 2024 · 12 citations
- V-FRAMER: Visualization Framework for Mitigating Reasoning Errors in Public PolicyLily W. Ge, Matthew W. Easterday, Matthew Kay, Evanthia Dimara et al.CHI 2024 · 12 citations
- Visualization Guardrails: Designing Interventions Against Cherry-Picking in Interactive Data ExplorersMaxim Lisnic, Zach Cutler, Marina Kogan, Alexander LexCHI 2025 · 9 citations
Builds on8
- Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science OnlineCrystal Lee, Tanya Yang, Gabrielle D. Inchoco, Graham M. Jones et al.CHI 2021 · 140 citations
- Surfacing Visualization MiragesAndrew M. McNutt, Gordon Kindlmann, Michael CorrellCHI 2020 · 103 citations
- Mapping the Landscape of COVID-19 Crisis VisualizationsYixuan Zhang, Yifan Sun, Lace M. K. Padilla, Sumit Barua et al.CHI 2021 · 74 citations
- Many Faced Hate: A Cross Platform Study of Content Framing and Information Sharing by Online Hate GroupsShruti Phadke, Tanushree MitraCHI 2020 · 64 citations
- Data Hunches: Incorporating Personal Knowledge into VisualizationsHaihan Lin, Derya Akbaba, Miriah Meyer, Alexander LexIEEE VIS 2022 · 45 citations
Related papers
- Is this chart lying to me? Automating the detection of misleading visualizationsJonathan Tonglet, Jan Zimny, Tinne Tuytelaars, Iryna GurevychACL 2026 · 4 citations
- Visualization Design Practices in a Crisis: Behind the Scenes with COVID-19 Dashboard CreatorsYixuan Zhang, Yifan Sun, Joseph D. Gaggiano, Neha Kumar et al.IEEE VIS 2022 · 33 citations
- Annotating Line Charts for Addressing DeceptionArlen Fan, Yuxin Ma, Michelle Mancenido, Ross MaciejewskiCHI 2022 · 44 citations
- Why Change My Design: Explaining Poorly Constructed Visualization Designs with Explorable ExplanationsLeo Yu-Ho Lo, Yifan Cao, Leni Yang, Huamin QuIEEE VIS 2023 · 6 citations
- CALVI: Critical Thinking Assessment for Literacy in VisualizationsLily W. Ge, Yuan Cui, Matthew KayCHI 2023 · 68 citations
