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A Classification Method for Micro-Blog Popularity Prediction: Considering the Semantic Information

  • Lei LiuEmail author
  • Chen Yang
  • Tingting LiuEmail author
  • Xiaohong Chen
  • Sung-Shun Weng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

Abstract

Predicting the scale and quantity of reposting in micro-blog network have significances to the future network marketing, hot topic detection and public opinion monitor. This study proposed a novel two-stage method to predict the popularity of a micro-blog prior to its release. By focusing on the text content of the specific micro-blog as well as its source of publication (user’s attributes), a special classification method—Labeled Latent Dirichlet allocation (LLDA) was trained to predict the volume range of future reposts for a new message. To the authors’ knowledge, this paper is the first research to utilize this multi-label text classifier to investigate the influence of one micro-blog’s topic on its reposting scale. The experiment was conducted on a large scale dataset, and the results show that it’s possible to estimate ranges of popularity with an overall accuracy of 72.56%.

Keywords

Popularity prediction Classification Semantic information Short contents LLDA Micro-blog 

Notes

Acknowledgements

This work is supported in part by National Natural Science Foundation of China (Project No. 71701134), The Humanity and Social Science Youth Foundation of Ministry of Education of China (Project No. 16YJC630153), National Taipei University of Technology- Shenzhen University Joint Research Program (Project No. 2018003), and Natural Science Foundation of Guangdong Province of China (Project No. 2017A030310427).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of ManagementShenzhen UniversityShenzhenChina
  2. 2.Department of Information and Finance ManagementNational Taipei University of TechnologyTaipeiTaiwan

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