On predicting Twitter trend: Factors and models

Peng Zhang, Xufei Wang, Baoxin Li

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

6 Scopus citations

Abstract

In this paper, we predict hashtag trend in Twitter network with two basic issues under investigation, i.e. trend factors and prediction models. To address the first issue, we consider different content and context factors by designing features from tweet messages, network topology, user behavior, etc. To address the second issue, we adopt prediction models that have different combinations of the two basic model properties, i.e. linearity and state-space. Experiments on large Twitter dataset show that both content and context factors can help trend prediction. However, the most relevant factors are derived from user behaviors on the specific trend. Non-linear models are significantly better than their linear counterparts, which can be further slightly improved by the adoption of state-space models.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
PublisherAssociation for Computing Machinery
Pages1427-1429
Number of pages3
ISBN (Print)9781450322409
DOIs
StatePublished - Jan 1 2013
Event2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 - Niagara Falls, ON, Canada
Duration: Aug 25 2013Aug 28 2013

Publication series

NameProceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013

Other

Other2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
CountryCanada
CityNiagara Falls, ON
Period8/25/138/28/13

Keywords

  • Information diffusion
  • Trend prediction
  • Twitter

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Zhang, P., Wang, X., & Li, B. (2013). On predicting Twitter trend: Factors and models. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 (pp. 1427-1429). (Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013). Association for Computing Machinery. https://doi.org/10.1145/2492517.2492576