The Gulf of Interpretation: From Chart to Message and Back Again

TL;DR

The paper addresses the gulf between producers' intended messages in data visualizations and readers' interpretations for broad public audiences. It employs a three-phase mixed-methods approach with professional chart producers and diverse consumer groups, analyzing eight real-world charts to compare intended messages with reader messages using semantic content levels and message typologies. Key findings show that readers rely on chart text, struggle with data density, and that conventional chart choices can hinder understanding; when designers focus on messaging and test with diverse audiences, interpretation improves. The work demonstrates practical implications for data journalists and visualization researchers, advocating message-centered design, collaborative development, and lightweight audience testing to enhance accessibility, trust, and effectiveness of public data communication.

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.

Figures (7)

• Figure 1: Our methodological process with three phases. The blue arrow lines depict the different parts of the analysis and the corresponding section.
• Figure 2: Demographic overview of our 42 participants in the consumer group. The bar charts show the distribution of the participants for the a) age range, b) education level, c) experience with visualization, and d) interest in visualization.
• Figure 3: Sample of charts used in the workshops and interviews (see supplemental material S02 for full-page figures). Reproduced with permission. V$_{1}$: Graphic: Süddeutsche Zeitung (SZ from November 4, 2022) / Julia Schubert and Isabel Kronenberger; V$_{2}$: Graphic: IHS Preismonitor (2022) by Sebastian P. Koch and Christine Lietz, Source: Eurostat, IHS; V$_{3}$: Graphic: ©APA / Walter Longauer, Source: BMAW / IfW Kiel; V$_{4}$: Graphic: Wiener Zeitung / Irma Tulek, Source: Statistik Austria, APA; V$_{5}$: Graphic: ©APA / Walter Longauer, Source: SORA; V$_{6}$: Graphic: Wiener Zeitung / Moritz Ziegler, Source: Statistik Austria; V$_{7}$, V$_{8}$: Graphic, Source: Eurostat.
• Figure 4: Typology of the 174 individual messages created by the consumer groups. Messages are grouped into the four different levels of messages (a) defined by Lundgard and Satyanarayan lundgard2022 and based on the correctness of their content (b).
• Figure 5: Heatmap showing the distribution of specific message types (L1-L4) within the different consumer groups.