Brief presentation of selection of social media research conducted at Robert H. Smith School of Business; used for seeding discussion of how to use innovative social network analytical techniques applied to FDA priorities.

1. Social Media Research
Brief Presentation for FDA
May 2013
Slides available for download at:
http://bit.ly/CHIDSSMforFDAWG

2. Presenters
 Ritu Agarwal, PhD
 Professor and Dean’s Chair of Information Systems
 Co-Director of CHIDS
 Kenyon Crowley, MBA, MSIS, CPHIMS
 Deputy Director of CHIDS
 Director of Health Innovation, COEHITR
 Gordon Gao, PhD
 Associate Professor of Information Systems
 Co-Director of CHIDS
 Bill Rand, PhD
 Director, Center for Complexity in Business
 Assistant Professor of Marketing
 Il-Horn Hann, PhD
 Associate Professor if Information Systems
 Co-Director, Center for Digital Innovation, Thought and Strategy (DIGITS)
2 May 2013

3. Topic agenda
 Introduction of research team and CHIDS
 Gao
 Online ratings of healthcare services
 Information diffusion in online communities
 Pain management preferences concept
 Rand
 Trust and Influence in social media
 Urgent diffusion
 Preference mapping
 Social media recruiting
 Hann
 Social influence and biases in online rating communities
 Discussion, concluding remarks and questions
3 May 2013

4. Strategy
TechnologyPolicy
CHIDS
Mission
 Designed to research, analyze, and
recommend solutions to challenges
surrounding the introduction and
integration of information and decision
technologies into the health care system
 Improve the practice and delivery of health
care by offering researched solutions that
impact safety, quality, access, efficiency, and
return on investment
 Works with many partners across
academia, government, clinical orgs and
industry
4 May 2013

5. Research focus areas
Impact and
Comparative
Effectiveness of
Health
Information
Systems
New Models of
Care (ACO, HIE,
PCMH, Care
Transitions)
Healthcare
Analytics (Data-
driven Health
Services Insights,
Modeling,
Operations)
Consumers,
Quality &
Transparency,
and Social Media
May 20135

6. Lack of information in the information age
 Jessie Gruman
 Three-time Cancer Survivor
 Hodgkin’s disease, cervical
cancer, colon cancer
 Then a fourth one hit her
 “I searched online but found that
comparative quality information
on surgeons specializing in
stomach cancer was virtually
nonexistent.” (Health Affairs,
2013)
6 May 2013

7. Web 2.0 comes to doctors
 53 + and growing!
0
100000200000300000400000
2005 2006 2007 2008 2009 2010
ratings physicians
Sources: Gao, McCullough, Agarwal, and Jha 2012
0
2
4
6
8
10
12
14
2005 2006 2007 2008 2009 2010
7 May 2013

8. Do online ratings reflect doctor quality?
Sources: Gao, Greenwood, McCullough, and Agarwal 2012
8 May 2013

9. Knowledge flow in online patient community
Sources: Goh, Gao and Agarwal 2013
9 May 2013

10. Stopping the biggest man-made epidemic
in the United States
 Prescription drug overdose
 Kills 40,000 people last year
 More than deaths from traffic accidents
 Distribution of morphine increased 600% from 1997-2007
 Use social media as a surveillance tool
 Text mining analysis on conversations in pain mgmt forums
 Optimize strategies to use social media to curb the growth of drug
overdose?
 Identify factors that affect a patient's risk attitude toward drug
overdose;
 Leverage the social network structure to reduce drug overdose
(information hub, influentials, followers, etc.)
10 May 2013

11. Trust and Influence in Social Media
 Who are the most influential in social
media?
 Not just the people who have the
most fans, but the boundary spanners
 Who are the most trusted?
 Can be measured by observing
behavior over time
 Trust is the best predictor of “virality”
and information diffusion
11

12. Observed data and model predictions for Hurricane Sandy data
(first tweets by hour)
0
5
10
15
20
25
30
35
40
45
50
55
Thur23
Fri7
Fri15
Fri23
Sat7
Sat15
Sat23
Sun7
Sun15
Sun23
Mon7
Mon15
Mon23
Tue7
Tue15
Tue23
Wed7
Wed15
Wed23
Thur7
Thur15
Thur23
Fri7
Fri15
Fri23
Sat7
Sat15
Sat23
Sun7
Hour
NumberofTweetsperHour
Observed data
Geometric decay NHPP model
Constant intensity NHPP model
Sum of geometric decay NHPP model
Urgent Diffusion
 Constructing a model of the
diffusion of information when the
time scale of external events is as
fast or faster than the diffusion
process
 Social media can be used to
identify key individuals who get
information out soon
 Sometimes the social network is
irrelevant
 The process really depends on the
presence of new information
12 May 2013

13. Preference Mapping from Social Media
 Rather than doing focus
groups and surveys, we
can monitor social
media to understand
consumer preferences
 Enables a quicker and
faster way to estimate
consumer preferences
13 May 2013

14. Social Media Recruiting
 Social Media Monitoring
can be used to identify
individuals with an interest
or concern
 Piloting with flu
identification
 Can target based on
location
14 May 2013

15. SEPARATING SOCIAL INFLUENCE
FROM OPINION REPORTING IN
SOCIAL MEDIA
Research Question
15 May 2013

16. 16 May 2013

17. There Is Social Influence in Online Ratings
May 20, 2013
 What Implications does this have?
 What is the value of online rating systems?
 How credible is this information for consumers?
 Risk reduction still possible?
 In general: Can we separate ‘true’ evaluations from
social influence in social networks?
17 May 2013

18. Bias in Online Ratings
May 20, 2013
 Hu, Pavlou and Zhang (2009)
 Acquisition bias
 Under-reporting bias
18 May 2013

19. Bias in Online Ratings
May 20, 2013
 Li and Hitt (2008)
19 May 2013

20. Bias in Online Ratings
May 20, 2013
 Wu and Huberman(2008)
 Perceived uniqueness in identity
20 May 2013

21. The Social Bias in Online Ratings
May 20, 2013
Social Context Influences Ratings
Social Bias in Social Networks21 May 2013

22. Demo: Negative Rating Condition
May 20, 2013
Friend:1.00
AFTER Case
My rating: 2.30
BEFORE case
My rating: 2.80
Average Rating: 3.30
Homophily
Social Bias
Social Bias in Social Networks22 May 2013

23. Insights from the Model
May 20, 2013
 Social Bias
 Focal rating is unbiased ONLY when there is no social
factor
 Path Dependence
 Early ratings affect later ratings, therefore changing overall
rating trajectory
 Un-Correctability
 Without knowing each user's social preference, it is
impossible to correct this bias
23 May 2013

24. Discussion
 How can these findings, techniques and future
extensions of this research be applied to FDA
patient preferences goals and objectives?
 Questions
24 May 2013

25. Follow up
 Thank You!
 Kenyon Crowley
 kcrowley@rhsmith.umd.edu
 Mobile: (919) 649-2279
 Slides available for download at:
 http://bit.ly/CHIDSSMforFDAWG
25 May 2013