Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter

Amira Ghenai, Yelena Mejova

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

7 Citations (Scopus)

Abstract

In February 2016, World Health Organization declared the Zika outbreak a Public Health Emergency of International Concern. With developing evidence it can cause birth defects, and the Summer Olympics coming up in the worst affected country, Brazil, the virus caught fire on social media. In this work, we use Zika as a case study in building a tool for tracking the misinformation around health concerns on Twitter. We collect more than 13 million tweets regarding the Zika outbreak and track rumors outlined by the World Health Organization and Snopes fact checking website. The tool pipeline, which incorporates health professionals, crowdsourcing, and machine learning, allows us to capture health-related rumors around the world, as well as clarification campaigns by reputable health organizations. We discover an extremely bursty behavior of rumor-related topics, and show that, once the questionable topic is detected, it is possible to identify rumor-bearing tweets using automated techniques.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781509048816
DOIs
Publication statusPublished - 8 Sep 2017
Event5th IEEE International Conference on Healthcare Informatics, ICHI 2017 - Park City, United States
Duration: 23 Aug 201726 Aug 2017

Other

Other5th IEEE International Conference on Healthcare Informatics, ICHI 2017
CountryUnited States
CityPark City
Period23/8/1726/8/17

Fingerprint

Crowdsourcing
Communication
Disease Outbreaks
Health
Social Media
Health Promotion
Brazil
Emergencies
Public Health
Organizations
Viruses
Zika Virus Infection
Machine Learning

Keywords

  • Health
  • Medical
  • Misinformation
  • Rumors
  • Social Media
  • Twitter
  • Zika

ASJC Scopus subject areas

  • Health Informatics

Cite this

Ghenai, A., & Mejova, Y. (2017). Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter. In Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017 [8031204] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2017.58

Catching Zika Fever : Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter. / Ghenai, Amira; Mejova, Yelena.

Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8031204.

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

Ghenai, A & Mejova, Y 2017, Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter. in Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017., 8031204, Institute of Electrical and Electronics Engineers Inc., 5th IEEE International Conference on Healthcare Informatics, ICHI 2017, Park City, United States, 23/8/17. https://doi.org/10.1109/ICHI.2017.58
Ghenai A, Mejova Y. Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter. In Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8031204 https://doi.org/10.1109/ICHI.2017.58
Ghenai, Amira ; Mejova, Yelena. / Catching Zika Fever : Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter. Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017. Institute of Electrical and Electronics Engineers Inc., 2017.
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