A Magic “Bullet”: Exploring Sport Fan Usage of On-Screen, Ephemeral Posts During Live Stream Sessions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The bullet-screen function is an augmented comment feature that has been adopted by the majority of Over-the-Top (OTT) services to foster users’ interaction and watching experience. This feature empowers sports customers to post and view numerous, short, and fast-moving comments that overlap over the screen while watching live stream sports events in real time. This research aims to investigate how sports fans embrace the bullet-screen feature while watching live stream sports. Through a combination of thematic analyzing bullet-screen comments from a National Basketball Association Finals game, and semi-structured interviews among bullet-screen users ( N = 15), the results indicate that sport fans’ bullet-screen messages could be classified into five categories: critical commentary, socialization, supportive interactions, random messages, and trash talk. Four motives for sports fans to engage with bullet-screen posting were identified: entertainment, gathering information, interaction, and finding belonging. The study also showed that the inappropriateness of comments and too much overlay on the screen could prevent sports fans from utilizing the service. Theoretical and practical implications have also been discussed.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it