Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how Olympic audiences utilized Twitter to follow American National Governing Bodies (NGBs) during the 2016 Rio Olympic Games. Design/methodology/approach Guided by economic demand theory, the researchers sought to explore whether factors such as the content of social media messages, athlete’s performance, event presentation, scheduling, and TV broadcasting contribute to enhancing fans’ interests in following NGBs on Twitter during the Olympic Games. In total, 33 American NGB Twitter accounts formed the data set for this study. Each of NGBs’ Twitter data was collected every night at midnight from August 7 to 23, 2016. Data collected from each NGB account included number of followers, number of accounts followed, number of tweets, and number of “likes.” Findings Results of this study revealed that team’s performance and the number of tweets had direct and positive relationships with increasing the number of NGB’s Twitter followers on each competition day. The number of “likes,” however, had a significant negative relationship with fans’ interests in following NGBs’ Twitter. Originality/value The results of the study are expected to help Governing Bodies in the Olympic sports have a better understanding of fans’ social media usage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".