Discovering seasonal patterns of smoking behavior using online search information

Zhu Zhang, Xiaolong Zheng, Daniel Dajun Zeng, Kainan Cui, Chuan Luo, Saike He, Scott Leischow

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

4 Scopus citations

Abstract

Discovering temporal patterns and changes in tobacco use has important practical implications in tobacco control. This paper presents one of the first comprehensive international studies of seasonal smoking patterns based on online searches performed. Using periodogram and cross-correlation, we find that smoking-related search behavior shows strong seasonality effect across countries. In addition, there are significant pairwise associations between such seasonality in different countries.

Original languageEnglish (US)
Title of host publicationIEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics
Subtitle of host publicationBig Data, Emergent Threats, and Decision-Making in Security Informatics
Pages371-373
Number of pages3
DOIs
StatePublished - 2013
Event11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013 - Seattle, WA, United States
Duration: Jun 4 2013Jun 7 2013

Publication series

NameIEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics

Other

Other11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013
Country/TerritoryUnited States
CitySeattle, WA
Period6/4/136/7/13

Keywords

  • behavior informatics
  • search behavior
  • seasonality
  • smoking

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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