Real-Time prediction of meme burst

Jie Bai, Linjing Li, Lan Lu, Yanwu Yang, Dajun Zeng

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

2 Scopus citations

Abstract

Predicting meme burst is of great relevance to develop security-related detecting and early warning capabilities. In this paper, we propose a feature-based method for real-Time meme burst predictions, namely 'Semantic, Network, and Time' (SNAT). By considering the potential characteristics of bursty memes, such as the semantics and spatio-Temporal characteristics during their propagation, SNAT is capable of capturing meme burst at the very beginning and in real time. Experimental results prove the effectiveness of SNAT in terms of both fixed-Time and real-Time meme burst prediction tasks.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Intelligence and Security Informatics
Subtitle of host publicationSecurity and Big Data, ISI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages167-169
Number of pages3
ISBN (Electronic)9781509067275
DOIs
StatePublished - Aug 8 2017
Event15th IEEE International Conference on Intelligence and Security Informatics, ISI 2017 - Beijing, China
Duration: Jul 22 2017Jul 24 2017

Other

Other15th IEEE International Conference on Intelligence and Security Informatics, ISI 2017
CountryChina
CityBeijing
Period7/22/177/24/17

Keywords

  • meme burst
  • real-Time prediction
  • semantic analysis
  • spatio-Temporal analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Bai, J., Li, L., Lu, L., Yang, Y., & Zeng, D. (2017). Real-Time prediction of meme burst. In 2017 IEEE International Conference on Intelligence and Security Informatics: Security and Big Data, ISI 2017 (pp. 167-169). [8004900] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISI.2017.8004900