Virtual Zika transmission after the first U.S. case: who said what and how it spread on Twitter

Published:January 04, 2018DOI:


      • Transmission, effects on pregnancy, and travel were the top 3 Zika themes on Twitter.
      • News media, public health institutions, and grassroots users exert the most influence.
      • News media play an important role during EIDOS by spreading information via Twitter.
      • Grassroots users are very visible; they amplify social concerns and conspiracy theories.
      • Social media analytics can strengthen public health agencies' efforts during EIDOs.


      This paper goes beyond detecting specific themes within Zika-related chatter on Twitter, to identify the key actors who influence the diffusive process through which some themes become more amplified than others.


      We collected all Zika-related tweets during the 3 months immediately after the first U.S. case of Zika. After the tweets were categorized into 12 themes, a cross-section were grouped into weekly datasets, to capture 12 amplifier/user groups, and analyzed by 4 amplification modes: mentions, retweets, talkers, and Twitter-wide amplifiers.


      We analyzed 3,057,130 tweets in the United States and categorized 4997 users. The most talked about theme was Zika transmission (~58%). News media, public health institutions, and grassroots users were the most visible and frequent sources and disseminators of Zika-related Twitter content. Grassroots users were the primary sources and disseminators of conspiracy theories.


      Social media analytics enable public health institutions to quickly learn what information is being disseminated, and by whom, regarding infectious diseases. Such information can help public health institutions identify and engage with news media and other active information providers. It also provides insights into media and public concerns, accuracy of information on Twitter, and information gaps. The study identifies implications for pandemic preparedness and response in the digital era and presents the agenda for future research and practice.

      Graphical abstract

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to American Journal of Infection Control
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Wagner L.
        How “Zika-Proof” Hope Solo became the biggest villian of the Rio Olympics.
        (Available from)
        • Vijaykumar S.
        • Jin Y.
        • Nowak G.
        Social media and the virality of risk: the risk amplification through media spread (RAMS) model.
        J Homel Secur Emerg Manag. 2015; 12: 653-677
        • Pidgeon N.
        • Kasperson R.E.
        • Slovic P.
        The social amplification of risk.
        Cambridge University Press, Cambridge, UK2003
        • Centers for Disease Control and Prevention
        Data and metrics.
        (Available from)
        Date accessed: September 25, 2016
        • World Health Organization
        Zika strategic response framework and joint operations plan.
        (Available from)
        • Wong R.
        • Harris J.K.
        • Staub M.
        • Bernhardt J.M.
        Local health departments tweeting about Ebola: characteristics and messaging.
        J Public Health Manag Pract. 2017; 23: e16-e24
        • Statista
        Number of monthly active Twitter users worldwide from 1st quarter 2010 to 2nd quarter 2017 (in millions).
        (Available from)
        • Bernardo T.M.
        • Rajic A.
        • Young I.
        • Robiadek K.
        • Pham M.T.
        • Funk J.A.
        Scoping review on search queries and social media for disease surveillance: a chronology of innovation.
        J Med Internet Res. 2013; 15: e147
        • Brownstein J.S.
        • Freifeld C.C.
        • Madoff L.C.
        Digital disease detection—harnessing the Web for public health surveillance.
        N Engl J Med. 2009; 360: 2153-2157
        • Thackeray R.
        • Neiger B.L.
        • Smith A.K.
        • Van Wagenen S.B.
        Adoption and use of social media among public health departments.
        BMC Public Health. 2012; 12: 1
        • Merchant R.M.
        • Elmer S.
        • Lurie N.
        Integrating social media into emergency-preparedness efforts.
        N Engl J Med. 2011; 365: 289-291
        • Keim M.E.
        • Noji E.
        Emergent use of social media: a new age of opportunity for disaster resilience.
        Am J Disaster Med. 2010; 6: 47-54
        • Chou W.-Y.S.
        • Hunt Y.M.
        • Beckjord E.B.
        • Moser R.P.
        • Hesse B.W.
        Social media use in the United States: implications for health communication.
        J Med Internet Res. 2009; 11: e48
        • Moorhead S.A.
        • Hazlett D.E.
        • Harrison L.
        • Carroll J.K.
        • Irwin A.
        • Hoving C.
        A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication.
        J Med Internet Res. 2013; 15: e85
        • Korda H.
        • Itani Z.
        Harnessing social media for health promotion and behavior change.
        Health Promot Pract. 2013; 14: 15-23
        • Aramaki E.
        • Maskawa S.
        • Morita M.
        Twitter catches the flu: detecting influenza epidemics using Twitter.
        (In Proceedings of the Conference on Empirical Methods in Natural Language Processing; Edinburgh, United Kingdom; July 27-31)2011
        • Chew C.
        • Eysenbach G.
        Pandemics in the age of Twitter: content analysis of tweets during the 2009 H1N1 outbreak.
        PLoS ONE. 2010; 5: e14118
        • Fung I.C.-H.
        • Tse Z.T.H.
        • Cheung C.-N.
        • Miu A.S.
        • Fu K.-W.
        Ebola and the social media.
        Lancet. 2014; 384: 2207
        • Paul M.J.
        • Dredze M.
        You are what you tweet: analyzing Twitter for public health.
        Proc Int AAAI Conf Weblogs Soc Media. 2011; 20: 265-272
        • Signorini A.
        • Segre A.M.
        • Polgreen P.M.
        The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic.
        PLoS ONE. 2011; 6: e19467
        • Odlum M.
        • Yoon S.
        What can we learn about the Ebola outbreak from tweets?.
        Am J Infect Control. 2015; 43: 563-571
        • Yoon S.
        • Elhadad N.
        • Bakken S.
        A practical approach for content mining of tweets.
        Am J Prev Med. 2013; 45: 122-129
        • Oyeyemi S.O.
        • Gabarron E.
        • Wynn R.
        Ebola, Twitter, and misinformation: a dangerous combination?.
        BMJ. 2014; 349: g6178
        • Fu K.-W.
        • Liang H.
        • Saroha N.
        • Tse Z.T.H.
        • Ip P.
        • Fung I.C.-H.
        How people react to Zika virus outbreaks on Twitter? A computational content analysis.
        Am J Infect Control. 2016; 44: 1700-1702
        • Khatua A.
        • Khatua A.
        Immediate and long-term effects of 2016 Zika Outbreak: a Twitter-based study.
        (In Proceedings of the; IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom); Munich, Germany; September 14-17)2016
        • Southwell B.G.
        • Dolina S.
        • Jimenez-Magdaleno K.
        • Squiers L.B.
        • Kelly B.J.
        Zika virus–related news coverage and online behavior, United States, Guatemala, and Brazil.
        Emerg Infect Dis. 2016; 22: 1320
        • Stefanidis A.
        • Vraga E.
        • Lamprianidis G.
        • Radzikowski J.
        • Delamater P.L.
        • Jacobsen K.H.
        • et al.
        Zika in Twitter: temporal variations of locations, actors, and concepts.
        JMIR Public Health Surveill. 2017; 3: e22
        • The Guardian
        Zika emergency pushes women to challenge Brazil's abortion law.
        (Available from)
        • Chung J.E.
        A smoking cessation campaign on Twitter: understanding the use of Twitter and identifying major players in a health campaign.
        J Health Commun. 2016; 21: 517-526
        • Cha M.
        • Haddadi H.
        • Benevenuto F.
        • Gummadi P.K.
        Measuring user influence in Twitter: the million follower fallacy.
        Proc Int AAAI Conf Weblogs Soc Media. 2010; 10: 30
        • Kwak H.
        • Lee C.
        • Park H.
        • Moon S.
        What is Twitter, a social network or a news media?.
        (Proceedings of the 19th International Conference on World Wide Web; Raleigh, NC; April 26-30)2010
        • Bakshy E.
        • Hofman J.M.
        • Mason W.A.
        • Watts D.J.
        Identifying influencers on Twitter.
        (Proceedings of the Fourth ACM International Conference on Web Seach and Data Mining (WSDM); Hong Kong, China; February 9-12)2011
        • McAllister-Spooner S.M.
        Fulfilling the dialogic promise: a ten-year reflective survey on dialogic Internet principles.
        Public Relat Rev. 2009; 35: 320-322
        • Sommerfeldt E.J.
        • Kent M.L.
        • Taylor M.
        Activist practitioner perspectives of website public relations: why aren't activist websites fulfilling the dialogic promise?.
        Public Relat Rev. 2012; 38: 303-312
        • Guidry J.P.
        • Jin Y.
        • Orr C.A.
        • Messner M.
        • Meganck S.
        Ebola on Instagram and Twitter: how health organizations address the health crisis in their social media engagement.
        Public Relat Rev. 2017; 43: 477-486
        • Petrovic S.
        • Osborne M.
        • Lavrenko V.
        RT to win! Predicting message propagation in Twitter.
        (Proceedings of the 5th. International AAAI Conference on Weblogs and Social Media; Barcelona, Spain; July 17-21)2011
        • Fowler A.
        • Margolis M.
        The political consequences of uninformed voters.
        Elect Stud. 2014; 34: 100-110
        • Reedy J.
        • Wells C.
        • Gastil J.
        How voters become misinformed: an investigation of the emergence and consequences of false factual beliefs.
        Soc Sci Q. 2014; 95: 1399-1418
        • Tafuri S.
        • Gallone M.
        • Cappelli M.
        • Martinelli D.
        • Prato R.
        • Germinario C.
        Addressing the anti-vaccination movement and the role of HCWs.
        Vaccine. 2014; 32: 4860-4865
        • Weeks B.E.
        Emotions, partisanship, and misperceptions: how anger and anxiety moderate the effect of partisan bias on susceptibility to political misinformation.
        J Commun. 2015; 65: 699-719
        • Bode L.
        • Vraga E.K.
        In related news, that was wrong: the correction of misinformation through related stories functionality in social media.
        J Commun. 2015; 65: 619-638
        • Borge-Holthoefer J.
        • Moreno Y.
        Absence of influential spreaders in rumor dynamics.
        Phys Rev E. 2012; 85: 026116
        • Yoon S.
        • Bakken S.
        Methods of knowledge discovery in tweets.
        (In; NI 2012: Proceedings of the 11th International Congress on Nursing Informatics; Montreal, Canada; June 23-27)2012
        • Rosado L.
        Facebook continues to dominate social-network use in Puerto Rico.
        (Available from)
        • Pew Research Center
        Social media update 2016.
        (Available from)
        • World Health Organization
        WHO statement on the first meeting of the International Health Regulations (2005) (IHR 2005) Emergency Committee on Zika virus and observed increase in neurological disorders and neonatal malformations.
        (Available from)
        • Househ M.
        Communicating Ebola through social media and electronic news media outlets: a cross-sectional study.
        Health Informatics J. 2016; 22: 470-478
        • Vijaykumar S.
        • Meurzec R.W.
        • Jayasundar K.
        • Pagliari C.
        • Fernandopulle Y.
        What's buzzing on your feed? Health authorities' use of Facebook to combat Zika in Singapore.
        J Am Med Inform Assoc. 2017; 24: 1155-1159
        • Allcott H.
        • Gentzkow M.
        Social media and fake news in the 2016 election.
        J Econ Perspect. 2017; 31: 211-236
        • van Lent L.G.
        • Sungur H.
        • Kunneman F.A.
        • van de Velde B.
        • Das E.
        Too far to care? Measuring public attention and fear for Ebola using Twitter.
        J Med Internet Res. 2017; 19: e193