Dealing With Statistical Significance in Big Data: The Social Media Value of Game Outcomes in Professional Football
Bibliographic record
Abstract
The identification of relevant effects is challenging in Big Data because larger samples are more likely to yield statistically significant effects. Professional sport teams attempting to identify the core drivers behind their follower numbers on social media also face this challenge. The purposes of this study are to examine the effects of game outcomes on the change rate of followers using big social media data and to assess the relative impact of determinants using dominance analysis. The authors collected data of 644 first division football clubs from Facebook ( n = 297,042), Twitter ( n = 292,186), and Instagram ( n = 312,710) over a 19-month period. Our fixed-effects regressions returned significant findings for game outcomes. Therefore, the authors extracted the relative importance of wins, draws, and losses through dominance analysis, indicating that a victory yielded the highest increase in followers. For practitioners, the findings present opportunities to develop fan engagement, increase the number of followers, and enter new markets.
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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 | 0.061 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".