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How did Ebola information spread on twitter: broadcasting or viral spreading?
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 t...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485141/ https://www.ncbi.nlm.nih.gov/pubmed/31023299 http://dx.doi.org/10.1186/s12889-019-6747-8 |
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author | 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 |
author_facet | 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 |
author_sort | Liang, Hai |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12889-019-6747-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6485141 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64851412019-05-03 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 BMC Public Health Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12889-019-6747-8) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-25 /pmc/articles/PMC6485141/ /pubmed/31023299 http://dx.doi.org/10.1186/s12889-019-6747-8 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article 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 How did Ebola information spread on twitter: broadcasting or viral spreading? |
title | How did Ebola information spread on twitter: broadcasting or viral spreading? |
title_full | How did Ebola information spread on twitter: broadcasting or viral spreading? |
title_fullStr | How did Ebola information spread on twitter: broadcasting or viral spreading? |
title_full_unstemmed | How did Ebola information spread on twitter: broadcasting or viral spreading? |
title_short | How did Ebola information spread on twitter: broadcasting or viral spreading? |
title_sort | how did ebola information spread on twitter: broadcasting or viral spreading? |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485141/ https://www.ncbi.nlm.nih.gov/pubmed/31023299 http://dx.doi.org/10.1186/s12889-019-6747-8 |
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