A short-term prediction model of topic popularity on microblogs

Juanjuan Zhao, Weili Wu, Xiaolong Zhang, Yan Qiang, Tao Liu, Lidong Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Online social networks can be used as networks of human sensors to detect important events. It is important to detect important events as early as possible. Microblogs provide a new communication and information sharing platform for people to report daily-life events, and express their views on various issues. Because of the quickness of microblogs, microblog data can be used to predict popular topics. In this paper, we propose a short-term prediction model of topic popularity. With data from Sina Weibo, the most popular microblog service in China, we test our algorithm and our data shows that the proposed model could give a short-term prediction on topic popularity.

Original languageEnglish (US)
Title of host publicationComputing and Combinatorics - 19th International Conference, COCOON 2013, Proceedings
Pages759-769
Number of pages11
DOIs
StatePublished - Oct 8 2013
Event19th International Computing and Combinatorics Conference, COCOON 2013 - Hangzhou, China
Duration: Jun 21 2013Jun 21 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7936 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Computing and Combinatorics Conference, COCOON 2013
CountryChina
CityHangzhou
Period6/21/136/21/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhao, J., Wu, W., Zhang, X., Qiang, Y., Liu, T., & Wu, L. (2013). A short-term prediction model of topic popularity on microblogs. In Computing and Combinatorics - 19th International Conference, COCOON 2013, Proceedings (pp. 759-769). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7936 LNCS). https://doi.org/10.1007/978-3-642-38768-5_69