Follower Segments within and across the Social Media Networks of Major Professional Sport Organizations
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
The purpose of this study was to identify segments within the social media networks of major professional sport organizations. Relational data were collected from the Twitter accounts of four major professional sport organizations based in Toronto, Canada. Users within these networks were subsequently parsed based upon their Twitter behavior (e.g., likes, retweets, and follows) and their demographic information using an automated cluster analysis. After revealing characteristics of each segment, the findings highlight both sport focused (e.g., hockey, basketball) and non-sport focused (e.g., mothers, music lovers) subgroups which, in some cases, appear in multiple professional sport team networks. The findings provide the antecedents to social media interaction and suggest managers within professional sport organizations consider this information before forging new or enhanced relationship marketing activities as well as cross-promotional campaigns with additional brands.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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