Giving Guidance to Graphs
Evaluating Annotations of Data Visualizations for the News
Bibliographic Data
| ID | 21747702 |
|---|---|
| Authors | Russell Chun (0000-0003-3936-9991, corresponding author), Russell S Chun |
| Year | 2020 |
| Volume | 27 |
| Issue | 2 |
| Pages | 84-97 |
| Publication date | 2020-04-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Visual Communication Quarterly (JOURNAL) |
| Journal identifiers | ISSN: 1555-1393 • E-ISSN: 1555-1407 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/15551393.2020.1749842 |
| OpenAlex | W3039597041 |
| Language | EN |
| References cited | 16 |
This study quantifies the effectiveness of information recall with direct and indirect labeling of the annotation layer in a news data visualization. Variations of three data visualizations from The New York Times, The Washington Post, and The Wall Street Journal were presented to participants in a crowdsourced experiment to measure their story comprehension. Our results demonstrate that direct labeling offers no advantage over indirect labeling. More significantly, annotations on visualizations do no better to enhance comprehension than visualizations without them, contradicting data visualization orthodoxy
Annotation · Cognitive psychology · Comprehension · Data mining · Data science · Data visualization · Information retrieval · Recall · Visualization · World Wide Web · Advanced Text Analysis Techniques · Computer Science · Data Analysis with R · Data Visualization and Analytics · Psychology · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |