The Gulf of Interpretation: From Chart to Message and Back Again
Christian Knoll, Torsten Möller, Kathleen Gregory, Laura Koesten
Abstract
Charts are used to communicate data visually, but often, we do not know whether a chart's intended message aligns with the message readers perceive. In this mixed-methods study, we investigate how data journalists encode data and how members of a broad audience engage with, experience, and understand these visualizations. We conducted workshops and interviews with school and university students, job seekers, designers, and senior citizens to collect perceived messages and feedback on eight real-world charts. We analyzed these messages and compared them to the intended message. Our results help to understand the gulf that can exist between messages (that producers encode) and viewer interpretations. In particular, we find that consumers are often overwhelmed with the amount of data provided and are easily confused with terms that are not well known. Chart producers tend to follow strong conventions on how to visually encode particular information that might not always benefit consumers.
CCS Concepts: • Human-centered computing → HCI design and evaluation methods; Visualization design and evaluation methods.
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 f5c8a9cb-173c-45d9-9a9d-88e403b3445dCited by top-tier papers1
Ask how each one uses itBuilds on10
- Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic ContentAlan Lundgard, Arvind SatyanarayanIEEE VIS 2021 · 141 citations
- Affective Learning Objectives for Communicative VisualizationsElsie Lee-Robbins, Eytan AdarIEEE VIS 2022 · 83 citations
- Affective Visualization Design: Leveraging the Emotional Impact of DataXingyu Lan, Yanqiu Wu, Nan CaoIEEE VIS 2023 · 75 citations
- CALVI: Critical Thinking Assessment for Literacy in VisualizationsLily W. Ge, Yuan Cui, Matthew KayCHI 2023 · 68 citations
- Striking a Balance: Reader Takeaways and Preferences when Integrating Text and ChartsChase Stokes, Vidya Setlur, Bridget Cogley, Arvind Satyanarayan et al.IEEE VIS 2022 · 64 citations
Related papers
- Do You See What I See? A Qualitative Study Eliciting High-Level Visualization ComprehensionGhulam Jilani Quadri, Arran Zeyu Wang, Zhehao Wang, Jennifer Adorno Nieves et al.CHI 2024 · 37 citations
- Visualization Accessibility in the Wild: Challenges Faced by Visualization DesignersShakila Cherise S. Joyner, Amalia Riegelhuth, Kathleen Garrity, Yea-Seul Kim et al.CHI 2022 · 42 citations
- Visual Task Performance and Spatial Abilities: An Investigation of Artists and MathematiciansSara Tandon, Alfie Abdul-Rahman, Rita BorgoCHI 2023 · 2 citations
- Trustworthy by Design: The Viewer's Perspective on Trust in Data VisualizationOen G. McKinley, Saugat Pandey, Alvitta OttleyCHI 2025 · 5 citations
- Image or Information? Examining the Nature and Impact of Visualization Perceptual ClassificationAnjana Arunkumar, Lace M. K. Padilla, Gi-Yeul Bae, Chris BryanIEEE VIS 2023 · 15 citations
