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Record W2346036600 · doi:10.1145/2851581.2892427

TwitchViz

2016· article· en· W2346036600 on OpenAlexaff
Rui Pan, Lyn Bartram, Carman Neustaedter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceMultimediaLive streamingHuman–computer interactionVisualizationGame designArtificial intelligence

Abstract

fetched live from OpenAlex

Twitch.tv is a flagship platform for live game streaming between players and viewers. It allows players to broadcast their gameplay to a public audience where viewers chat with each other and discuss gameplay. Current tools for analyzing live game streaming and chat rooms are limited. In this paper, we describe the design of TwitchViz: a new visualization tool with the goal of helping both players and game designers to better understand the relationship between gameplay and Twitch viewers' chatting behaviors. An initial feasibility study showed that TwitchViz supports novel ways to get an insight of gameplay issues from the patterns of chatting behaviors of viewers and highlighted design issues to address in subsequent versions of the tool.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.022
GPT teacher head0.298
Teacher spread0.276 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations19
Published2016
Admission routes1
Has abstractyes

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