Investigating the Differences in Twitter Content and Effectiveness Between Individual and Team Sport Athletes
Bibliographic record
Abstract
While the existing literature has categorized Twitter conversations and examined gender differences in professional athletes’ online self-presentation initiatives, researchers have neglected to examine the differences in Twitter presentation between individual and team sport athletes. This study examined the differences in self-promotional content and effectiveness of Twitter activity between individual and team sport athletes. The authors utilized content analysis to categorize Twitter activity while a comparison not only between different types of athletes but also within categories was conducted by analyzing composite variables. While the findings confirmed the existence of content contrasts in the promotional category, no significant differences were observed in the remaining tweet categories. The analysis of fan perceptions identified team athlete tweets as more effective aside from the promotional category. Independently, the professional category was found to be most effective amongst team athlete tweets, while the athlete exchange category was deemed most effective amongst individual athlete tweets. The current study contributed to the understanding of self-promotional tactics utilized by two categories of athletes (i.e., individual and team) through the investigation of content of tweets and fan perception analysis. Key implications for the academic field and the sport marketing industry and recommendations for future research were discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".