Title: Catching Zika Fever: Application of Crowdsourcing and Machine Learning for Tracking Health Misinformation on Twitter,
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,
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 --
spanning the initial reports in February 2016 and the Summer Olympics --
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. In the case of Zika, 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. Thus, we illustrate insights the proposed tools
provide into potentially harmful information on social media, allowing public
health researchers and practitioners to respond with a targeted and timely
action.