VIBES: Exploring Viewer Spatial Interactions as Direct Input for Livestreamed Content
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
Livestreaming has rapidly become a popular online pastime, with real-time interaction between streamer and viewer being a key motivating feature. However, viewers have traditionally had limited opportunity to directly influence the streamed content; even when such interactions are possible, it has been reliant on text-based chat. We investigate the potential of spatial interaction on the livestreamed video content as a form of direct, real-time input for livestreamed applications. We developed VIBES, a flexible digital system that registers viewers' mouse interactions on the streamed video, i.e., clicks or movements, and transmits it directly into the streamed application. We used VIBES as a technology probe; first designing possible demonstrative interactions and using these interactions to explore streamers' perception of viewer influence and possible challenges and opportunities. We then deployed applications built using VIBES in two livestreams to explore its effects on audience engagement and investigate their relationships with the stream, the streamer, and fellow audience members. The use of spatial interactions enhances engagement and participation and opens up new avenues for both streamer-viewer and viewer-viewer participation. We contextualize our findings around a broader understanding of motivations and engagement in livestreaming, and we propose design guidelines and extensions for future research.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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