Place Your Bets: An Exploratory Study of Sports-Gambling Operators’ Use of Twitter for Relationship Marketing
Bibliographic record
Abstract
Worldwide, sports gambling is a multibillion-dollar industry. Despite the industry’s size and success, little research has been conducted on sport-gambling operators (SGOs), and no research has examined their presence on social media. As such, this exploratory study aimed to examine the social media habits of SGOs through a relationship-marketing lens. To do so, 16,466 tweets were collected from the Twitter accounts of six Australian SGOs, with descriptive statistics from tweets presented and Leximancer performing automated thematic analyses. Results indicated that SGOs are discussing professionalized sport, influencers, and subbrands, as well as extensively making use of hashtags and mentions. Given these results, the strategies that SGOs are using to communicate and interact with their consumers focuses particularly on a North American professional-sport and horseracing context. This research contributes to the growing understanding of social media stakeholders in sport and provides an initial starting point for future research on SGOs given the recent legalization of sports gambling in the United States.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".