Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics

A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, publ...

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Bibliographic Details
Published in:Discrete dynamics in nature and society 2021-05, Vol.2021, p.1-19
Main Authors: Chen, Xiaoliang, Lan, Xiang, Wan, Jihong, Lu, Peng, Yang, Ming
Format: Article
Language:English
Subjects:
Analysis
Deep learning
Dependent variables
Evolution
Prediction models
Propagation
Public opinion
Social media
Social network analysis
Social networks
Time dependence
Time series
Trends
User generated content
Virtual communities
Volatility
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Summary:A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N-SEP2M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N-SEP2M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.
ISSN:1026-0226
1607-887X
DOI:10.1155/2021/5551718