Optimizing Social Media Engagement in Professional Sport: A 3-Year Examination of Facebook, Instagram, and Twitter Posts
Bibliographic record
Abstract
Although social media has gained significant notoriety, there remains a “missing link” in examining engagement in the sport context. While the why , what , and whom have been explored, the where and when have received considerably less uptake. Accordingly, the purpose of this study was to examine social media engagement for professional sports teams to determine optimal when and where points of user engagement, and the relationship between impressions and engagement. Over two billion data points from 108,124 Facebook, Instagram, and Twitter posts were collected from four professional sports teams between 2017 and 2019. Findings from a regression analysis indicate that both when and where variables significantly predicted impression, and findings from the correlation analysis indicate that impression and engagement are nearly identical. These findings show fan engagement in the context of professional sport teams, prompting scholars to consider the impacts of time and platform, and encourage practitioners to rethink posting on Twitter, the least engaging of the Facebook, Instagram, and Twitter platforms.
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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 | 0.004 | 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 | 0.000 | 0.000 |
| 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".