[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]
BMC Public Health. 2019 Apr 25;19(1):438. doi: 10.1186/s12889-019-6747-8.
How did Ebola information spread on twitter: broadcasting or viral spreading?
Liang H1,2, Fung IC3,4,5, Tse ZTH6, Yin J3, Chan CH2, Pechta LE7, Smith BJ8, Marquez-Lameda RD7, Meltzer MI4, Lubell KM7, Fu KW9.
Author information: 1 School of Journalism and Communication, The Chinese University of Hong Kong, Hong Kong, Hong Kong. 2 Journalism and Media Studies Centre, The University of Hong Kong, Hong Kong, Hong Kong. 3 Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, USA. 4 Health Economics and Modeling Unit, Scientific and Program Service Branch, Division of Preparedness and Emerging Infections, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, USA. 5 IHRC, Inc., Atlanta, USA. 6 School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, USA. 7 Research and Evaluation Team, Emergency Response Communication Branch, Division of Emergency Operations, Office of Public Health Preparedness and Response, Centers for Disease Control and Prevention, Atlanta, USA. 8 McKing Consulting Corporation, Atlanta, USA. 9 Journalism and Media Studies Centre, The University of Hong Kong, Hong Kong, Hong Kong. email@example.com.
Information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. Health information could be transmitted from one to many (i.e. broadcasting) or from a chain of individual to individual (i.e. viral spreading). The aim of this study is to examine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages.
Our data was purchased from GNIP. We obtained all Ebola-related tweets posted globally from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships. Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns.
On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcasting was more pervasive than viral spreading. We found that influential users and hidden influential users triggered more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users.
Broadcasting was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work beneficially with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger many retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion. However, challenges remain due to uncertain credibility of these hidden influential users.
KEYWORDS: Broadcast model; Ebola; Network analysis; Social media; Viral diffusion model
PMID: 31023299 DOI: 10.1186/s12889-019-6747-8
Keywords: Ebola; Public Health; Society.