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Anjali Manivannan

dblp:153/7771 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › visual communication
persuasive visualization
0.212014
The Persuasive Power of Data Visualization · IEEE Trans. Vis. Comput. Graph. 2014
Visualization and visual analytics
visualization evaluation
0.212014
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
YearPublicationVenuePosition
2014 The Persuasive Power of Data Visualization
abstract
Data 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