Health Networks Research Today
• Effect of social networks on health status

– Social support (both perceived and actual)
– Social influence (such as attitudes or norms)
– Access to resources (money, occupations, information, or
knowledge)
– Social involvement (both exclusion and inclusion)
– Transmission of disease or disease-related factors (such as
human immuno-deficiency virus and acquired immune
deficiency syndrome (HIV/AIDS), mucus, and secondhand
cigarette smoke)

* Sharon L. Brennan (2012). Health Networks. Barnett, G. A. (ed). Encyclopedia of
Social Networks. Thousands Oaks, CA: Sage Publisher. pp. 346-351.

History of SNA

Borgatti et al (2009)

3

• Theoretical rationale
– People are interconnected, and thus their health is
interconnected
– Selection and homophily are two major concepts in
health-related social network research.
– Early studies focused on mortality and morbidity

• Three main areas covered by social and communication
scientists
- The conceptulization of health and illness
- The study of their measurement and social distribution
- The explanation of patterns of health and illness

* Sharon L. Brennan (2012). Health Networks. Barnett, G. A. (ed). Encyclopedia of
Social Networks. Thousands Oaks, CA: Sage Publisher. pp. 346-351.

You and Your Friend’s Friend’s Friends
• NY Times

Comparison with other methods

Scott (1991), p.3

Types of SNA data
• Whole-network method
- Measuring all connections with others in
group
- Population

• Ego-centric method
- Snowballing
- Sample

• A combined method

Bi-linked network of politically active
A-list Korean citizen blogs (July 2005)
URI=Centre
DLP=Left
GNP=Right

Just A-list blogs exchanging links with politicians

Group, group member, liaison, isolates, dyad, tree
Richards (1995)

* Co-inlink : a link to two
different nodes from a third
node
* Co-outlink : A link from two
different nodes to a third node

Björneborn (2003)

관계모양별 네트워크 유형

C

B C B
C B A

A A A B C

D E
D D D E

E E

< 스타형 > <Y 형 > < 체인형 > < 서클형 >

관계모양별 네트워크 유형
중심 노드

< 스타형 > <Y 형 > < 체인형 > < 서클형 >
중심화 비중심화

 노드 (node): 사람

 선 (line): 사람 간 커뮤니케이션의 잠재적인 채널 Borgatti et al (2009)

바베라스와 리빗의 실험 결과

 1) 문제해결의 시간적 측면
스타형 , Y 형 > 체인형 , 서클형 ☞ 중심화 성향 (centralized)

 2) 메시지 교환의 수

서클형 > 체인형 > 스타형 , Y 형

 3) 참여한 사람들의 만족도

서클형 > 체인형 > Y 형 > 스타형

바베라스와 리빗의 실험 결과

 4) 에러 (error) 발생
스타형 , Y 형 , 체인형 < 서클형 ☞ 에러 수정의 서클형에서 빈번

 5) 자발적 리더발생 확률

서클형 > 체인형 > Y 형 > 스타형

 6) 성과 향상

서클형 > 스타형 , Y 형 , 체인형

구조적 공백 / 틈새 / 혈

A B A B

C C

구조적 공백 / 틈새 / 혈

open closed

 왼쪽에 있는 Ego- 네트워크 : 많은 구조적 공백

 오른쪽에 있는 Ego- 네트워크 : 적은 구조적 공백
Borgatti et al (2009)

구조적 공백

버
기업에서 승진을 잘 하는 사람이 실제로 연결망에
서 좋은 위치에 놓인 사람이라고 주장
트

나 나

Centrality Indicators

지역단장  연결중심성 : 직접적으로 맺

은 관계가 많은 마당발
교육팀장
신입가족

 매개중심성 : 브로커 , 중개자 ,
매니저 부사장
부회장 회장
통제자 , 수문장

지점장 본부장  근접성 : 조직 구성원과 가장

빠르게 의사소통하는 확산자 ,
팀장

방송국

조직내 확산을 연결 / 차단할 적임자는 ?

연결성 매개성 근접성
지역단장
신입가족 4 0.83 52.94

지역단장 3 0.00 50.00
교육팀장
신입가족
교육팀장 5 8.33 64.29

매니저 6 3.67 60.00
매니저 부사장
부회장 회장
지점장 4 0.83 52.94

팀장 3 0.00 50.00
지점장 본부장
본부장 5 8.33 64.29

부사장 3 14.00 60.00
팀장
부회장 2 8.00 42.86

회장 1 0.00 31.03

NodeXL

Cluster Coefficient:
' 친구의 친구를 아느냐 ' 를 보여주는 지표

네트워크 유형에 따른 중심성

B
C E
G F
F D B A C E G
(b)Circle
(b)Circle
D
A
D G
F C
E
A B

(a)Star (c)Line

Star Circle Line
연결성 A 동일 F, G 제외
매개성 A 동일 A
근접성 A 동일 A

구글의 권위 지수
Page Rank Flow Betweenness high

Level of Importance
Level of Importance
low

네트워크 유형과 확산 조정 메커니즘

< 네트워크 A>

< 네트워크 B>

클러스터 ,
구조적 등위성 , 블록 모델링

실습 : 각 번호에 해당하는 본인과 동료의 이름을 적으세요

7
2 6
1 8
3
11
5
4 10 9
22
23

21
25

24 12 13
19 20
18
14

17 26
15
16

Richards (1995)


7
2 6
1 8
3
11
5
4 10 9
22
23

21
25

24 12 13
19 20
18
14

17 26
15
16

Richards (1995)

7
2 6
1 8
3
11
5
4 10 9
22
23

21
25

24 12 13
19 20
18
14

17 26
15
16

Clustering Coefficient

연결성에서 당신은 얼마나 중요한 존재인가 ?

매개성에서 당신은 얼마나 중요한 존재인가 ?

근접성에서 당신은 얼마나 중요한 존재인가 ?


7
2 6
1 8
3
11
5
4 10 9
22
23

21
25

24 12 13
19 20
18
14

17 26
15
16

1
Richards (1995)

네트워크에서 당신은 얼마나 중요한 존재인가 ?

7
2 6
1 8
3
11
5
4 10 9
22
23

21
25

24 12 13
19 20
18

17 26
15
16

제거된 노드 수 1

파편화 정도 0.409

Richards (1995)


7
2 6
1 8
3
11
5
4 10 9
22
23

21
25

24 12 13
19 20
18
14

17 26
15
16

2
Richards (1995)


7
2 6
1 8
3
11
5
4 10 9
23

21
25

24 12 13
19 20
18

17 26
15
16



Richards (1995)


7
2 6
1 8
3
11
5
4 10 9
22
23

21
25

24 12 13
19 20
18
14

17 26
15
16

3
Richards (1995)


7
2 6
1 8
3
11
5
4 10 9
23

25

24 12 13
19 20
18

17 26
15
16



Richards (1995)


7
2 6
1 8
3
11
5
4 10 9
22
23

21
25

24 12 13
19 20
18
14

17 26
15
16

4
Richards (1995)


7
2
1 8
3
11
5
4 10 9
23

25

24 12 13
19 20
18

17 26
15
16



Richards (1995)


7
2 6
1 8
3
11
5
4 10 9
22
23

21
25

24 12 13
19 20
18
14

17 26
15
16

5
Richards (1995)


7
2
1 8
3
11
5
10 9
23

25

24 12 13
19 20
18

17 26
15
16



Richards (1995)


7
2 6
1 8
3
11
5
10 9
4
22
23

21
25

24 12 13
19 20
18
14

17 26
15
16

파편화 파편화 파편화 파편화 파편화
(1 개 노드 제거 ) (2 개 노드 제거 ) (3 개 노드 제거 ) (4 개 노드 제거 ) (5 개 노드 제거 )
0.409 0.468 0.778 0.886 0.902
(14) (14/22) (14/22/21) (14/22/21/6) (14/22/21/6/4)

http://novaspivack.typepad.com/nova_spivacks_weblog/2007/02/steps_towards_a.html 에서 재인용

Big data
 Big data usually includes data sets with sizes beyond the
ability of commonly-used software tools to capture,
manage, and process the data within a tolerable elapsed
time.
 Big data sizes may vary per discipline.
 Characteristics: Garner’s 3Vs plus SAS’s VC
- Volume (amount of data), velocity (speed of data in and
out), variety (range of data types and sources)
- Variability: Data flows can be highly inconsistent with
daily, seasonal, and event-triggered peak data loads
- Complexity: Multiple data sources requiring cleaning,
linking, and matching the data across systems.

All models are wrong but some are useful
- Emergence of data author on dataverse

Big data and the end of theory?
 Does big data have the answers? Maybe some, but not all, says -
Mark Graham
 In 2008, Chris Anderson, then editor of Wired, wrote a provocative
piece titled The End of Theory. Anderson was referring to the ways
that computers, algorithms, and big data can potentially generate
more insightful, useful, accurate, or true results than specialists or
domain experts who traditionally craft carefully targeted hypotheses
and research strategies.
 We may one day get to the point where sufficient quantities of big
data can be harvested to answer all of the social questions that most
concern us. I doubt it though. There will always be digital divides;
always be uneven data shadows; and always be biases in how
information and technology are used and produced.
 And so we shouldn't forget the important role of specialists to
contextualise and offer insights into what our data do, and maybe
more importantly, don't tell us.

http://www.guardian.co.uk/news/datablog/2012/mar/09/big-data-theory

CONNECTED: THE SURPRISING P OWER OF
OUR SOCIAL NETWORKS AND HOW THEY
SHAPE OUR LIVES

By Nicholas A. Christakis and James H. Fowler
Illustrated. 338 pp. Little, Brown & Company

Nicholas Christakis: How social networks predict
epidemics

James Fowler - Back to the Village

CSS Approach
1. development of webometric tools to automate
social Internet research process (e.g., data
collection and analysis from search engines,
SNS and microblogging sites)
2. experimentation with new types of data
visualization (e.g, HNA and dynamic
geographical mappings using Google)

Why CSS?
Bonacich, P. (2004).
The Invasion of the Physicists. Social Networks 26(3): 285-288

• Savage and Burrows (2007, p.
886) laments, “Fifty years ago,
academic social scientists might
be seen as occupying the apex of
the – generally limited – social
science research ‘apparatus’. Now
they occupy an increasingly
marginal position in the huge
research infrastructure.

http://www.nature.com/news/compu
-social-science-making-the-links-1.1

http://www.nature.com/news/computational-social-science-making-the-links-
1.11243

http://www.nature.com/news/facebook-experiment-boosts-us-voter-turnout-1.114
http://overstated.net/2010/11/04/how-voters-turned-out-on-facebook

“Webometrics refers to a set of research methods that
illustrates texts and their web linkages as a network and
quantitatively examine the spreadable aspects of web-
mediated communication activities of social actors and issues
(Jenkins, 2011), in comparison to traditional methods (Savage
& Burrows, 2007; Salganik & Levy, 2012). ” (by Han Woo Park)

Seminal publications: * 실시간 피인용률 보기
 Garton, L., Haythornthwaite, C., & Wellman, B. (1997).
Studying online social networks. Journal of Computer-
Mediated Communication, 3(1).
 Wellman, B. (2001). 'Computer networks as social
networks,' Science,Vol. 293, Issue (14), pp. 2031-2034.
 Park, H. W. (2003). Hyperlink network analysis: A new
method for the study of social structure on the web.
Connections, 25(1), 49-61 .
 Park, H. W., & Thelwall, M. (2003). Hyperlink analyses of
the World Wide Web: A review. Journal of Computer-
Mediated Communication, 8(4).

Recent special issues related to CSS
 Special issues

- Social Science Computer Review, 2011, 29(3)
Theme: Social Networking Activities Across Countries

- Asian Journal of Communication, 2011, 21(5),
Theme: Online Social Capital and Participation in Asia-Pacific

- Scientometrics, 2012, 90(2)
Theme : Triple Helix and Innovation in Asia using Scientometrics,
Webometrics, and Informetrics

- Journal of Computer-Mediated Communication, 2012, 17(2)
Theme: Hyperlinked Society

Selected publications related to CSS
 Recent publications

- Park, H. W., Barnett, G. A., & Chung, C. J. (2011). Structural changes in the global hyperlink
network: Centralization or diversification. Global networks. 11 (4). 522–542
- Lim, Y. S., & Park, H. W. (2011). How Do Congressional Members Appear on the Web?:
Tracking the Web Visibility of South Korean Politicians. Government Information Quarterly.
28 (4), 514-521.
- Sandra González-Bailón, Rafael E. Banchs and Andreas Kaltenbrunner (2012). Emotions,
Public Opinion, and U.S. Presidential Approval Rates: A 5-Year Analysis of Online Political
Discussions Human Communication Research
- Sams, S., Park, H. W. (2012 forthcoming). The Presence of Hyperlinks and Messages on Social
Networking Sites: A Case Study of Cyworld in Korea. Journal of Computer-Mediated
Communication
- Nam, Y., Lee, Y.-O., Park, H.W. (2013, March). Can web ecology provide a clearer
understanding of people’s information behavior during election campaigns?. Social Science
Information.

Mike Thelwall: WA 2.0

http://lexiurl.wlv.ac.uk/index.html

Prof. Han Woo PARK
CyberEmotions Research Center
Department of Media and Communincation,
YeungNam University, Korea
hanpark@ynu.ac.kr
http://www.hanpark.net

Formerly,
World Class University Webometrics Institute

WCU
WEBOMETRICS
INSTITUTE
INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS

Social Web Network Analysis-Health Communication Networks

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