학술논문
Repost Number Prediction of Micro-blog on Sina Weibo Using Time Series Fitting and Regression Analysis
Document Type
Conference
Author
Source
2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI) Identification, Information, and Knowledge in the Internet of Things (IIKI), 2015 International Conference on. :66-69 Oct, 2015
Subject
Language
Abstract
Sina Weibo, as the most popular micro-blog platform in China, has become a major source of network hot events and sensitive public opinion. This paper presents a scheme to predict the repost number of micro-blog message. Curve fitting and time-series model are used for the prediction. In order to improve the predicting precision, an empirical correction model are built by utilizing the prediction data of 3200 micro-blog messages using least square and second-order polynomial regression methods, which takes the daily periodic fluctuation of reposting probability into consideration. By experimental verification, the proposed scheme can predict the repost number of micro-blog message accurately.