Demo Data from EDBT 2026 / reviewers in the wild / expert
Anjali Manivannan
dblp:153/7771
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › visual communication
persuasive visualization |
0.2 | 1 | 2014 | The Persuasive Power of Data Visualization · IEEE Trans. Vis. Comput. Graph. 2014 |
Visualization and visual analytics
visualization evaluation |
0.2 | 1 | 2014 | The Persuasive Power of Data Visualization · IEEE Trans. Vis. Comput. Graph. 2014 |
Methods — techniques the papers use, named apart from their topics
quantitative analysis · 0.2qualitative analysis · 0.2controlled experiment · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | The Persuasive Power of Data VisualizationabstractData visualization has been used extensively to inform users. However, little research has been done to examine the effects of data visualization in influencing users or in making a message more persuasive. In this study, we present experimental research to fill this gap and present an evidence-based analysis of persuasive visualization. We built on persuasion research from psychology and user interfaces literature in order to explore the persuasive effects of visualization. In this experimental study we define the circumstances under which data visualization can make a message more persuasive, propose hypotheses, and perform quantitative and qualitative analyses on studies conducted to test these hypotheses. We compare visual treatments with data presented through barcharts and linecharts on the one hand, treatments with data presented through tables on the other, and then evaluate their persuasiveness. The findings represent a first step in exploring the effectiveness of persuasive visualization. Anshul Vikram Pandey, Anjali Manivannan, Oded Nov, Margaret Satterthwaite, Enrico Bertini |
IEEE Trans. Vis. Comput. Graph. | 2 |