Enriching Digital Sport Marketing Communication: Examining Emoji Functions Through Media Richness Theory
Bibliographic record
Abstract
This study extends Media Richness Theory (MRT) by examining how emojis function as nonverbal cues in digital sport communication. This was accomplished through a mixed-method approach combining machine learning and quantitative content analysis of 13,642 X (formerly Twitter) posts from professional sport teams. The study aimed to understand how emojis function within sport organizations’ digital messaging and to identify contextual factors that influence emoji interpretation in sport digital communication. Our findings indicate that emojis serve dual functions: (i) as replacements for textual content and (ii) as supplements that reinforce emotional resonance with the classification models, achieving higher accuracy for single-emoji (80.3% F1-score) versus multi-emoji messages (72.2%). Contextual elements, including immediate textual surroundings and broader sport situations, significantly shape emoji interpretation and effectiveness. Results show that strategic emoji usage patterns vary across game situations, team performance contexts, and marketing-related announcements. This research offers theoretical contributions by extending MRT to account for nonverbal digital cues while providing foundational understanding to inform sport marketing strategies. The findings establish patterns of emoji functionality that future research can build upon to directly measure consumer response and marketing outcomes across diverse sport contexts.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".