Abstract

Online information propagates differently on the web, some of which can be viral. In this paper, first we introduce a simple standard deviation sigma levels based Tweet volume breakout definition, then we proceed to determine patterns of re-tweet network measures to predict whether a hashtag volume will breakout or not. We also developed a visualization tool to help trace the evolution of hashtag volumes, their underlying networks and both local and global network measures. We trained a random forest tree classifier to identify effective network measures for predicting hashtag volume breakouts. Our experiments showed that “local” network features, based on a fixed-sized sliding window, have an overall predictive accuracy of 76%, where as, when we incorporate “global” features that utilize all interactions up to the current period, then the overall predictive accuracy of a sliding window based breakout predictor jumps to 83%.

Original languageEnglish (US)
Title of host publicationSocial Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings
EditorsKevin Xu, Nitin Agarwal, Nathaniel Osgood
PublisherSpringer Verlag
Pages3-12
Number of pages10
ISBN (Electronic)9783319162676
DOIs
StatePublished - Jan 1 2015
Event8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015 - Washington, United States
Duration: Mar 31 2015Apr 3 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9021
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015
CountryUnited States
CityWashington
Period3/31/154/3/15

Keywords

  • Diffusion networks
  • Hashtag volumes
  • Information diffusion
  • Prediction
  • Social networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Alzahrani, S., Alashri, S., Koppela, A. R., Davulcu, H., & Toroslu, I. (2015). A network-based model for predicting hashtag breakouts in twitter. In K. Xu, N. Agarwal, & N. Osgood (Eds.), Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings (pp. 3-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9021). Springer Verlag. https://doi.org/10.1007/978-3-319-16268-3_1