|
|
|
| Data Mining of Live Streaming Platforms: Statistical Characteristics and Temporal Pattern |
| GUO Shuhui, LÜ Xin
|
| College of Systems Engineering, National University of Defense Technology, Changsha 410073, China |
|
|
|
|
Abstract To explore the behavioral characteristics of massive crowds under the active interaction of millions of streamers and viewers in the field of live streaming, this paper summarized the temporal patterns of live streaming workload and user behavior characteristics of the live streaming platform, taking Douyu and Huya live streaming platforms as examples, a statistical analysis of 123 consecutive days, involving more than 2.4 million anchors, and more than 726 million live streaming data. The live streaming workload has obvious intra-day and intra-week effect. Different live streaming modes have significant differences in live streaming characteristics such as the average number of viewers and followers. The lifetime of streamers and the number of viewers conform to a power law distribution. With the development of the platform, there is a strong linear correlation between the number of streamers and viewers, but its volatility is gradually increasing, reflecting the increasingly strong heterogeneity and non-uniformity of the system. It is of great significance for understanding user behavior patterns in complex systems of live streaming, mining user distribution laws and changing trends, and designing business models such as personalized recommendations.
|
|
Received: 06 September 2021
Published: 21 July 2023
|
|
|
|
|
|
|
|
|
