Promoting Mental Well-Being for Audiences in a Live-Streaming Game by Highlight-Based Bullet Comments
Clicks: 50
ID: 283529
2021
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
14.7
/100
50 views
17 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #551 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
This paper proposes a method for generating bullet comments for
live-streaming games based on highlights (i.e., the exciting parts of video
clips) extracted from the game content and evaluate the effect of mental health
promotion. Game live streaming is becoming a popular theme for academic
research. Compared to traditional online video sharing platforms, such as
Youtube and Vimeo, video live streaming platform has the benefits of
communicating with other viewers in real-time. In sports broadcasting, the
commentator plays an essential role as mood maker by making matches more
exciting. The enjoyment emerged while watching game live streaming also
benefits the audience's mental health. However, many e-sports live streaming
channels do not have a commentator for entertaining viewers. Therefore, this
paper presents a design of an AI commentator that can be embedded in live
streaming games. To generate bullet comments for real-time game live streaming,
the system employs highlight evaluation to detect the highlights, and generate
the bullet comments. An experiment is conducted and the effectiveness of
generated bullet comments in a live-streaming fighting game channel is
evaluated.
| Reference Key |
paliyawan2021promoting
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Junjie H. Xu; Yulin Cai; Zhou Fang; Pujana Paliyawan |
| Journal | arXiv |
| Year | 2021 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.