The world’s highest-paid athletes, product endorsement, and Twitter
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the practice of celebrity athletes’ product endorsement in the context of social media, guided by meaning transfer model. Design/methodology/approach The study adopted a content analysis method based on data gathered from the official Twitter account of 17 of the highest-paid athletes over a period of five months. Findings Results outline the state, involvement level, roles, modes, preferred content types, discernible differences, shared features, and best practices employed in endorsement tweets. A framework of athletes’ product endorsement on Twitter is presented. Research limitations/implications The study presented theoretical and practical implications, and limitations and impetus for future research. Originality/value The study investigated professional athletes’ use of their own media channel for the purpose of endorsement, presented a framework that illustrates the practice of celebrity athletes’ product endorsement on social media, and identified a best practice and an exemplary reference.
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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 | 0.001 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 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".