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Record W3160639803 · doi:10.1145/3411763.3451646

Characterizing Growth and Decline in Online UX Communities

2021· article· en· W3160639803 on OpenAlexaff
Gillian Chen, Lillio Mok

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicExpert finding and Q&A systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMainstreamComputer scienceDiversity (politics)User experience designStack (abstract data type)World Wide WebLongevitySociologyHuman–computer interactionPolitical science

Abstract

fetched live from OpenAlex

UX practitioners increasingly rely on online communities to collaborate on and discuss complex design problems. Understanding how these platforms flourish is thus of interest to both HCI academia and the broader UX discipline. In this study, we comparatively investigate the longevity of two such groups: the r/userexperience community on Reddit and the UX subforum on Stack Exchange. By quantifying how users post online on aggregate and what users discuss in their individual posts, we find that Reddit has grown consistently as a digital forum for UX practice. In contrast, Stack Exchange has contracted despite being more responsive and being as capable of addressing mainstream UX concepts as Reddit. Discussions of niche, higher-level UX concepts on Stack Exchange also declined disproportionately, leading to less conceptual diversity. Our results therefore contribute an initial comparative understanding of community longevity between online UX platforms.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.004
Science and technology studies0.0020.002
Scholarly communication0.0040.009
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.036
GPT teacher head0.268
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2021
Admission routes1
Has abstractyes

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