Studying Animation for Real-Time Visual Analytics: A Design Study of Social Media Analytics in Emergency Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Domains such as emergency management have a need for real-time change monitoring and pattern analysis, but interface design principles for real-time visual analysis situations are still under development. In this paper, we present early results from a design study in social media visual analytics for emergency management. Our motivation is a main information visualization challenge: the lack of clear design principles informed by research in human cognition for the use of animation in real-time streams. We discuss three domain-specific challenges: (1) Coping with the high volume of social media data that is generated during disaster response, (2) analysts' need to quickly extract relevant features for real-time sense-making; and (3) the effective analysis of social media streams even when some critical attributes are absent. This paper presents preliminary results on a research-based design principle for the use of animation in real-time visual analytics, targeted to support the real-time analysis of social media data in emergency management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it