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1112 social media and public health
1. Social media and public health
St. Francis Xavier University, Antagonish, Nova Scotia, Canada
Presenter: Melodie Yunju Song, RN, MSc, PhD
Health Systems Impact Fellow, Public Health Ontario
2. 1. Think about health behavior and information diffusion from a
networked lens
2. Learn ways in which social media act as platforms for information
diffusion, belief diffusion, and behavioral diffusion
3. Describe the mechanisms of misinformation diffusion on social
media
4. Give examples of monitoring and surveying vaccine hesitancy
using YouTube
5. Future applications of social media for health promotion
Objectives:
3. 3
What can networks tell us?
“We use network science to discover how the whole comes to be greater
than sum of its parts. The study of networks enriches our understanding
of phenomena such as riots, violence, and social and biological epidemics
— enabling us to develop more effective interventions.” – Nicholas
Christakis
4. Gelad Lotan and Christakis
•Why is vaccine misinformation so prevalent online?
(Diresta and Lotan, 2015)
@MelodieYJSong
7. Friendships are networks
McCabe, Janice M. “How Your College Friendships Help You – or Don’t.” The Conversation, 2016.
http://theconversation.com/how-your-college-friendships-help-you-or-dont-68413.
8. Different network structures influence the
distance, speed, and depth of information spread
Case, Nicky. “The Wisdom and/or Madness of Crowds,” 2018.
http://ncase.me/crowds/.
9. Network depicting romantic and sexual
relations in a high school
Bearman, Peter S., James Moody, and Katherine
Stovel. “Chains of Affection: The Structure of
Adolescent Romantic and Sexual Networks.”
American Journal of Sociology 110, no. 1 (July 2004):
44–91. https://doi.org/10.1086/386272.
10. Networks depicting the spread of obesity
Christakis, Nicholas A., and James H. Fowler. “The Spread
of Obesity in a Large Social Network over 32 Years.” New
England Journal of Medicine 357, no. 4 (July 26, 2007):
370–79. https://doi.org/10.1056/NEJMsa066082.
11. Group discussion:
Are baseball caps fashionable?
Each circle is colored to indicate that person’s stance on
the issue. Blue circles think they are
fashionable. Orange circles think they are not. (On this
issue, everyone has an opinion.)
A polling firm recently asked whether each person
thought baseball caps would get a majority of
support.
Question: would the poll results show that the
majority consider baseball caps fashionable?
Lerman, Kristina, Xiaoran Yan, and Xin-Zeng Wu. “The ‘Majority
Illusion’ in Social Networks.” Edited by Frederic Amblard. PLOS
ONE 11, no. 2 (February 17, 2016): e0147617.
https://doi.org/10.1371/journal.pone.0147617.
14. Health misinformation & visuals
- the perfect storm
•Hybrid Media System (Chadwick, 2013) – hyperconnected,
dysfunctional, “Politics (and decision making) is increasingly defined
by organizations, groups, and individuals who are best able to blend
older and newer media logics”.
•The future is visual (Flusser, 1985)
@MelodieYJSong
15. Social media can be bad for health (1):
Health misinformation on Instagram and Pinterest
•Misinformation: false information
that is spread, regardless of
whether there is intention to
mislead.
•Homophily effect (McPherson,
2001): Online, we connect with
people with similar beliefs, e.g.,
age, gender, experiences, hobbies,
and even BMI (Centola & van de
Rijt, 2015).
16. Social media is bad for health (2): AIDS denialists
on VKontakte
•Visualization of 'friendships'
between online community
members: nodes indicate users,
edges indicate 'friendships'. Node
size is proportional to user activity
in the community. Colors: red -
convinced denialists, yellow -
doubters, blue - orthodox (i.e.
supporters of medical science on
the issue), grey - undetermined.
Rykov, Y. G., Meylakhs, P. A., & Sinyavskaya, Y. E. (2017). Network Structure of an
AIDS-Denialist Online Community: Identifying Core Members and the Risk
Group. American Behavioral Scientist, 61(7), 688-706.
17. Social media is bad for health (3):
•Corresponding to Twitter and
Instagram hashtags and keywords
related to “13 Reasons Why”, a
Austrian study shows that the
number of suicides increased by
15% between 10-19 year-olds in
the US following Netflix release of
the show.
Niederkrotenthaler, Thomas, Steven Stack, Benedikt Till, Mark Sinyor, Jane Pirkis, David
Garcia, Ian R. H. Rockett, and Ulrich S. Tran. “Association of Increased Youth Suicides in
the United States With the Release of 13 Reasons Why.” JAMA Psychiatry 76, no. 9
(September 1, 2019): 933–40. https://doi.org/10.1001/jamapsychiatry.2019.0922.
18. Why do I keep seeing anti-vaccine videos?
YouTube
18
?
Vaccine
hesitancy
Vaccine
information-seekers
Algorithmic accountability: Human influences are
embedded into algorithms (training data, semantics, and
interpretation) - including institutional processes +
intent. (Diakopoulos, 2014)
19. Vaccine videos on YouTube in 2007
(Keelan et al, 2007)
@MelodieYJSong
20. Information sources on the web
•https://www.youtube.com/watch?v=K-4LkJfEBDw
•Getman example
(Getman et al, 2017)
@MelodieYJSong
22. 1. What sentiment (e.g., pro- or anti-vaccine) is most prevalent among
videos recommended by YouTube?
2. Are pro- or anti-vaccine videos more central in the recommender
network?
3. Are there any pronounced differences in vaccine sentiment in relation to
video attributes (i.e., video category, dislike/like count, view count)?
Research Questions
@MelodieYJSong @gruzd 22
23. Data Collection
• Software: Netvizz for collecting YouTube related videos (Reinhardt, 2015)
• Search terms: vaccine, immunization, and other vaccine-related keywords for
5 iterations (N= 9489 videos, including video attributes: view count, comment
count, dislike/like ratio, video categories).
• Inclusion criteria: (1) Titles containing vaccination-related terms, (2) pro- and
anti-vaccine videos in English (97.9% of all videos). (N=1984)
@MelodieYJSong @gruzd 23
24. Data Analysis
@MelodieYJSong @gruzd
Data analysis
❖ Sentiment analysis/ visual content analysis: N=1984
❖ Social network analysis: Gephi and UCINet 6
Statistical analysis
❖ T-test to compare the means of node-level anti-vaccine and
pro-vaccine video’s centrality measures on UCINet.
❖ Logistic regression for video properties and vaccine sentiment.
24
25. 65% of vaccine-related videos are
anti-vaccine (N=1984)
Results:
1. What sentiment (e.g., pro- or anti-vaccine) is most prevalent
among videos recommended by YouTube?
@MelodieYJSong @gruzd
25
27. Results:
2. Are pro- or anti-vaccine videos more central in the recommender network?
• Anti-vaccine videos are easier to reach than pro-vaccine videos (esp. if you started
with one).
Centrality
measures
Mean of
Anti-vaccine
related videos
Mean of
Pro-vaccine
related videos
Difference in
means
Significance
Out-degree 0.004 0.004 0.000 0.0009**
In-degree 0.003 0.003 0.000 0.9735
Out-closeness 0.240 0.232 0.008 0.0001***
In-closeness 0.183 0.179 0.004 0.0096**
Betweenness 0.001 0.002 -0.001 0.0021**
28. Videos with higher
dislike/like ratio have
3.912 higher odds of
being pro-vaccine.
@MelodieYJSong @gruzd
Results:
3. What are the differences in vaccine sentiment in relation to video attributes?
28
Sentiment
Dislike/like ratio (OR 3.912)**
Dislike count (OR 0.996)**
Like count (OR 1.000)**
View count (OR 1.000)*
Comment count (OR 0.338)
R = 0.09
30. “YouTube, the great radicalizer” (Tufekci, 2018)
•Beyond algorithmic biases, we are increasingly hostile and sarcastic to
people who are anti-vaccine, creating distinct languages of in-group
and out-groups.
•There is an abundance of anti-vaccine influencers (chiropractors,
naturopaths, osteopaths, talk show hosts, and scientists) who are
influential
•There is a lack of incentive for traditional gatekeepers to use YouTube
to communicate with the public
@MelodieYJSong
31. Potentials of social media
to counter misinformation
• “Epidemic prevalence information on
social networks can mediate emergent
collective outcomes in voluntary vaccine
schemes” (Sharma et al, 2019)
• Creating safe spaces with access to
anonymous peers for discussing stigma
and taboo health issues.
• Remove hostility and incivility from
online interactions.
• Create a tipping point (25%) of
consensus through network majority
illusion (Centola, 2018).
32. Reference:
• McKeever, Brooke Weberling, Robert McKeever, Avery E. Holton, and Jo-Yun Li. “Silent Majority:
Childhood Vaccinations and Antecedents to Communicative Action.” Mass Communication and
Society 19, no. 4 (July 3, 2016): 476–98. https://doi.org/10.1080/15205436.2016.1148172.
• Lerman, Kristina, Xiaoran Yan, and Xin-Zeng Wu. “The ‘Majority Illusion’ in Social Networks.” Edited
by Frederic Amblard. PLOS ONE 11, no. 2 (February 17, 2016): e0147617.
https://doi.org/10.1371/journal.pone.0147617.
• https://www.independent.co.uk/news/science/majority-illusion-a-quick-puzzle-to-tell-whether-you
-know-what-people-are-thinking-a6689636.html (reading 1 for Mark’s class)
• https://www.annualreviews.org/doi/abs/10.1146/annurev-publhealth-031816-044528
• Olteanu, Alexandra, Carlos Castillo, Fernando Diaz, and Emre Kıcıman. “Social Data: Biases,
Methodological Pitfalls, and Ethical Boundaries.” Frontiers in Big Data 2 (2019).
https://doi.org/10.3389/fdata.2019.00013.(reading for Deena’s class)
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4964231/
• www.washingtonpost.com/education/2018/12/10/word-year-misinformation-heres-why/
33. Group discussion
1. Discuss the types of diet advice/trends that your friends tell you
about. Do you follow your friends’ dieting advice?
2. Do you search for dieting advices online? Where from? (e.g., social
media? Websites?)
3. How do you make informed decisions about dieting?
4. Thinking like a network scientist, if you want to reach the largest
crowd of 18-24 year-old women, how would you design a
“marketing scheme” to promote your diet advice on social media?
