Look What We Have Here: Exploring Brand-Related Sport Consumer Twitter Conversation Topics
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
As sport organizations leverage social media as a critical component of marketing strategy, tools for exploring the large volume of sport consumer social media conversations are vital. This scholarship demonstrates the value of unsupervised latent Dirichlet allocation (LDA) as a tool for exploring consumers' digital conversations. Specifically unsupervised LDA was applied to derive latent topics among Women's National Basketball Association-related Twitter conversation over the course of the 2020 season. Quantitative (cv and umass scores) and qualitative (two expert reviews) approaches were utilized to delineate topic configurations. Marginal topic distance established topic importance. Results from 118,518 tweets revealed 18 conversation topics spanning two overarching themes: social justice issues and on-court performance. The range and depth of the results highlight the importance of the unsupervised topic modeling method (without semi-supervised predetermined topic leads) for considering holistic rather than subsampled or snapshot datasets. This empirical investigation extends the conversation surrounding natural language processing to sport management research and practice, delivers a foundation for unsupervised LDA application to sport consumer conversation, and explores social media conversations during a critical moment for the WNBA.
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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 | 0.004 | 0.000 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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