Demo Data from SIGIR 2026 / reviewers in the wild / expert
J. K. Edward Gitre
dblp:265/3053
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
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% | |
| Human-computer interaction and pervasive computing
1 paper |
Immersive interaction · 77% Usability and user experience research · 23% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › visual analytics
immersive analytics |
0.5 | 1 | 2021 | Sensemaking Strategies with Immersive Space to Think · VR 2021 |
Visualization and visual analytics
sensemaking |
0.5 | 1 | 2021 | Sensemaking Strategies with Immersive Space to Think · VR 2021 |
Usability and user experience research › human-data interaction
analyst workflow |
0.1 | 1 | 2021 | Sensemaking Strategies with Immersive Space to Think · VR 2021 |
Methods — techniques the papers use, named apart from their topics
qualitative analysis · 1.0observational study · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Sensemaking Strategies with Immersive Space to ThinkabstractThe process of sensemaking involves foraging through and extracting information from large sets of documents, and it can be a cognitively intensive task. A recent approach, the Immersive Space to Think (IST), allows analysts to browse, read, mark up documents, and use immersive 3D space to organize and label collections of documents. In this study, we observed seventeen novice analysts perform a historical analysis task in order to understand how users utilize the features of IST to extract meaning from large text-based datasets. We found three different layout strategies they employed to create meaning with the documents we provided. We further found patterns of interaction and organization that can inform future improvements to the IST approach. Lee Lisle, Kylie Davidson, J. K. Edward Gitre, Chris North 0001, Doug A. Bowman |
VR | 3 |