2017/07/12 by Amira Ghenai, Yelena Mejova, Ghenai, Amira +1
Computer Science · Social Sciences · #68P20 #Computers and Society (cs.CY) #FOS: Computer and information sciences #H.2.8 #H.3.3 #Hate Speech and Cyberbullying Detection #I.2.7 #J.3 #Misinformation and Its Impacts #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1707.03778
openalex publication_date 2017/07/12 · openalex created_date 2022/10/07 · openalex updated_date 2026/07/28
In February 2016, World Health Organization declared the Zika outbreak a\nPublic Health Emergency of International Concern. With developing evidence it\ncan cause birth defects, and the Summer Olympics coming up in the worst\naffected country, Brazil, the virus caught fire on social media. In this work,\nuse Zika as a case study in building a tool for tracking the misinformation\naround health concerns on Twitter. We collect more than 13 million tweets --\nspanning the initial reports in February 2016 and the Summer Olympics --\nregarding the Zika outbreak and track rumors outlined by the World Health\nOrganization and Snopes fact checking website. The tool pipeline, which\nincorporates health professionals, crowdsourcing, and machine learning, allows\nus to capture health-related rumors around the world, as well as clarification\ncampaigns by reputable health organizations. In the case of Zika, we discover\nan extremely bursty behavior of rumor-related topics, and show that, once the\nquestionable topic is detected, it is possible to identify rumor-bearing tweets\nusing automated techniques. Thus, we illustrate insights the proposed tools\nprovide into potentially harmful information on social media, allowing public\nhealth researchers and practitioners to respond with a targeted and timely\naction.\n