Investigating the Role of Real-Time Chat Summaries in Supporting Live Streamers
Bibliographic record
Abstract
Live streaming platforms have become established communication channels where streamers and viewers can communicate through the chatbox. While there are numerous benefits to streamer-viewer interactions, managing messages can become challenging for streamers, especially during high-activity periods. In this paper, we investigate a strategy to supporting streamers that involves providing them with real-time summaries of chat messages. We investigate the feasibility of this approach through a prototype called the Stream Assistant, which provides automated poll summaries, a Word Cloud depiction of chat messages, and an overview of popular Emotes in the chat. We explore the potential utility of this approach in a multi-session study with 10 streamers, where we interviewed participants on their current chat management approaches and perceptions of the Stream Assistant after using it during one of their live streams. Our findings highlight the role of the chat in boosting engagement and audience growth and illustrate how streamers from different domains balance the chat with their activity. Our findings also indicate that many participants were enthusiastic about the Stream Assistant’s lightweight polling, whereas the utility of the other features might depend on the pace of the chat and the intensity of the streamer’s activities.
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 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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".