presented at Medicine 2.0'10 in Maastricht

1. Gunther
Eysenbach MD MPH
Gunther
Eysenbach MD MPH
Director, Consumer Health & Public
Health I nformatics Lab
Associate Professor
Department of Health Policy, Management and
Evaluation, University of Toronto;
Senior Scientist,
Centre for Global eHealth Innovation,
Division of Medical Decision Making and Health Care Research;
Toronto General Research Institute of the UHN,
Toronto General Hospital, Canada
geysenba@gmail.com
Pandemics in the Age of Twitter: A Case Study of
Infodemiology and Infoveillance as New Methods for
Knowledge Translation Research and Syndromic
Surveillance
Medicine 2.0
Maastricht
Nov 2010

2. Economists have something public health practitioners don’t have:
Real-time indices to track behavior and emotions

3. The premise
“The Internet has made measurable what
was previously immeasurable: The
distribution of health information in a
population, tracking (in real time) health
information trends over time, and
identifying gaps between information
supply and demand. “
Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006

4. Research Goals
Developing innovative tools & methods
to measure/track health-related attitudes,
knowledge, emotions, public attention,
behavior in real time for public health
using textual data from the Internet &
Social Media
Investigate how the public is using social
media during a pandemic, and how social
media can be used to engage the public

5. Gunther Eysenbach MD, MPH, www.medicine20congress.com
Image Source:
http://web2.wsj2.com/
Studying information patterns in the era of user-generated information (Web 2.0)
enables us to measure user attitudes, behavior, awareness, knowledge, attention,
information needs etc.

7. Infoveillance
• Predicting/tracking outbreaks and other
public-health relevant events,
• Tracking changes in behavior, attitudes,
knowledge (e.g. as a result of public
health messages or interventions)
• Situational awareness regarding current
concerns, issues, questions, emotions, of
the public
Eysenbach G. Infodemiology and Infoveillance
J Med Internet Res 2009: e11
http://www.jmir.org/2009/1/e11

8. The science of distribution
and determinants of
disease in populations
Epidemiology,
Polls, Focus groups
Public Health Professionals
Policy Makers
Public Health Interventions
Policy Decisions
Population Behaviour,
Attitudes,
Health Status
Traditional Knowledge
Translation Circle
PR / Media
Campaigns

9. The science of distribution
and determinants of
disease in populations
Epidemiology,
Polls, Focus groups
Public Health Professionals
Policy Makers
Public Health Interventions
Policy Decisions
Population Behaviour,
Attitudes,
Health Status
Information &
Communication
patterns
Web 1.0: Webpages, News
Web 2.0: User generated
content, social media
Searches, Navigation, Clicks
Traditional Knowledge
Translation Circle
PR / Media
Campaigns

10. “Infodemiology”
the epidemiology of information
Analyzing information & communication patterns
(on the web)
The science of distribution
and determinants of
disease in populations
Epidemiology,
Polls, Focus groups
Public Health Professionals
Policy Makers
Public Health Interventions
Policy Decisions
Population Behaviour,
Attitudes,
Health Status
Information &
Communication
patterns
Web 1.0: Webpages, News
Web 2.0: User generated
content, social media
Searches, Navigation, Clicks
Traditional Knowledge
Translation Circle
PR / Media
Campaigns
Infoveillance
Metrics

11. Infovigil
Aggregator/
Datamining/
Vizualisation
Infovigil
Vision: an open source infoveillance prototype
Centre for Global eHealth Innovation, Toronto
Public,
Clinicians,
Epidemiologists
Websites
Filter
Keywords / Concepts of Interest
Online
Questionnaires

12. Swine Flu / H1N1 Tweets Analytics Project
• between May 1st, 2009
and April 1st, 2010, we
archived over 3 million
tweets containing the
keywords or hashtags (#)
“H1N1”, “swine flu”, and
“swineflu”.
• Also archived content of
cited URLs using
webcitation.org

13. What are people talking about
in tweets?
Qualitative analysis of
H1N1/Swine Flu tweets

14. 23 %
53 %
14 %
8 %
1 %
2 %
Chew C, Eysenbach G. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak.
PLoS ONE, 2009 November 29th;5(11): e14118. http://dx.plos.org/10.1371/journal.pone.0014118.

15. Absolute number of tweets
(Blue: swine flu, red: h1n1)
spikes mainly due to major news events e.g
• [A] WHO declares pandemic,
• [P] Obama declares national emergency
• [B] Harry Potter actor Rupert Grint has Swine Flu
Media Resonance Analysis

16. Relative usage of “H1N1” terminology over “Swine Flu”
H1N1:SwineFlu Ratio
• The relative proportion of tweets using “H1N1” increased from 8.8% to 40.5% in an almost
linear fashion (R2
= .788; p < .001), indicating a gradual adoption of the WHO-recommended
H1N1 terminologyas opposed to “Swine Flu”
• also social media campaigns show some effect ([G] #oink campaign of farmers)

17. “Happiness / Humor / Mood Index”:
Smileys : Frowneys Ratio

18. Question Index
Number of tweets with ? : Total Tweets

19. Prayer Index
Number of tweets with “pray” : Total Tweets
H1N1 Hospitalizations / Deaths

20. Personal Experiences
Chew C, Eysenbach G. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak.
PLoS ONE, 2009 November 29th;5(11): e14118. http://dx.plos.org/10.1371/journal.pone.0014118.

21. Number of tweets with “personal
experiences” correlates to H1N1 incidence
Chew & Eysenbach. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak.
PloS One 2010 (in press)

22. Vaccine / Vaccination Mentionings
Chew C, Eysenbach G. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak.
PLoS ONE, 2009 November 29th;5(11): e14118. http://dx.plos.org/10.1371/journal.pone.0014118.

23. Sentiment Analysis
H1N1 Vaccine Sentiment over Time
10
20
30
40
50
60
18-May-09 15-Jun-09 13-Jul-09 10-Aug-09 7-Sep-09 5-Oct-09 2-Nov-09 30-Nov-09 28-Dec-09
% of Sample
ANTI
PRO

24. negative emotion
3%
paranoia/distrus
t
physiological
safety/harm/harm to
children
24%
vaccine and pandemic
downplay/dissuasion
16%
dissatisfaction roll-
out
negative intention
5%
Anti-Vaccination Themes
Qualitative content analysis of
n=689 anti-vaccination tweets
18 May - 28 Dec 2009

25. Conclusions
• Infoveillance: New methodology, offers wealth of
quantitative + qualitative data, complementary to
traditional survey methods, more timely and
inexpensive
• Twitter is a rich source of opinions and experiences,
which can be used for near real-time content and
sentiment analysis, knowledge translation research,
and potentially as a syndromic surveillance tool,
allowing health authorities to become aware of and
respond to real or perceived concerns/issues raised by
the public
• Social media appeared underused by Canadian public
health authorities during the H1N1 pandemic

26. “In the era of the 24-hour news cycle, the
traditional once-a-day press conference
featuring talking heads with a bunch of fancy
titles has to be revamped and supplemented
with Twitter posts, YouTube videos and the like.
The public needs to be engaged in
conversations and debate about issues of public
health, they don’t need to be lectured to.”
-Andre Picard
Picard A (2010) Lessons of H1N1: Preach less, reveal more. Globe and Mail.
Available: http://www.webcitation.org/5qYZly99e.

27. Principal Investigator:
Gunther Eysenbach MD MPH
Director, Consumer Health & Public Health Informatics Lab
Centre for Global eHealth Innovation
geysenba@gmail.com
• Thanks to CIHR & Reviewers
• Cynthia Chew (MSc Student): Coding & Qualitative
Analysis of Tweets
• Latifa Mnyusiwalla (MHI Student): Vaccination Sentiment
Analysis
• Marina Sokolova PhD, CHEO Ottawa: Natural Language
Processing
• Phil Cairns: Developer
Acknowledgements