Interactions across Displays and Space: A Study of Virtual Reality Streaming Practices on Twitch
Bibliographic record
Abstract
The growing live streaming economy and virtual reality (VR) technologies have sparked interest in VR streaming among streamers and viewers. However, limited research has been conducted to understand this emerging streaming practice. To address this gap, we conducted an in-depth thematic analysis of 34 streaming videos from 12 VR streamers with varying levels of experience, to explore the current practices, interaction styles, and strategies, as well as to investigate the challenges and opportunities for VR streaming. Our findings indicate that VR streamers face challenges in building emotional connections and maintaining streaming flow due to technical problems, lack of fluid transitions between physical and virtual environments, and not intentionally designed game scenes. As a response, we propose six design implications to encourage collaboration between game designers and streaming app developers, facilitating fluid, rich, and broad interactions for an enhanced streaming experience. In addition, we discuss the use of streaming videos as user-generated data for research, highlighting the lessons learned and emphasizing the need for tools to support streaming video analysis. Our research sheds light on the unique aspects of VR streaming, which combines interactions across displays and space.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".