Demo Data from EDBT 2026 / reviewers in the wild / expert
Jeon Cheol Su
dblp:349/4461
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0003-2764-5522ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 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 |
Usability and user experience research · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › information visualization
affective visualization |
0.8 | 1 | 2024 | Creating Emordle: Animating Word Cloud for Emotion Expression · IEEE Trans. Vis. Comput. Graph. 2024 |
Usability and user experience research › evaluation methodology
crowdsourced evaluation |
0.2 | 1 | 2024 | Creating Emordle: Animating Word Cloud for Emotion Expression · IEEE Trans. Vis. Comput. Graph. 2024 |
Methods — techniques the papers use, named apart from their topics
crowdsourcing study · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Creating Emordle: Animating Word Cloud for Emotion ExpressionabstractWe propose emordle, a conceptual design that animates wordles (compact word clouds) to deliver their emotional context to audiences. To inform the design, we first reviewed online examples of animated texts and animated wordles, and summarized strategies for injecting emotion into the animations. We introduced a composite approach that extends an existing animation scheme for one word to multiple words in a wordle with two global factors: the randomness of text animation (entropy) and the animation speed (speed). To create an emordle, general users can choose one predefined animated scheme that matches the intended emotion class and fine-tune the emotion intensity with the two parameters. We designed proof-of-concept emordle examples for four basic emotion classes, namely happiness, sadness, anger, and fear. We conducted two controlled crowdsourcing studies to evaluate our approach. The first study confirmed that people generally agreed on the conveyed emotions from well-crafted animations, and the second one demonstrated that our identified factors helped fine-tune the extent of the emotion delivered. We also invited general users to create their own emordles based on our proposed framework. Through this user study, we confirmed the effectiveness of the approach. We concluded with implications for future research opportunities of supporting emotion expression in visualizations. Liwenhan Xie, Xinhuan Shu, Jeon Cheol Su, Yun Wang 0012, Siming Chen 0001, Huamin Qu |
IEEE Trans. Vis. Comput. Graph. | 3 |