Ebola virus disease and social media: A systematic review

Fung, Isaac Chun-Hai; Duke, Carmen H.; Finch, Kathryn C.; Snook, Kassandra R.; Tseng, Pei Ling; Hernández, Ana Cristina Gallego; Gambhir, Manoj; Fu, King‐Wa; Tse, Zion Tsz Ho

doi:10.1016/j.ajic.2016.05.011

Cited by 108 publications

(83 citation statements)

References 32 publications

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“…Social media is an emerging tool for health communication during outbreak responses [3]. Recent research on ZIKV-related social media health communication has focused on Twitter [4-6] and Facebook [7].…”

Section: Introductionmentioning

confidence: 99%

Zika Virus on YouTube: An Analysis of English-language Video Content by Source

Basch

Fung

Hammond

et al. 2017

J Prev Med Public Health

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ObjectivesThe purpose of this study was to describe the source, length, number of views, and content of the most widely viewed Zika virus (ZIKV)-related YouTube videos. We hypothesized that ZIKV-related videos uploaded by different sources contained different content.MethodsThe 100 most viewed English ZIKV-related videos were manually coded and analyzed statistically.ResultsAmong the 100 videos, there were 43 consumer-generated videos, 38 Internet-based news videos, 15 TV-based news videos, and 4 professional videos. Internet news sources captured over two-thirds of the total of 8 894 505 views. Compared with consumer-generated videos, Internet-based news videos were more likely to mention the impact of ZIKV on babies (odds ratio [OR], 6.25; 95% confidence interval [CI], 1.64 to 23.76), the number of cases in Latin America (OR, 5.63; 95% CI, 1.47 to 21.52); and ZIKV in Africa (OR, 2.56; 95% CI, 1.04 to 6.31). Compared with consumer-generated videos, TV-based news videos were more likely to express anxiety or fear of catching ZIKV (OR, 6.67; 95% CI, 1.36 to 32.70); to highlight fear of ZIKV among members of the public (OR, 7.45; 95% CI, 1.20 to 46.16); and to discuss avoiding pregnancy (OR, 3.88; 95% CI, 1.13 to 13.25).ConclusionsPublic health agencies should establish a larger presence on YouTube to reach more people with evidence-based information about ZIKV.

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Section: Introductionmentioning

confidence: 99%

Zika Virus on YouTube: An Analysis of English-language Video Content by Source

Basch

Fung

Hammond

et al. 2017

J Prev Med Public Health

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“…The simplest and perhaps most ideal approach that academicians writing about topics of public interest may consider is to summarize the current approach to the problem they are addressing. Word limits in publications may make it difficult to spend any space on what should be considered background knowledge of the reader, however scientists could also consider using social media and press releases to directly communicate the actionable items in their findings to serve as both scientific speaker and lay translator [135]. …”

Section: Discussionmentioning

confidence: 99%

Contrasting academic and lay press print coverage of the 2013-2016 Ebola Virus Disease outbreak

2017

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Under a traditional paradigm, only those with the expected background knowledge consume academic literature. The lay press, as well as government and non-government agencies, play a complementary role of extracting findings of high interest or importance and translating them for general viewing. The need for accurate reporting and public advising is paramount when attempting to tackle epidemic outbreaks through behavior change. Yet, public trust in media outlets is at a historic low. The Crisis and Emergency Risk Communication (CERC) model for media reporting on public health emergencies was established in 2005 and has subsequently been used to analyze media reporting on outbreaks of influenza and measles as well as smoking habits and medication compliance. However, no media analysis had yet been performed on the 2013–2016 Ebola Virus Disease (EVD) outbreak. This study compared the EVD information relayed by lay press sources with general review articles in the academic literature through a mixed-methods analysis. These findings suggest that comprehensive review articles could not serve as a source to clarify and contextualize the uncertainties around the EVD outbreak, perhaps due to adherence to technical accuracy at the expense of clarity within the context of outbreak conditions. This finding does not imply inferiority of the academic literature, nor does it draw direct causation between confusion in review articles and public misunderstanding. Given the erosion of the barriers siloing academia, combined with the demands of today’s fast-paced media environment, contemporary researchers should realize that no study is outside the public forum and to therefore consider shifting the paradigm to take personal responsibility in the process of accurately translating their scientific words into public policy actions to best serve as a source of clarity.

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“…As highlighted in a recent review, the vast majority of published Ebola-related social media studies were analyses of data from a very short time frame [12].…”

Section: Discussion Principal Resultsmentioning

confidence: 99%

How did Ebola Information Spread on Twitter: Broadcasting or Viral Spreading? (Preprint)

Liang¹,

Fung²,

Tse³

et al. 2018

Preprint

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Background: It has been argued that 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. For example, health information could be transmitted from one to many (i.e. broadcasting), which is similar to how traditional mass media passes information to the general public. Health information could also be transmitted from many to many (i.e. viral spreading), which is analogous to the spread of infectious diseases. Objective: The aim of this study is to determine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. On Twitter, influential users are those whose tweets receive a large number of retweets. Methods: Our data was purchased from GNIP, the official Twitter data provider. We obtained all Ebola-related tweets (including retweets and replies) posted from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships (who follows whom on Twitter). 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. Disseminators received fewer retweets than expected based on their number of followers, common users and influential users received as many or fewer retweets than expected, and hidden influential users received more retweets than expected. 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 broadcast model was more pervasive than viral spreading. Furthermore, we found that influential users and hidden influential users can trigger 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: The broadcast model was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work 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 a lot of 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, because the hidden influential users can receive...

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Ebola virus disease and social media: A systematic review

Cited by 108 publications

References 32 publications

Zika Virus on YouTube: An Analysis of English-language Video Content by Source

Zika Virus on YouTube: An Analysis of English-language Video Content by Source

Contrasting academic and lay press print coverage of the 2013-2016 Ebola Virus Disease outbreak

How did Ebola Information Spread on Twitter: Broadcasting or Viral Spreading? (Preprint)

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