Interactive Ritual Chain in Esports Live Streaming: A Case Study of Bullet Screen Interaction During the 2025 Fearless Contract Toronto Masters
Bibliographic record
Abstract
This study uses Collins' Interactive Ritual Chain Theory as a framework, combined with digital media research, audiovisual analysis and cross-cultural communication theory, to analyze the interactive ritual phenomenon of bullet comments in the 2025 Toronto Masters live broadcast of "Fearless Contract". This study collects Chinese and English bullet screen data from Bilibili and Twitch, and uses text analysis, in-depth interviews, symbol analysis and participatory observation to explore how the live broadcast of the event constructs a virtual carnival space for global players, how players complete collective emotional accumulation and release through bullet screen, and the influence of platform algorithm and cross-cultural differences on ritual solidarity. The study reveals that the bullet-screen interaction in esports live streaming not only inherits the four core elements of the traditional interactive ritual chain theory, but also expands the boundaries of "physical presence" within the theory. It further demonstrates new interactive characteristics such as "mediatic mediation," "cross-culturality," and "spatiotemporal extension," offering a fresh research perspective for understanding collective carnival rituals in the digital age.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".