Does rivalry matter? An analysis of sport consumer interest on social media
Bibliographic record
Abstract
Purpose Previous research on rivalry games in sport has predominantly focused on understanding the nature of these games and their effects on consumer behavior. As such, the purpose of this paper is to conduct an empirical examination to provide better theoretical and empirical understanding of how rivalries may impact the posting of content online. Design/methodology/approach This research utilizes Twitter data measuring the number of posts by individuals about college football teams to model how often fans create content during game days. The models in this study were estimated using fixed-effects panel regressions. Findings After controlling for a number of factors, including the type of rivalry game, results indicate fans post more during traditional rivalries. Furthermore, newer rivalry games had less impact on the amount of content posted about a team. Practical implications The findings from this research provide sport marketers with important information regarding fan use of digital platforms. Notably, the results suggest rivalries can help to boost the volume of content individuals post about a team, indicating these games provide teams with an opportunity to maximize their engagement with fans and focus on key marketing objectives. Originality/value To date, there has been little examination considering whether rivalries affect behaviors in the digital realm. Therefore, the current investigation is one of the first studies to examine how rivalries impact social media behavior.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".