Creating Digital Community in Hockey: A Study of the Montreal Canadiens’ Instagram Engagement During the 2021 Stanley Cup Final
Bibliographic record
Abstract
Within social media there are multiple strategies and methods you can use to grow your page but, in terms of content there are many variables to determine what yields the most engagement. With the Montreal Canadiens being in the 2021 Stanley Cup Final for the first time in the social-media age, this paper evaluated how the content they posted on their in-feed Instagram account @canadiensmtl from June 28, 2021, to July 8, 2021. This study aims to determine what content achieve the greatest average engagement per post, and to identify the top engaging moments during the period indicated, while using likes and comments. This research will test the hypothesis that game and community content will result in the highest average engagement. This will be completed by collecting data on all in-feed Instagram posts from the page, separating into categories of type of post format (photo, video, graphics, and motion graphics) and type of post content (game content, community content, sponsored, and other). Once it is determined what category, a post belongs to the sum of likes and comments were added together and divided by the number of posts in the category to determine the average engagement per post. The results showed for type of content; game content and community content have the highest amount of average engagement. Then for type of post format; photos were the top average engagement multimedia content. These results show us that top performing posts come from top emotional and important moments during the game, and immediately after.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".