Examining the Digital Pitch: A 3-Year Examination of Social Media Metrics From Men’s Professional Sport
Bibliographic record
Abstract
Although research in the social media and sport domain continues to uncover key insights related to content, there has been a push toward identifying the social media metrics that serve as the antecedents to relationship marketing engagement. Along that vein, the purpose of this study was to analyze social media activity (i.e., impressions and engagements) from all teams in a given professional sport league over a 3-year period. Contextually set with Major League Soccer teams for the 2017, 2018, and 2019 calendar years, 66,745 Instagram posts were retrieved using MVPindex and parsed for focal social media metrics (i.e., impressions and engagements) for each team using a temporal lens (i.e., by month and by day). Findings of this study align with past work indicating the need for sport properties to focus on posting outside of game-day windows, harnessing the ongoing, instantaneous nature of social media.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".