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1
Zika Virus on YouTube: an analysis of English language video contents by source
Corey H. Basch, EdD, MPH, 1†
Isaac Chun-Hai Fung, PhD, 2†*
Rodney Hammond, MPH, 1
Elizabeth B. Blankenship, BS, 2
Zion Tsz Ho Tse, PhD, 3
King-Wa Fu, PhD, 4
Patrick Ip, MBBS, MPH, 5
Charles E. Basch, PhD. 6
1 Department of Public Health, College of Science and Health, William Paterson University, Wayne, New
Jersey, 07470.
2 Department of Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public
Health, Georgia Southern University, Statesboro, Georgia, 30460-8015.
3 College of Engineering, The University of Georgia, Athens, Georgia, 30602.
4 Journalism and Media Studies Centre, The University of Hong Kong, Hong Kong.
5 Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University
of Hong Kong, Hong Kong.
6 Department of Health and Behavior Studies, Teachers College, Columbia University, New York City,
NY, 10027.
† Co-first authors
* Correspondence should be addressed to Prof. Isaac Chun-Hai Fung, Jiann-Ping Hsu College of Public
Health, Georgia Southern University, P.O. Box 8015, Statesboro, Georgia, 30460-8015. Telephone: +1
912 478 5079. Fax: +1 912 478 0171. Email: cfung@georgiasouthern.edu.
Word count, n = 1978. Abstract, n = 199. Journal of Preventive Medicine & Public Health.
2
Abstract (201 words)
Objective: The purpose of this study was to describe 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 contain different content.
Methods: The 100 most viewed English ZIKV-related videos were manually coded and analyzed
statistically.
Results: Among 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 8,894,505 views. Compared with consumer-generated videos, internet-based news videos were more
likely to mention ZIKV’s impact on babies (Odds ratio, 6.25; 95% CI, 1.64, 23.76), the number of cases
in Latin America (OR, 5.63; 95% CI, 1.47, 21.52); and ZIKV in Africa (OR, 2.56; 95% CI, 1.04, 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, 32.70); to highlight that the public was afraid of ZIKV
(OR, 7.45; 95% CI, 1.20, 46.16); and to discuss avoiding pregnancy (OR, 3.88; 95% CI, 1.13, 13.25).
Conclusions: Public health agencies should establish a larger presence on YouTube to reach more people
with evidence-based information about ZIKV.
Key Words: Zika Virus, Health Communication, YouTube, Social Media
3
Introduction
Zika Virus (ZIKV) is known to cause microcephaly among some neonates born to ZIKV-infected women
and is associated with the onset of Guillain-Barre Syndrome among some infected patients.1, 2 As no
vaccine or treatment is available, health communication becomes a key intervention apart from mosquito
control.
Social media is an emerging tool for health communication during outbreak responses.3 Recent research
on ZIKV-related social media health communication focuses on Twitter4-6 and Facebook.7 Previous
research has highlighted the importance of visual images in health communication. 8Accounting for 22%
of social media traffic, YouTube is the second most popular social media in the United States.9 Previous
studies found that YouTube videos about outbreaks can attract thousands, even millions, of views, but
many of these videos are created and uploaded by non-professionals.10 News videos uploaded online draw
considerable attention from social media users and stimulate ‘traffic’ between sites. For example, a study
suggested that Ebola-related online videos released by two major news channels drove up Ebola-related
Twitter traffic in 2014.11
While health agencies endeavor to communicate a core set of messages about infectious disease to the
public, people also acquire health information from non-professional sources, including videos posted by
consumers, which sometimes contain content that is inconsistent with the best available scientific
evidence.12 This runs the risk of confusing the general public and undermining the effectiveness of public
health communication campaigns. Content analysis of YouTube videos provides public health
professionals with an overview of the information that the populations they serve are most likely to
receive.
4
We present a cross-sectional study of English language ZIKV-related YouTube videos in which their
source, length, number of views and contents were manually coded. Our hypothesis is that ZIKV-related
YouTube videos created and uploaded by different sources contain different content. More specifically,
the contents of internet-based news videos, TV-based news videos, and videos created by medical
professionals or government agencies are different from those of videos uploaded by individual lay
consumers.
Methods
Data retrieval. We searched for “Zika virus” on YouTube.com with default “content location” (United
States). Popularity was determined by total view count, which was determined by sorting videos
according to how many times they had been viewed. In order to reach the study goal of watching the 100
most popular videos, a total of 253 videos were viewed; 153 videos were excluded because they were not
in English. One of the primary sources of information used to create categories was the main ZIKV
webpage from the Centers for Disease Control and Prevention (CDC).13 At the time the categories were
made, information on the page was last reviewed February 11, 2016 and all updates until March 14, 2016
were included. A sample of 15 videos, that were not included in the sample of 100 videos due to low
viewership, was used to inductively generate additional content categories. Categories for the source,
length, and date that the video was uploaded were also parts of the coding instrument.
Manual coding. One author coded the entire sample of 100 videos. First, the source of the uploaded video
was determined. Consumer videos were delineated as those posted by a member of the lay public.
Professional videos were those posted by an individual with the qualifications to be working in a medical
profession. Network television was distinguished as shows that were focused on entertainment, whereas
television-based news clips were focused on providing news and information to the viewer. Internet-based
news was considered the provision of news and information on a website that was not affiliated with a
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television station. A government source was defined as anything with a tag from a government agency.
Finally, print or radio sources were videos created by a conventional print or radio source. For each video,
the following information was documented: its source, its year uploaded, its length in minutes, and its
total number of views as of May 9, 2016.
Content categories were coded dichotomously as “yes” or “no” per mention of the topic of each category:
general and specific modes of disease transmission (mosquito, male sex partner, transfusion, other
avenues); impact on infants; ZIKV treatment; anxiety and fear of catching ZIKV; modes of prevention;
number of cases overall, in Latin America, and in the U.S., respectively; public fear of ZIKV; avoiding
pregnancy; lack of preparedness in the U.S.; highlights of specific ZIKV cases in the U.S.; need for
financial aid in non-U.S. countries; need for medical help/medical resources in Latin America; need for
the U.S. to allocate additional funds for disaster preparedness; need for international cooperation/response;
need for training of healthcare personnel; need for coordination between local, state, and federal
government; danger for healthcare personnel; Olympics in Brazil; ZIKV is a hoax, is intentional
population control, or other conspiracy theories; and the video is a part of a comedy skit/parody or prank
about ZIKV.
To demonstrate high inter-rater reliability, 15 of the 100 video samples were randomly chosen and
double-coded by a second researcher. There was 100% agreement between two coders for all content
variables. The two coders agreed on the category of the source for 14 of the 15 videos, reconciling the one
disagreement.
Statistical analysis. Analysis was conducted in R 3.3.0.14 For the sources of videos, we merged the
“Government” category (n=3) and the “Professional” category (n=1) as a combined “Professional”
category (n=4). For the length of videos, given that the distribution was not normal, we performed
6
Kruskal-Wallis H Test across the four source categories and Wilcoxon rank sum test between the
categories in a pairwise manner. We performed univariate logistic regression with the source of the video
as the predictor variable and the manually coded content variables as outcome variables. We calculated
the odds ratio of a specific type of videos (professional, internet-based news and television-based news)
showing a specific type of content as compared with the reference category (consumers’ videos).
Ethical approval. This study was determined not to be human subject research by the institutional review
board at William Paterson University.
Results
Among the 100 manually coded ZIKV-related YouTube videos, there were 43 consumer-generated videos,
38 internet-based news videos, 15 TV-based news videos and 4 professional videos (Table 1).
Collectively, these videos were viewed 8,894,505 times. Internet-based news videos and consumer
videos accounted for 67.7% and 22.4% of the total 8,894,505 views respectively. The distributions of
video lengths are significantly different across the four source categories (Kruskal-Wallis χ2 =12.215,
p=0.007). Pairwise comparison found that there were statistically significant differences between
consumer-generated videos and television-based news videos (W=511, p=0.0008424), and between
television-based news videos and internet-based news videos (W=175, p=0.03061). Likewise, the
distributions of number of views are significantly different across the four source categories (Kruskal-
Wallis χ2=9.4735, p =0.02). Pairwise comparison found that there was a statistically significant difference
between consumer-generated videos and internet-based news videos (W=529, p=0.006). Across the 100
videos, a small positive correlation existed between the lengths and the number of views of videos
(Spearman’s rho=0.24, S=127160, p=0.02).
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Table 2 presents the frequency of ZIKV-related YouTube videos by their content and source categories.
Odds ratios for each content category covered by source, using consumer-generated videos as a reference
category, are presented in Table 3. Compared with consumer-generated videos, internet-based news
videos were 6.25 times more likely to mention ZIKV’s impact on babies (95% confidence interval, CI,
1.64, 23.76); 5.63 times more likely to mention the number of cases in Latin America (95% CI, 1.47,
21.52); and 2.56 times more likely to mention ZIKV in Africa (95% CI, 1.04, 6.31). In contrast, compared
with consumer-generated videos, TV-based news videos were 6.67 times more likely to express anxiety or
fear of catching ZIKV (95% CI, 1.36, 32.70); 7.45 times more likely to highlight that the public was
afraid of ZIKV (95% CI, 1.20, 46.16); and 3.88 times more likely to discuss not becoming pregnant (95%
CI, 1.13, 13.25). Professional videos were much more likely to highlight ZIKV cases in the US (OR =
20.5, 95% CI, 1.82, 230.51), when compared with consumer-generated videos, but there was a very small
sample of professional videos (n=4), representing only 1.3% of total views.
Discussion
This cross-sectional study categorized information circulated by the 100 most popular ZIKV-related
YouTube videos in English, and their respective sources. Of the 100 videos, the four created by medical
professionals or government public health agencies captured only 1.3% of total views, while the 43
consumer-generated videos captured 22.4%. In total, these videos were viewed 8,894,505 times.
Compared with the consumer-generated videos, internet-based news videos (which captured over two-
thirds of total views) were more likely to mention ZIKV’s impact on babies, ZIKV cases in Latin America,
and Africa. In sharp contrast, television-based news videos, which captured 8.6% of total views, were
8
more likely to express anxiety or fear of catching ZIKV, to highlight that the public was afraid of ZIKV,
and to discuss avoiding pregnancy.
Social media poses both opportunities and challenges in the ZIKV response. Social media are popular
sources of health information related to pregnancy and children’s health.15, 16 The interactive nature of
social media and their high penetration in industrialized countries may allow for more effective
communication of health information than traditional media.17 In fact, social media interacts with
traditional media and amplifies its impact. Previous research found that Ebola-related videos released
online by news channels drove up Ebola-related Twitter traffic.11 Both social media and traditional media
can become part of health communication strategies deployed by public health agencies to achieve greater
effects. However, social media also has the potential to amplify unnecessary anxiety during critical time
periods of infectious disease outbreaks.18 Emerging reports of fetal microcephaly, a potential indicator of
impaired fetal brain development, in ZIKV-infected pregnant women,2 may cause anxiety and may lead to
unnecessary abortions among pregnant women with potential exposure to ZIKV. Our findings indicate
that some of the most widely viewed YouTube videos mention anxiety and fear associated with ‘catching’
ZIKV and include discussions about avoiding pregnancy due to the potential risk of ZIKV infection
during pregnancy. While they may be legitimate television news reports, the extended coverage on this
uncertain risk and exaggerated anxiety may lead to adverse effects on public health, because witnessing
others’ fear may actually induce one’s own anxiety and lead to misinformed pregnancy decisions,19 as
well as stress during pregnancy, which would be detrimental to the health of both pregnant women and
their fetus. While reading about others’ personal health experience on social media may enhance feelings
of identification, social support, and in some cases may improve health literacy about ZIKV,
distinguishing accurate information from misinformation or communications that evoke excessive fear
9
remains a challenge. Little is known about how these YouTube videos may affect users’ mental health or
health-related behaviors, and these topics warrant further research.
Policy implications. Engaging non-professional users is critical to developing ZIKV-related health
communication that is not only accurate but actually reaches its intended audience. A top-down and
“knowledge-deficient” model of health communications during crisis situations remains pervasive among
public health practitioners,20 wherein the general public is assumed to be “deficient” in understanding
essential health knowledge and making informed risk-related decisions, while health communicators
known to be sufficiently knowledgeable aim to fill the information “gap”. This view is challenged by
Hulme,21 who argued that risk communications must involve both non-experts and experts and there
should be two-way interactions. The development of social media further reinforces a discussion-based
environment in which a mixture of professional- and layman-led health information is present. Therefore,
health communicators should establish a better understanding of widely views viewed social media posts
and videos generated by consumers, and the rationale upon which it is based. This will help inform health
authorities responsible for developing a public engagement policy embracing bidirectional
communication.
Limitations. This is a cross-sectional study design and we do not have longitudinal data on how the
number of views of YouTube videos changes over time. We limited our study to English language videos.
Spanish and Portuguese videos will be an interesting subject for future studies. We did not have data on
the viewers. We did not evaluate the production or scientific quality of the videos. These analyses were
beyond the scope for this paper and will be fruitful directions for future research.
10
To conclude, we observed statistically significant differences in informational content between consumer-
generated ZIKV-related videos and videos from other sources. Public health agencies should consider
establishing a larger presence on YouTube to reach more people with evidence-based information about
ZIKV.
11
Funding
We received no external funding to conduct this research project.
Acknowledgement
ICHF and ZTHT received salary support from the Centers for Disease Control and Prevention
(15IPA1509134 and 16IPA1619505). This paper is not related to their CDC-funded projects. This paper
does not represent the official positions of the CDC or the United States Government.
Contributions
CHB and ICHF conceived and designed the study. CHB created the codebook. CHB ad RH manually
coded the YouTube videos. ICHF and EBB did the statistical analysis. CHB, ICHF, KWF, PI, ZTHT and
CEB wrote the first draft of the paper. All authors edited the manuscript with intellectual inputs. CHB and
ICHF are the guarantors of this paper.
12
References
1. Rasmussen SA, Jamieson DJ, Honein MA, Petersen LR. Zika Virus and Birth Defects -
Reviewing the Evidence for Causality. N Engl J Med. 2016.
2. European Centre for Disease Prevention and Control. Rapid risk assessment: Zika virus disease
epidemic: potential association with microcephaly and Guillain–Barré syndrome, sixth update (May 23,
2016). 2016 [June 21, 2016]; Available from:
http://ecdc.europa.eu/en/publications/Publications/zika%20virus%20rapid%20risk%20assessment%2010-
05-2016.pdf.
3. Fung IC, Duke CH, Finch KC, Snook KR, Tseng PL, Hernandez AC, et al. Ebola virus disease
and social media: A systematic review. American journal of infection control. 2016.
4. Glowacki EM, Lazard AJ, Wilcox GB, Mackert M, Bernhardt JM. Identifying the public's
concerns and the Centers for Disease Control and Prevention's reactions during a health crisis: An
analysis of a Zika live Twitter chat. American journal of infection control. 2016.
5. Fu KW, Liang H, Saroha N, Tse ZT, Ip P, Fung IC. How people react to Zika virus outbreaks on
Twitter? A computational content analysis. American journal of infection control. 2016.
6. Southwell BG, Dolina S, Jimenez-Magdaleno K, Squiers LB, Kelly BJ. Zika Virus-Related News
Coverage and Online Behavior, United States, Guatemala, and Brazil. Emerg Infect Dis. 2016; 22:1320-1.
7. Sharma M, Yadav K, Yadav N, Ferdinand KC. Zika virus pandemic-analysis of Facebook as a
social media health information platform. American journal of infection control. 2016.
8. Houts PS, Doak CC, Doak LG, Loscalzo MJ. The role of pictures in improving health
communication: a review of research on attention, comprehension, recall, and adherence. Patient Educ
Couns. 2006; 61:173-90.
9. Statista. Statistics and facts about YouTube. 2016 [June 21, 2016]; Available from:
http://www.statista.com/topics/2019/youtube/.
10. Basch CH, Basch CE, Ruggles KV, Hammond R. Coverage of the Ebola Virus Disease Epidemic
on YouTube. Disaster Med Public Health Prep. 2015; 9:531-5.
11. Towers S, Afzal S, Bernal G, Bliss N, Brown S, Espinoza B, et al. Mass Media and the Contagion
of Fear: The Case of Ebola in America. PLOS ONE. 2015; 10:e0129179.
12. Pathak R, Poudel DR, Karmacharya P, Pathak A, Aryal MR, Mahmood M, et al. YouTube as a
Source of Information on Ebola Virus Disease. N Am J Med Sci. 2015; 7:306-9.
13. Centers for Disease Control and Prevention. Zika Virus. 2016 [June 21, 2016]; Available from:
http://www.cdc.gov/zika/.
14. R Development Core Team. R: A language and environment for statistical computing. 2016.
15. Bernhardt JM, Felter EM. Online pediatric information seeking among mothers of young children:
results from a qualitative study using focus groups. Journal of medical Internet research. 2004; 6:e7.
16. Wong WH, Li SL, Fu K-W, Tinsley H, Chow C-B, Ip P. The Importance of Online Resources for
Parents of Children with Special Needs in Hong Kong: South China's Experience. Journal of Health &
Medical Informatics. 2015; 6:192.
17. Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C. A new dimension of health
care: systematic review of the uses, benefits, and limitations of social media for health communication.
Journal of medical Internet research. 2013; 15:e85.
18. Fung IC-H, Tse ZTH, Cheung C-N, Miu AS, Fu K-W. Ebola and the social media. The Lancet.
2014; 384:2207.
19. Bandura A, Walters RH. Social learning theory. 1977.
20. Ziman J. Not knowing, needing to know, and wanting to know. In: Lewenstein B, editor. When
science meets the public. Washington, DC: American Association for the Advancement of Science; 1992.
p. 13-20.
13
21. Hulme M. Why we disagree about climate change: understanding controversy, inaction and
opportunity. Cambridge, UK ; New York: Cambridge University Press; 2009.
Table 2. Frequency count of 100 Zika Virus-related videos in English by their sources and contents
Source category of videos
Content category†
Consumer
(N = 43)
n (%)
Internet-
based
news
(N = 38)
n (%)
Professional
(N = 4)
n (%)
TV-based
news
(N = 15)
n (%)
Total
(N = 100)
n (%)
Transmission: mentioned how Zika is
transmitted
No
9 (21)
4 (11)
0 (0)
0 (0)
13 (13)
Yes
34 (79)
34 (89)
4 (100)
15 (100)
87 (87)
Mosquito: mentioned that Zika is
transmitted by mosquitos
No
9 (21)
4 (11)
0 (0)
0 (0)
13 (13)
Yes
34 (79)
34 (89)
4 (100)
15 (100)
87 (87)
Sex: mentioned Zika transmission
through male sex partners
No
27 (63)
25 (66)
3 (75)
11 (73)
66 (66)
Yes
16 (37)
13 (34)
1 (25)
4 (27)
34 (34)
Transfusion: mentioned Zika
transmission through transfusion
14
No
41 (95)
37 (97)
4 (100)
15 (100)
97 (97)
Yes
2 (5)
1 (3)
0 (0)
0 (0)
3 (3)
Baby: mentioned Zika’s impact on
babies
No
15 (35)
3 (8)
0 (0)
0 (0)
18 (18)
Yes
28 (65)
35 (92)
4 (100)
15 (100)
82 (82)
Treatment: mentioned treatment on
Zika
No
43 (100)
37 (97)
4 (100)
15 (100)
99 (99)
Yes
0 (0)
1 (3)
0 (0)
0 (0)
1 (1)
Anxiety: mentioned anxiety/fear of
catching Zika
No
40 (93)
33 (87)
3 (75)
10 (67)
86 (86)
Yes
3 (7)
5 (13)
1 (25)
5 (33)
14 (14)
Prevention: mentioned (any)
prevention
No
29 (67)
31 (82)
0 (0)
6 (40)
66 (66)
Yes
14 (33)
7 (18)
4 (100)
9 (60)
34 (34)
Case: mentioned number of cases
No
38 (88)
30 (79)
3 (75)
10 (67)
81 (81)
Yes
5 (12)
8 (21)
1 (25)
5 (33)
19 (19)
Latin America: mentioned number of
cases in Latin America
No
14 (33)
3 (8)
0 (0)
0 (0)
17 (17)
15
Yes
29 (67)
35 (92)
4 (100)
15 (100)
83 (83)
US: mentioned number of cases in
the US
No
35 (81)
31 (82)
2 (50)
10 (67)
78 (78)
Yes
8 (19)
7 (18)
2 (50)
5 (33)
22 (22)
Public fear: highlighted that the
public is/was afraid
No
41 (95)
37 (97)
4 (100)
11 (73)
93 (93)
Yes
2 (5)
1 (3)
0 (0)
4 (27)
7 (7)
Not pregnant: discussed not
becoming pregnant
No
31 (72)
21 (55)
4 (100)
6 (40)
62 (62)
Yes
12 (28)
17 (45)
0 (0)
9 (60)
38 (38)
US cases: highlighted cases in the US
No
41 (95)
36 (95)
2 (50)
12 (80)
91 (91)
Yes
2 (5)
2 (5)
2 (50)
3 (20)
9 (9)
Olympics: discussed the 2016
Olympics in Brazil
No
38 (88)
30 (79)
4 (100)
13 (87)
85 (85)
Yes
5 (12)
8 (21)
0 (0)
2 (13)
15 (15)
Hoax: mentioned that people felt that
Zika is a hoax or there is no such
thing or cases are staged
No
40 (93)
33 (87)
4 (100)
15 (100)
92 (92)
16
Yes
3 (7)
5 (13)
0 (0)
0 (0)
8 (8)
Conspiracy: mentioned that Zika is
intentional, population control,
conspiracy theory, etc.
No
31 (72)
27 (71)
4 (100)
15 (100)
77 (77)
Yes
12 (28)
11 (29)
0 (0)
0 (0)
23 (23)
Comedy: part of a comedy
skit/parody
No
42 (98)
38 (100)
4 (100)
15 (100)
99 (99)
Yes
1 (2)
0 (0)
0 (0)
0 (0)
1 (1)
Prank: pranking somebody about
Zika
No
42 (98)
38 (100)
4 (100)
15 (100)
99 (99)
Yes
1 (2)
0 (0)
0 (0)
0 (0)
1 (1)
Africa: mentioned Zika in Africa
No
29 (67)
17 (45)
4 (100)
12 (80)
62 (62)
Yes
14 (33)
21 (55)
0 (0)
3 (20)
38 (38)
Asia: mentioned Zika in South-East
Asia
No
34 (79)
26 (68)
4 (100)
12 (80)
76 (76)
Yes
9 (21)
12 (32)
0 (0)
3 (20)
24 (24)
Pacific: mentioned Zika in Pacific
Islands
No
34 (79)
25 (66)
4 (100)
12 (80)
75 (75)
17
Yes
9 (21)
13 (34)
0 (0)
3 (20)
25 (25)
Content categories that have no
entries†
No
43 (100)
38 (100)
4 (100)
15 (100)
100 (100)
Yes
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
†Content categories that all entries were “no” (0): US not prepared, aid, medical help, preparedness,
cooperation, training, coordination, and danger.
18
Table 3. Odds ratios of categories of sources of English Language Zika Virus-related YouTube
videos as compared to consumer-generated videos, for each content category.
Content category*†
Odds ratio† (95%
CI)
p-value
Transmission: mentioned how Zika is transmitted
Internet
2.25 (0.63, 8.01)
0.21
Professional
†
†
TV news
†
†
Mosquito: mentioned that Zika is transmitted by
mosquitos
Internet
2.25 (0.63, 8.01)
0.21
Professional
†
†
TV news
†
†
Sex: mentioned Zika transmission through male
sex partners
Internet
0.88 (0.35, 2.18)
0.78
Professional
0.56 (0.05, 5.88)
0.63
TV news
0.61 (0.17, 2.25)
0.46
Transfusion: mentioned Zika transmission
through transfusion
Internet
0.54 (0.048, 6.36)
0.64
Professional
†
†
TV news
†
†
Other: mentioned Zika transmission through
19
other avenues
Internet
3.11 (0.57, 17.05)
0.192
Professional
†
†
TV news
†
†
Baby: mentioned Zika’s impact on babies
Internet
6.25 (1.64, 23.76)
0.0072
Professional
†
†
TV news
†
†
Anxiety: mentioned anxiety/fear of catching Zika
Internet
2.02 (0.45, 9.09)
0.36
Professional
4.44 (0.35, 56.88)
0.25
TV News
6.67 (1.36, 32.70)
0.02
Prevention: mentioned (any) prevention
Internet
0.47 (0.17, 1.32)
0.15
Professional
†
†
TV News
3.11 (0.92, 10.46)
0.07
Case: mentioned number of cases
Internet
2.03 (0.60, 6.83)
0.25
Professional
2.53 (0.22, 29.29)
0.46
TV News
3.8 (0.92, 15.75)
0.07
Latin America: mentioned number of cases in
Latin America
Internet
5.63 (1.47, 21.52)
0.01
Professional
†
†
20
TV News
†
†
US: mentioned number of cases in the US
Internet
0.99 (0.32, 3.04)
0.98
Professional
4.38 (0.53, 35.91)
0.17
TV News
2.19 (0.58, 8.19)
0.25
Public fear: highlighted that the public is/was
afraid
Internet
0.55 (0.05, 6.36)
0.64
Professional
†
†
TV News
7.45 (1.20, 46.16)
0.03
Not pregnant: discussed not becoming pregnant
Internet
2.09 (0.83, 5.27)
0.12
Professional
†
†
TV News
3.88 (1.13, 13.25)
0.03
US cases: highlighted cases in the US
Internet
1.14 (0.15, 8.50)
0.90
Professional
20.50 (1.82, 230.51)
0.01
TV News
5.12 (0.77, 34.31)
0.09
Olympics: discussed the 2016 Olympics in Brazil
Internet
2.03 (0.60, 6.83)
0.26
Professional
†
†
TV News
1.17 (0.20, 6.77)
0.86
Hoax: mentioned that people felt that Zika is a
hoax or there is no such thing or cases are staged
21
Internet
2.02 (0.45, 9.09)
0.36
Professional
†
†
TV News
†
†
Conspiracy: mentioned that Zika is intentional,
population control, conspiracy theory, etc.
Internet
1.05 (0.40, 2.77)
0.92
Professional
†
†
TV News
†
†
Africa: mentioned Zika in Africa
Internet
2.56 (1.04, 6.31)
0.04
Professional
†
†
TV News
0.52 (0.13, 2.14)
0.36
Asia: mentioned Zika in South-East Asia
Internet
1.74 (0.64, 4.76)
0.28
Professional
†
†
TV News
9.44 (0.22, 4.08)
0.94
Pacific: mentioned Zika in Pacific Islands
Internet
1.96 (0.73, 5.31)
0.18
Professional
†
†
TV News
0.94 (0.22,4.08)
0.94
* The category of “consumer” videos was used as the reference category for the other three categories of
sources of YouTube videos (“internet-based news”, “professional” and “TV news”) † If all videos belong
to a particular category of source of video, then we cannot calculate the odds ratio and the standard error
will not be meaningful. We omitted the content category “Treatment”, as there was only one internet-
22
based news video that mentioned treatment, and no meaningful odds ratio could be calculated. Likewise,
we omit the content categories “comedy” and “prank” as there was only one consumer video that
contained such contents.
Table 1. Length and Number of Views of 100 Popular English Language Zika Virus-related Videos Posted on YouTube
Video Length (in minutes)
Number of Views
n
Mean
[SE]
Median
Range
95% CI
Mean
[SE]
Median
Range
95% CI
Total (%)
Consumer
43
7.623
[1.058]
5.817
0.15 –
37.25
5.487 –
9.758
46,311
[7,453]
26,260
11,910 –
216,700
31,269 –
61,352
1,991,358
(22.4)
Internet-
based news
38
6.767
[1.095]
4.833
0.867 –
28.12
4.549 –
8.986
158,500
[39,886]
51,680
12,300 –
1,224,000
77,684 –
239,317
6,023,012
(67.7)
Professional
4
3.267
[0.378]
3.358
2.4 – 3.95
2.062 –
4.471
28,821
[10,483]
22,200
11,600 –
59,290
-4,540 –
62,182
115,284
(1.3)
TV-based
news
15
3.190
[0.660]
2.267
1.4 –
10.32
1.774 –
4.606
50,990
[16,344]
24,120
12,230 –
264,400
15,936 –
86,044
764,851
(8.6)
Overall
100
6.459
[0.641]
4.583
0.15 –
37.25
5.187 –
7.730
88,945
[16,492]
31,120
11,600 –
1,224,000
56,222 –
121,668
8,894,505
(100.0)
CI, confidence interval; SE, standard error.
