The threats of connectivity

HEALTH MISINFORMATION ON
SOCIAL MEDIA
Melodie Yunju Song (@MelodieYJSong)
Health Systems Impact Fellow, Public Health Ontario

SOCIAL MEDIA CAN BE GOOD FOR HEALTH
• Health information-seeking is low-cost and highly efficient
• Stigmatized and marginalized populations find anonymous support:
• E.g., Patients with rare diseases could connect with others who share similar disabilities
to pool resources and knowledge for care
• Patients with HIV/AIDS, mental illness, and populations in rural areas
• Surveillance of disease outbreaks using ML techniques with social media data
(completely free and real-time).

THE OBESITY NETWORK
Christakis and Fowler. “The Spread of Obesity in a Large
Social Network over 32 Years.” New England Journal of
Medicine, 2007.
5

• Think about health behavior and information diffusion from a networked lens
• Learn ways in which social media act as platforms for information diffusion,
belief diffusion, and behavioral diffusion
• Describe the mechanisms of misinformation diffusion on social media
• Understand the proliferation of vaccine hesitancy on social media
• Future applications of social media for health promotion
OBJECTIVES:
6

MODELS OF DIFFUSION IN PUBLIC HEALTH
• 1. Macro model: the two parameter model (Bass, 1969)
• Application: Medical innovation (Coleman, Katz, & Menzel, 1969)
• A reanalysis of the model showed that social contagion effects disappeared once
marketing efforts were controlled.
• Benefits: It measures the rate of idea/contagion spread from the source.
• Drawbacks: Does not account for context, assumes perfect social mixing (everyone
knows everyone).

• 2. Spatial autocorrelation: (Moran, 1956)
• Application: Identifying demographic characteristics susceptible to HIV infection in
St. Petersburg (Heimer et al, 2009)
• Benefits: Measures network autocorrelation, the correlation of a single variable
between pairs of neighbouring observations.
• Drawbacks: Does not show whether specific individuals were more likely to adopt
based on their network position.

• 3. Network models:
• Sample application: Estimating diffusion of obesity in a sociocentric network
(Christakis & Fowler, 2007).
• Benefits: Measures social influence and social contagion.
• Drawbacks: model inconsistency, homophily and environmental confounding,
statistical dependencies in the network (Lyons, 2011)

SOCIAL NETWORK INFLUENCE WEIGHTINGS
Relational Positional Central
Direct ties Percent positive matches (tie
overlap)
Degree
Indirect ties Euclidean distance Betweenness
Joint participation in groups or
events
Regular equivalence Closeness
Flow
Integration
Information
Eigenvector/ Bonacich (power)

SOCIAL NETWORK INFLUENCE
WEIGHTINGS
overlap)
Degree
events
Flow
Integration
Information
Power
overlap)
Degree
events
Flow
Integration
Information
Eigenvector/ Bonacich (power)

POPQUIZ: WHICH BLACK NODE IS
MORE POWERFUL?
(Neal, 2011)

WOULD WATCHING A NETFLIX SERIES
CHANGE YOUR VIEW ON HEALTH
MYTHS AND PRACTICES?
What variables are at play
when we measure network
influence?

1. Was the show recommended by weak ties or
strong ties

strong ties
2. Knowledge, perception, and behaviors of strong
ties

strong ties
ties
3. Consider the complementarity, legitimacy,
credibility required for social reinforcement

strong ties
ties
4. The frequency at which interactions occur

strong ties
ties
5. Activities associated with the practice of a health
myth.

strong ties
ties
5. Activities associated with the practice of a health
myth.
6. Is the idea simple? If multiple contacts needed for
adoption, weak ties may inhibit diffusion (Centola,
How Behaviors Spread, 2018)

WHAT IS THE NETWORK STRUCTURE
OF SOCIAL MEDIA?
• Scale-free
• core-periphery
• Small-worlds
• A single cohesive clique

DIFFERENT NETWORK STRUCTURES
INFLUENCES THE DISTANCE, SPEED, AND
DEPTH OF INFORMATION SPREAD
Case, Nicky. “The Wisdom and/or Madness of Crowds,” 2018.
http://ncase.me/crowds/.

NOT ALL INFORMATION ARE DISSEMINATED
EQUALLY
(Vosoughi et al, Nature, 2018)
In a study conducted using historical Twitter data from 2006-2017, of the 126,000 tweets shared
by 3 million people, those that evoke surprise and disgust were shared further, wider, and deeper
than messages that evoked emotions of sadness and joy.

LIBRARY OF COMPLEX NETWORK
https://icon.colorado.edu

FALSE INFORMATION COME IN MANY FORMS
(Kumar, Srijan, and Shah, 2018)

THE MAJORITY ILLUSION
Are baseball caps fashionable?
Each circle is colored to indicate that person’s
stance on the issue. Orange circles think they are
not fashionable. Blue circles think they are
fashionable. (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, Yan, and Wu. “The ‘Majority Illusion’ in Social
Networks.” PLOS ONE. 2016.
https://doi.org/10.1371/journal.pone.0147617.

THE MAJORITY ILLUSION
(Lerman et al, 2016)

SOCIAL MEDIA REMOVES GATEKEEPERS

AIDS DENIALISTS ON VKONTALTE
• 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, Meylakhs, Sinyavskaya, American
Behavioral Scientist, 2017)

GELAD LOTAN AND CHRISTAKIS
• Why is vaccine misinformation so prevalent online?
(Diresta and Lotan, 2015)
30
(Diresta and Lotan, 2015, Wired.com)
TWITTER NETWORK OF VACCINE
CONVERSATIONS BY USER SENTIMENT

(Getman et al, 2017)
31
(Getman et al, Health
Education and Behavior,
2017)
500 websites of
popular publishers
by hyperlink degree
centrality

(Kang et al, Vaccine, 2017)32
Semantic network analysis of
26,389 tweets in the US.
High closeness keywords:
provide cohesiveness to the
narrative.
High degree keywords (node
size): most central ideas in the
narrative
High betweenness keywords:
bridges ideas from one cluster to
another (coincides with high
closeness keywords.

THEORIES EXPLAINING VACCINE HESITANCY
Discipline Authors/ Year Theory Context
Health
communicati
on
(Reyna, 2012) Fuzzy trace theory Two types of memory retention exist: verbatim
memory (detailed and informational), and gist
memory (basic meaning), gist memory is used for
decision-making
Health
informatics
(Dunn et al.,
2015)
Exposure to vaccine
critical messages
influence vaccine
belief
Twitter users were traced longitudinally as to
whether exposure to Tweets expressing negative
sentiment towards HPV vaccine lead to vaccine
hesitancy
Public Health,
Biology,
environmenta
l studies,
global health
(McNutt et al.,
2016)
Statistical analysis of
the correlation of
affluence based on
school tuition with
rate of vaccine
exemption
California’s private kindergartens have higher
exemption rates than public kindergartens, the
correlation of affluence, and religiousness.

History (Ward, 2016) Resource mobilization and
social movements
2009 pandemic swine flu vaccine hesitancy
in France
Medical
Anthropology
(Dube et al., 2016) Vaccine hesitancy is
characterized by diverse
historic, political and socio-
cultural factors
A panel of 40 experts during a pan-
Canadian workshop discussed the possible
factors of vaccine hesitancy
(Kata, 2012) Postmodern discourse Anti-vaccine websites have varying
interpretations of vaccines that are
embedded in the postmodernism
discourse
(H. J. Larson et al.,
2015)
Interventions to increase
confidence in vaccines can
increase likelihood of vaccine
uptake.
A GRADE systematic review of literature on
the use of “social mobilization, mass media,
communication tool-based training for
health workers, non-financial incentive and
reminder/recall interventions” was
conducted

Medicine (Salmon et al.,
2015)
Multitemporal factors such as
adverse events, unfamiliarity with
vaccine-preventable diseases, lack of
trust in corporations and public
health agencies contribute to
vaccine hesitancy
vaccine hesitancy is detrimental because
it leads to vaccine refusal, schedule
delays and correlates with vaccine
preventable disease outbreaks
Biology (Salathe &
Kennedy, 2013)
Social contagion model Online networks may spread vaccine
sentiments
Geography (Tomeny, Vargo, &
El-Toukhy, 2017)
Geographic and demographic
variances
Geographical and demographic data on
social media may help identify areas
where public health might need more
efforts on vaccine awareness in the US.
Philosophy
and media
studies
(Goldenberg &
McCron, 2017)
Media framing of scientific studies
may further divide the public
different vaccine beliefs
A 2014 study in Pediatrics (Nyhan et al,
2014) was ill-framed as no intervention is
effective in changing anti-vaxxer’s minds.

Literature (Faubert, 2017) The Werther effect (the
dangerous contagion of
sympathy)
18th century Britain suffered from the “Werther effect”
whereupon readers of a book titled “Werther” which
talked about suicide would become suicidal.
Social
sciences (e.g.
political
science,
economics,
sociology)
(Betsch &
Sachse, 2012)
High perceived risk of
vaccination can lead to
strong risk-negation
Willingness to get vaccinated is known to be related
to the perceived risk of vaccine adverse events (VAE)
(Lakoff, 2015) Cultural and political
processes drive upsurge in
vaccine resistance under
the concept of
“modernization of risk”
The US measles outbreak is correlated with high-
income areas that have no relation to climate-change
deniers or conspiracy theorists
(Nyhan, Reifler,
Richey, & Freed,
2014)
The Backfire effect Randomized control trials assigned 1 of 4
interventions to parents
(Yaqub, Castle-
Clarke, Sevdalis,
& Chataway,
2014)
Institutional mistrust Review of 38 paper on attitudes towards vaccines in
the English language, 6 market research datasets
(e.g., surveys and consultations) on general
practitioners, health professionals, and the public

Educational
psychology
Rabinowitz et al, 2016 Political inclination is
correlated to ideologies tied to
vaccination
Political ideologies as a driver
of beliefs have not been
explored in the context of
vaccination in the US
Information
science
Huesch et al, 2013 Social Network theory Online blogs and social
network sites allow for
astroturfing and the
proliferation of anti-HPV
vaccine messages through
networks
Langley et al, 2016 Network theory applied with
Health Belief Model (HBM)
Online social media are quickly
becoming the main portal
where people share health
information and influence each
other’s beliefs and attitudes

Computer science Yom-Tov and
Fernandez-
Luque, 2014
Selective
informational
acquisition
Internet search engines enables selectivity based on
pre-existing beliefs
Communications McKeever et al,
2016
Communicative
Actions theory and
theory of the silent
majority
Communications does not happen in a vacuum of
harmony, where there’s a perceived problem that
needs to be solved, communicative action will be
mobilized
Ruiz and Bell,
2014
Search strategies
inform search
results
The perpetuation of online anti-vaccine myths in
websites may lead to opting-out of vaccination

Sociology Granovette
r, 1973;
Bakshy et
al, 2012
The
strength of
weak ties
Opposed to the notion that strong ties are how we receive
information; the social media environments demonstrates
that the diffusion of novel information relies mainly on the
abundance of weak ties; the more one is exposed to
information, the more they are likely to share that
information regardless of the “influence” of the original
promulgator of the information.
Giglietto,
2016
Hybrid
news
system and
“fake news”
Removing oneself from an actor-oriented inquiry, the
hybrid news system brings forth theoretical insights on the
processes of misinformation. “The process of misleading
information emerges as a chain of propagations resulting
by the operations of diverse actors acting according to
their individual agenda and understanding of the
informational content they are sharing”.
THEORIES EXPLAINING MISINFORMATION SPREAD

Discipline Author/yea
r
Theory Phenomena of exploration
Sociology Smith and
Christakis,
2008
Social
network
contagion
Social networks have been posited to influence heath
through social support, social influence, access to
resources, social involvement, and person-to-person
contagion.
Monsted et
al, 2017
Complex
contagion
Information diffusion in a techno-social system is proven
to be conducted through complex contagion through
social media platforms like Twitter.
Political
communic
ation
Chadwick,
2017
Hybrid
Media
system
Case studies of polarity in the 2016 presidential election
show that digital media are forms of competition and
organization, through which a non-linearity of power and
systems are created through competition and conflict. In
particular, social media metrics acts as proxies for public
interest that can create incremental changes in opinions,
values, and offline behavior.

Disci
plin
e
Author/year Theory Phenomena of exploration
Infor
mati
on
scien
ce
McPherson et
al,
Homophily The idea that “birds of a feather flock together”, treating the online
ecosystem as a public sphere, online users gravitate towards the
familiar (e.g., friends and family), and rely on their own belief systems
to navigate the online world for a sense of belonging.
Dubois and
Gaffney, 2014
Katz and
Lazarsfeld’s
Two-step flow
hypothesis
Opinion leaders are able to influence personal ties by social pressure or
social support. In the Twitter community, political leaders are influential
but the online platforms allows for other influences such as bloggers
and commentators to be equally influential in the online sphere.
Neumann et al,
1974; Hampton
et al, 2014
Neumann’s
Spiral of silence
People are less willing to discuss a controversial issue when they are on
social media if they "perceive" that the common census is that they are
not going to be agreed upon; this leads to a spiraling effect of being
mute on the issue.
Lerman, 2016 Majority illusion The theory that in an online environment, those who are more vocal in
a group are more visible than those who are silent; given that the vocal
participants continue to be vocal, information being put out by these
active nodes are then perceived by the silent nodes as being dominant;
this creates the illusion that a few active people’s opinions are the
majority opinion.
Fisher et al, Echo chambers are facilitated by social media’s platform which allows

Discipline Author
/year
Humaniti
es (Media
policy and
digital
democrac
y)
Dahlber
g, 2007
Discursiv
e
contestati
on
An exploration of the polarized public can be explained not by finding
convergence but an acceptance that disagreements is why public spheres
exist, especially in the online space. Consensus is “partially a result of
hegemony, a stabilization of meaning aided by cultural domination and
exclusion”, as such, the safer space for expression of differences will
continue to occur online.
Journalis
m
Marwic
k and
Lewis,
2017
Media
manipula
tion
Using the far-right proliferation online and the 2016 US presidential election
results; the authors propose that the mainstream media’s “predilection for
sensationalism, need for constant novelty, and emphasis on profits over civic
responsibility’ created a vulnerable environment for media manipulation. As
seen in social media sites, the subcultures in the “far-right”, which exhibits
levels of extremism, racism, nationalism, etc, have found a nesting place and
portal to proliferate their messages through the gratification of the new-
found sense of collective purpose.
Marketin
g
Dobele
et al,
2007
Virality The idea that messages that connect emotionally will be shared with family
and friends and spread like a virus, regardless of the correctness of the
information being shared.

Discipline Author/y
ear
Engineeri
ng
Gillespie,
2010;
2017
Existing
logics of
platforms
Platforms exists as a intermediary of content, which is premised on the
economics of popularity, thus, logistics of operation rely heavily on the
moderation of content in each platform with the intent to retain users;
which delineates from the course of an open web.
Engineeri
ng and
sociology
Friedkin
et al,
2016
Networks
on influence
under logic
constraints
When group decisions need to be made on an issue that is far from one’s
understanding, individuals in groups need to operate under logical
constraints and be able to reach consensus. Networks influence the way
individuals in groups reach a decision: the dynamic of decision-making
usually is constructed through active ‘nodes’ (the more vocal individuals
advocating their own beliefs), creating a false impression that such beliefs
are held by many.
Psycholo
gy
Packer,
2009
Normative
conflict
model
Strongly identified members are attentive to group problems, such that
when weaker members of the group express concerns on an issue, the
strongly identified members are willing to “bear the social costs associate
with dissent” to improve group outcomes.
Psycholo
gy and
sociology
Lewando
wsky et
al, 2012
Salience of
Misinformat
ion
The salience of misinformation is based on 4 issues: the continued influence
effect, the familiarity backfire effect, and overkill backfire effect, and the
worldview backfire effect. As long as the piece of misinformation is being
registered as “partially true”, or that it reckoned by the receiver that it is
plausible, misinformation becomes salient and challenging it creates
backfire effects.

CASE ONE:
POST-TRUTH ERA REALITY CHECK - CHILDHOOD
VACCINE-RELATED TWEETS IN ONTARIO, 2013-2016
• Do Ontarians still look to doctors and health professionals for advice?
• Is Twitter a good place to promote vaccine awareness?
• What are the advantages for public health agencies in Ontario on Twitter?

DATA COLLECTION
Search parameter
selection
• Vaccine type filter
• Time filter
• Geographical filter
Social Media data
collection
• Query manually
collected directly from
Twitter (875 tweets)
Sentiment analysis
and content
analysis

CODED TWEET ATTRIBUTES
• Occupation/affiliation
• Sentiment towards vaccines
• Emotion
• Information-seeking or information-sharing
• Type of interaction: retweets, mentions, replies, none
• Attached medium in tweet: image, text, video, multimedia
• Sources of information referred in the tweet: news, blogs, government websites,
other social media platforms

DESCRIPTIVE RESULTS• Occupation/affiliation:
• 24% of the people who ever tweeted about vaccines are in a health-related profession,
health-related government institutions and public health units.
• 10% are news.
• 10% (n=73) of Twitter accounts are set up specifically for anti-vaccine purposes, these are
not bots. (3 nut bars)
• Six health-related Twitter accounts expressed negative vaccine sentiment, these accounts
are held by chiropractors, naturopaths, health and nutrition consultants, and practitioners of
holistic medicine
• Sentiment:
• 58% are pro-vaccine,
• 28% Twitter accounts are anti-vaccine,
• 16% are neutral.
• Emotionality: 80% of the tweets exhibit neutral emotion.
• Information sharing:
• 58% of all tweets are information-sharing
• 5% are information-seeking

• Vaccine sentiment is associated with different behavioral attributes:
• Pro-vaccine sentiment is associated with more use of text-based links, more Retweets of news and
government information;
• Neutral sentiment Tweets are composed of news articles, information-seeking, and multimedia links;
• Negative vaccine sentiment expresses more negative emotions, and tend to share more image and video-
based links, alternative news sources with misinformation, and “call for action” Tweets.
• News is the most widely shared type of link, however, the types of news and sources vary depending
on the kind of vaccination sentiment of the Twitter user:
• Pro-vaccine Tweets contain news from mainstream or local news media that supports immunization;
• Neutral vaccine news that presents a ‘balanced’ view of vaccines were Tweeted the most by everyone,
including people who want to seek information from Twitter;
• Alternative news sources and alternative health news were exclusively Tweeted by those who are anti-
vaccine.
• Types of medium:
• anti-vaxxers are more likely to retweet videos,
• general public are more likely to retweet text-based links,
• physicians are more likely to tweet opinions and conference keynotes,
• public health units are most likely to tweet vaccination schedules.
VACCINE SENTIMENT AND BEHAVIORAL /
LINK-SHARE ATTRIBUTES IN ONTARIO

Visualization of vaccine-
related tweets in
Ontario, 2013-2016
“Jenny McCarthy’s anti-vaccine
views = misinformation. Please
ask The View to change their mind,“
July 22, 2013
“Reality Check: CDC
Scientist Admits
Data of Vaccines
and Autism Was
Trashed”, Mar 4,
2016
Ottawa’s vaccine-
free daycare, Feb 7,
2015

WHAT ARE ANTI-VACCINE TWEETS USING TO
SUPPORT THEIR SENTIMENT?
0
10
20
30
40
50
60
70
80
90
Alternative Treatment Trust Adverse events Other concerns Alternative evidence

DIFFERENT WAYS OF
COMMUNICATION…
Anti-vax messages Neutral messages Pro-vax messages

GOING BACK TO OUR QUESTIONS …
• Do Ontarians still look to doctors for advice? Yes, but...
• Is Twitter a good place for providing text-based vaccine information? Yes, but
responses should be immediate, find bridges, create interesting messages using
memes and images
• How can we create attention on Twitter for vaccine awareness, specifically? Around
a media sensation.

FOR FUTURE CONSIDERATION
• Research:
(1) Establish research capacities in public health to collect, code, and interpret how
content is being shared on various social media platforms.
(2) Evaluate the effectiveness of digital methods by creating different types of
content (e.g., dynamic vs. static) and measure informational spread
• Practice:
(1) Promote the use of dynamic content (images, gifs, and videos)
(2) Monitor how (in person? distal? One-on-one? In groups?) , where, and how
frequent do promulgators of disinformation connect with their communities
(3) Document convincing case studies across the Province to inform social media
practice to support new Public Health Standards?

“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
59

CASE TWO: YOUTUBE’S VACCINE SENTIMENT
NETWORK

WHY DO I KEEP SEEING ANTI-VACCINE VIDEOS?
YouTube
61
?
Vaccine
hesitancy
Vaccine information-
seekers
Algorithmic accountability: Human influences are
embedded into algorithms (training data, semantics, and
interpretation) - including institutional processes +
intent (Diakopoulos, 2014)

1. 1. What sentiment (e.g., pro- or anti-vaccine) is most prevalent
among videos recommended by YouTube?
2. 2. Are pro- or anti-vaccine videos more central in the
recommender network?
3. 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
62

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)
63

DATA ANALYSIS
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.
64

(Song and Gruzd, 2017)
YouTube vaccine network ( - 2016)

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?
66

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**
67

Videos with higher
dislike/like ratio have
3.912 higher odds of
being pro-vaccine.
Results:
3. What are the differences in vaccine sentiment in relation to video attributes?
68
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

VACCINE VIDEOS ON YOUTUBE IN 2007
(Keelan et al, JAMA, 2007)
@MelodieYJSong 69

INFORMATION SOURCES ON THE WEB
• https://www.youtube.com/watch?v=K-4LkJfEBDw
• Getman example
(Getman et al, Health Education,
2017)
@MelodieYJSong 70

(Abul-Fottouh, Song, and Gruzd, 2019)
YouTube vaccine network ( - 2019)

HOW CAN WE DEPOLARIZE SOCIAL
MEDIA?
• Examine the network structure of misinformation flow
• Experimental designs of network interventions
• A focused research question
• A strong theory + framework
• A good study design
• Make corrections to recommender systems’ collaborative filtering
• Put a pulse on misinformation typology and sentiment using ready-made tools:
• E.g., Crowdbreaks.org
• E.g., Mediacloud.org

PROPOSED NETWORK INTERVENTIONS

SOLUTIONS TO ADDRESS
MISINFORMATION
• Semantic dissonance detection
• Fact-checking from knowledge bases
• Fact-checking using crowd-sourcing
• Multimedia false information detection
• Bridging echo-chambers
• Adversarial creation to false information:
• Mitigation of false information

STICKY BELIEFS IN THE VACCINE RESEARCH
COMMUNITY
• A 4-armed RCT observed a “backfire effect” upon 4 types of information provision
(i.e., disease risk, autism correction, narrative danger, disease image). Anti-vaccine
parents become more hesitant (n= 678) (Nyhan et al. Pediatrics, 2014). Subsequent
studies confirmed that factual information provision does not reduce anti-vaccine
parents’ views (Nyhan & Reifler, Vaccine, 2014; Pluviano, Watt, and Sala, PLoS ONE,
2017)
• Conversely, a 3-arm randomized control trial showed that social media information
provision reduced parental concerns of vaccine risks(Daley et al, American J of
Preventative Medicine, 2018; Glanz et al, Pediatrics, 2017).
• Using expert sources (e.g., CDC) to correct misinformation based on “observational
correction” on social media reduces misconceptions towards Zika virus (Vraga and
Bode, Science Communication, 2017.

NEW DEVELOPMENTS TO COMBAT VACCINE
MISINFORMATION
• Epidemic prevalence information on social
networks can mediate emergent collective
outcomes in voluntary vaccine schemes
(Sharma et al, 2019).
• Create a tipping point (25%) of consensus
through network majority illusion (Centola,
2018).
• In theory, homogenous networks are more
conducive to large-scale coordination
(Piedrahita et al, 2018)
• Creating safe spaces with access to
anonymous peers for discussing stigma and
taboo health issues.
• Remove hostility and incivility from online
interactions.
77
(Sharma et al, PLoS Computational Biology, 2019)

POLICIES TO COUNTER MISINFORMATION
https://www.poynter.org/ifcn/anti-misinformation-actions/
In January 2019, the Canadian government
announced a multi-pronged effort to combat
misinformation ahead of elections in the fall.
A “Critical Election Incident Public Protocol”
that will monitor and notify other agencies
and the public about disinformation attempts.
That task force will be led by five non-political
officials and is an addition to a “rapid
response mechanism” housed within the
Department of Foreign Affairs.

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The threats of connectivity

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