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IJIET 2018 Vol.8(9): 685-692 ISSN: 2010-3689
doi: 10.18178/ijiet.2018.8.9.1123

Sentiment Analysis and Information Diffusion on Social Media: The Case of the Zika Virus

Chuan-Jun Su and Jorge A. Quan Yon

Abstract—First identified 50 years ago, the Zika virus has recently made global headlines due to a high profile outbreak in Brazil coinciding with the Olympics. Mentions of Zika on social media platforms exploded following initial reports of the outbreak, and this unprecedented surge of heterogeneous data can be processed using Big Data analysis techniques to acquire further insights and knowledge into general public opinion. Twitter data streams have previously been used to predict outcomes of real world events. Twitter data filtered for the keyword “Zika” was subjected to analysis using a sentiment analysis lexicon-based framework to establish the polarity of the messages. The World Health Organization (WHO) recommends avoiding exposure to Zika-infected mosquitoes as the most effective approach to prevention. The diffusion of Twitter messages citing the WHO recommendations is analyzed to help public health professionals and health agencies formulate an effective response. Our results show that the WHO recommendations were largely ignored in Twitter-based discussions related to the Zika virus.

Index Terms—Zika virus, sentiment analysis, social media, twitter, vector control, big data.

The authors are with the Department of Industrial Engineering and Management, Yuan Ze University, Taiwan, ROC (e-mail:iecjsu@saturn.yzu.edu.tw, s1038908@saturn.yzu.edu.tw).


Cite: Chuan-Jun Su and Jorge A. Quan Yon, "Sentiment Analysis and Information Diffusion on Social Media: The Case of the Zika Virus," International Journal of Information and Education Technology vol. 8, no. 9, pp. 685-692, 2018.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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