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

TL;DR

This paper presents a tool that combines crowdsourcing and machine learning to track and identify health misinformation on Twitter during the Zika outbreak, providing timely insights for public health response.

Contribution

The study introduces a novel pipeline integrating crowdsourcing and machine learning for real-time detection of health-related rumors on social media.

Findings

Rumor topics exhibit bursty behavior during outbreaks.

Automated techniques can identify rumor-bearing tweets.

The tool enables targeted responses by health authorities.

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.