”Chat Has No Chill”: A Novel Physiological Interaction For Engaging Live Streaming Audiences
Bibliographic record
Abstract
Now more than ever, people are using online platforms to communicate. Twitch, the foremost platform for live game streaming, offers many communication modalities. However, the platform lacks representation of social cues and signals of the audience experience, which are innately present in live events. To address this, we present a technology probe that captures the audience energy and response in a game streaming context. We designed a game and integrated a custom-communication modality—Commons Sense—in which the audience members’ heart rates are sensed via webcam, averaged, and fed into a video game to affect sound, lighting, and difficulty. We conducted an ‘in-the-wild’ evaluation with four Twitch streamers and their audience members (N=55) to understand how these groups interacted through Commons Sense. Audience members and streamers indicated high levels of enjoyment and engagement with Commons Sense, suggesting the potential of physiological interaction as a beneficial communication tool in live streaming.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".