Predicting popularity of microblogs in emerging disease event

Jiaqi Liu, Zhidong Cao, Dajun Zeng

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

1 Scopus citations

Abstract

During emerging disease outbreaks, massive information are disseminated through social network. In China, Sina microblog system as the biggest social network provide a novel way to monitoring the development of emerging disease and public awareness. However, only a small percentage of microblogs could wide spread. Therefore, predict popularity of microblogs timely are meaningful for emergency management. In this paper, a Judgment method for popularity level prediction of microblog is proposed and the temporal pattern between cases number and repost number is verified. Repost number is considered to measure the impact of microblogs. To predict the popularity of microblogs, Granger causality test was used to verify the temporal correlation pattern between development of disease and public concern while an Judgment method based on five classical classification models were proposed. Through analyses, case number of emerging disease are Granger causality of the popularity level of microblogs and the regression model got the best result when lag was three. By Judgment method, more than 86% microblogs can be classified correctly. The proposed Judgment method based on user, microblog and emerging disease information could analysis the popularity level of microblogs speedily and accurately. This is important and meaningful for monitoring the development of future public health event.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages3-13
Number of pages11
Volume8597
ISBN (Print)9783319115375
DOIs
Publication statusPublished - 2014
Event36th German Conference on Pattern Recognition, GCPR 2014 - Münster, Germany
Duration: Sep 2 2014Sep 5 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8597
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other36th German Conference on Pattern Recognition, GCPR 2014
CountryGermany
CityMünster
Period9/2/149/5/14

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Keywords

  • Classification
  • Granger causality
  • Microblogs
  • Popularity prediction

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Liu, J., Cao, Z., & Zeng, D. (2014). Predicting popularity of microblogs in emerging disease event. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8597, pp. 3-13). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8597). Springer Verlag. https://doi.org/10.1007/978-3-319-11538-2_1