Frontiers of Data and Computing ›› 2023, Vol. 5 ›› Issue (2): 119-135.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.02.010

doi: 10.11871/jfdc.issn.2096-742X.2023.02.010

• Technology and Application • Previous Articles     Next Articles

A Survey of Research on Microblog Popularity Prediction

LI Yan1,2(),HE Hongbo1,*(),WANG Runqiang1   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2022-02-17 Online:2023-04-20 Published:2023-04-24
  • Contact: HE Hongbo E-mail:yanli@cnic.cn;hhb@cnic.cn

Abstract:

[Objective] This paper is to conduct a multi-angle survey of the existing research on microblog popularity prediction, discuss the shortcomings of the existing approaches, foresee the future development trend, and provide a reference for follow-on researches. [Coverage] The paper sorts out and summarizes relevant literatures both in China and abroad in recent five years. [Methods] The paper first introduces the definition of popularity prediction and popularity calculation methods. Then the research methods of popularity prediction are analyzed from three aspects: characteristics, time sequence, and user behavior. An extensive study is conducted on the key technologies of popularity prediction. Finally, the problems of the existing methods and the prospect are summarized. [Results] Feature-based popularity prediction methods are widely used because of they are well customized. The method combining deep learning and ensemble learning is becoming the mainstream approach. [Limitations] As the dataset of the individual research is not publicly available, this study cannot make a horizontal comparison of all algorithms for the level of improvement against a unified standard. [Conclusions] Microblog popularity prediction is significant for public opinion monitoring, commercial marketing, and content promotion, etc. In the era of ever-increasing popularity of social media, the research on popularity prediction will be further promoted.

Key words: popularity prediction, microblog, machine learning, deep learning