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Record W4225012855 · doi:10.1145/3491102.3501934

”Chat Has No Chill”: A Novel Physiological Interaction For Engaging Live Streaming Audiences

2022· article· en· W4225012855 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCHI Conference on Human Factors in Computing Systems · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModalitiesContext (archaeology)Audience participationComputer scienceCommonsModality (human–computer interaction)MultimediaLive streamingUser engagementHuman–computer interactionWorld Wide WebSociologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.348
GPT teacher head0.360
Teacher spread0.012 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it