학술논문

Attitudinal Effects of Data Visualizations and Illustrations in Data Stories
Document Type
Periodical
Source
IEEE Transactions on Visualization and Computer Graphics IEEE Trans. Visual. Comput. Graphics Visualization and Computer Graphics, IEEE Transactions on. 30(7):4039-4054 Jul, 2024
Subject
Computing and Processing
Bioengineering
Signal Processing and Analysis
Data visualization
Visualization
Journalism
Complexity theory
Videos
Technological innovation
Task analysis
Attitude change
data stories
emotions
quantitative and qualitative evaluation
Language
ISSN
1077-2626
1941-0506
2160-9306
Abstract
Journalism has become more data-driven and inherently visual in recent years. Photographs, illustrations, infographics, data visualizations, and general images help convey complex topics to a wide audience. The way that visual artifacts influence how readers form an opinion beyond the text is an important issue to research, but there are few works about this topic. In this context, we research the persuasive, emotional and memorable dimensions of data visualizations and illustrations in journalistic storytelling for long-form articles. We conducted a user study and compared the effects which data visualizations and illustrations have on changing attitude towards a presented topic. While visual representations are usually studied along one dimension, in this experimental study, we explore the effects on readers’ attitudes along three: persuasion, emotion, and information retention. By comparing different versions of the same article, we observe how attitudes differ based on the visual stimuli present, and how they are perceived when combined. Results indicate that the narrative using only data visualization elicits a stronger emotional impact than illustration-only visual support, as well as a significant change in the initial attitude about the topic. Our findings contribute to a growing body of literature on how visual artifacts may be used to inform and influence public opinion and debate. We present ideas for future work to generalize the results beyond the domain studied, the water crisis.