How did Ebola information spread on Twitter: broadcasting or viral spreading?


Liang, Hai ; Fung, Isaac Chun-Hai ; Tse, Zion Tsz Ho ; Yin, Jingjing ; Chan, Chung-hong ; Pechta, Laura E. ; Smith, Belinda J. ; Marquez-Lameda, Rossmary D. ; Meltzer, Martin I. ; Lubell, Keri M. ; Fu, King-wa


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DOI: https://doi.org/10.1186/s12889-019-6747-8
URL: https://madoc.bib.uni-mannheim.de/58635
Additional URL: https://bmcpublichealth.biomedcentral.com/articles...
URN: urn:nbn:de:bsz:180-madoc-586358
Document Type: Article
Year of publication: 2019
The title of a journal, publication series: BMC Public Health
Volume: 19
Issue number: Article 438
Page range: 1-11
Place of publication: London
Publishing house: BioMed Central
ISSN: 1471-2458
Publication language: English
Institution: Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department B
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 320 Political science
Abstract: Background 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. Methods 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. Results 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. Conclusions 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.
Additional information: Online-Ressource

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