Mining and Analysis of Big Data Statistical Characteristics and Timing Rules for Webcasting

Abstract

In recent years, the rapid development of network information technology has made online live broadcast a new hot spot. In webcasting, the anchor and the audience can interact a lot. Through the study of the interaction behavior of the anchor and the audience, it can not only deepen the understanding of the content and production process of the webcasting, but also promote the healthy development of the live broadcast industry. This paper analyzes the load time series and user behavior of the webcast platform. The results show that the live broadcast load has obvious intraday and weekly effects, and the anchors in each live broadcast system have significant inter-group differences in statistical characteristics such as the number of viewers and fans. The survival time of the anchors and the number of viewers in the live broadcast room are distributed as a power function. According to statistics, the number of anchors and viewers also shows a linear relationship. Therefore, it is particularly important to use the timing rule mining algorithm to analyze the statistical characteristics of webcasting.