Gunther
Eysenbach MD MPH
Gunther
Eysenbach MD MPH
Director, Consumer Health & Public
Health I nformatics Lab
Associate Pro...
Economists have something public health practitioners don’t have:
Real-time indices to track behavior and emotions
The premise
“The Internet has made measurable what
was previously immeasurable: The
distribution of health information in ...
Research Goals
Developing innovative tools & methods
to measure/track health-related attitudes,
knowledge, emotions, publi...
Gunther Eysenbach MD, MPH, www.medicine20congress.com
Image Source:
http://web2.wsj2.com/
Studying information patterns in...
Infoveillance
• Predicting/tracking outbreaks and other
public-health relevant events,
• Tracking changes in behavior, att...
The science of distribution
and determinants of
disease in populations
Epidemiology,
Polls, Focus groups
Public Health Pro...
The science of distribution
and determinants of
disease in populations
Epidemiology,
Polls, Focus groups
Public Health Pro...
“Infodemiology”
the epidemiology of information
Analyzing information & communication patterns
(on the web)
The science of...
Infovigil
Aggregator/
Datamining/
Vizualisation
Infovigil
Vision: an open source infoveillance prototype
Centre for Global...
Swine Flu / H1N1 Tweets Analytics Project
• between May 1st, 2009
and April 1st, 2010, we
archived over 3 million
tweets c...
What are people talking about
in tweets?
Qualitative analysis of
H1N1/Swine Flu tweets
23 %
53 %
14 %
8 %
1 %
2 %
Chew C, Eysenbach G. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 200...
Absolute number of tweets
(Blue: swine flu, red: h1n1)
spikes mainly due to major news events e.g
• [A] WHO declares pande...
Relative usage of “H1N1” terminology over “Swine Flu”
H1N1:SwineFlu Ratio
• The relative proportion of tweets using “H1N1”...
“Happiness / Humor / Mood Index”:
Smileys : Frowneys Ratio
Question Index
Number of tweets with ? : Total Tweets
Prayer Index
Number of tweets with “pray” : Total Tweets
H1N1 Hospitalizations / Deaths
Personal Experiences
Chew C, Eysenbach G. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1...
Number of tweets with “personal
experiences” correlates to H1N1 incidence
Chew & Eysenbach. Pandemics in the Age of Twitte...
Vaccine / Vaccination Mentionings
Chew C, Eysenbach G. Pandemics in the Age of Twitter: Content Analysis of Tweets during ...
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-O...
negative emotion
3%
paranoia/distrus
t
physiological
safety/harm/harm to
children
24%
vaccine and pandemic
downplay/dissua...
Conclusions
• Infoveillance: New methodology, offers wealth of
quantitative + qualitative data, complementary to
tradition...
“In the era of the 24-hour news cycle, the
traditional once-a-day press conference
featuring talking heads with a bunch of...
Principal Investigator:
Gunther Eysenbach MD MPH
Director, Consumer Health & Public Health Informatics Lab
Centre for Glob...
Twitter in the age of pandemics: Infodemiology and Infoveillance
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Twitter in the age of pandemics: Infodemiology and Infoveillance

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  • Twitter in the age of pandemics: Infodemiology and Infoveillance

    1. 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. 2. Economists have something public health practitioners don’t have: Real-time indices to track behavior and emotions
    3. 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. 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. 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.
    6. 6. 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
    7. 7. 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
    8. 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 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
    9. 9. “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
    10. 10. 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
    11. 11. 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
    12. 12. What are people talking about in tweets? Qualitative analysis of H1N1/Swine Flu tweets
    13. 13. 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.
    14. 14. 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
    15. 15. 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)
    16. 16. “Happiness / Humor / Mood Index”: Smileys : Frowneys Ratio
    17. 17. Question Index Number of tweets with ? : Total Tweets
    18. 18. Prayer Index Number of tweets with “pray” : Total Tweets H1N1 Hospitalizations / Deaths
    19. 19. 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.
    20. 20. 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)
    21. 21. 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.
    22. 22. 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
    23. 23. 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
    24. 24. 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
    25. 25. “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.
    26. 26. 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

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