Video Game Livestream Trend Analysis Based on Twitch Livestream Data
- DOI
- 10.2991/aebmr.k.220307.470How to use a DOI?
- Keywords
- Twitch; video game; livestream; e-sports industry; data analysis; prospect prediction
- Abstract
With the continuous improvement of the technological level of networks, terminals, and videos, people’s demand for entertainment consumption has expanded rapidly, and the country’s policy to promote the upgrading of residents’ consumption structure has promoted the vigorous development of the e-sports industry. The game live broadcast is an important part of the e-sports industry. Its real-time interactivity and unique display receive many audience’s attention and love. Live broadcasting is not only a way of information transmission, but also an entertainment and cultural phenomenon. The theme is favored by venture capital, attracting the participation of many anchors and players, and generating huge traffic and revenue. As competition intensifies, the trends, problems behind this industry are worth digging into.
Using clustering, classification, etc. to analyze the Twitch Livestream data in 2016- 2021, which comes from Kaggle.com, the article would research the audience, earnings, and prospects in the video game livestream industry.
Through the analysis of the article, it is found that the distribution of Twitch users is unbalanced at present and the market competition is becoming intensified. Therefore, some suggestions are given, such occupying the high user traffic entrance, expanding the industrial chain.
- Copyright
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Luwei Yao PY - 2022 DA - 2022/03/26 TI - Video Game Livestream Trend Analysis Based on Twitch Livestream Data BT - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) PB - Atlantis Press SP - 2890 EP - 2895 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.220307.470 DO - 10.2991/aebmr.k.220307.470 ID - Yao2022 ER -