Social Web Network Analysis-Health Communication Networks

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사회 네트워크 분석 : 헬스커뮤니케이션 연구의 적용 가능성
한국헬스커뮤니케이션학회-한양대학교 창의성&인터랙션 연구소 공동 콜로키움
2012년 9월 21일(금) 14시~ 장소: 한양대학교 언론정보대학원 멀티미디어실(행당캠퍼스 제2공학관 1층) [제1주제] 사회 네트워크 분석 : 헬스커뮤니케이션 연구의 적용 가능성(박한우 영남대 교수) [제2주제] 헬스커뮤니케이션 연구 조사에서의 샘플링 및 가중치 부여(강남준 서울대 교수)

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  • http://www.sfu.ca/~richards/Pages/negopy.htm
  • Degree NrmDegree Share ------------ ------------ ------------ 1 Mean 3.538 14.154 0.000 2 Std Dev 1.575 6.298 0.000 3 Sum 92.000 368.000 0.000
  • Degree NrmDegree Share ------------ ------------ ------------ 1 Mean 3.538 14.154 0.000 2 Std Dev 1.575 6.298 0.000 3 Sum 92.000 368.000 0.000
  • 연구실 컴퓨터의 아이패드 사진 복사한 폴더에서 Science 잡지 특집호 그림을 여기에 .. 엠비씨 준비하던 것에 있는 거 아니가 ?
  • Social Web Network Analysis-Health Communication Networks

    1. 1. Virtual Knowledge Studio (VKS) Social (Web) Network Analysis and Health CommunicationAsso. Prof. Dr. Han Woo PARKCyberEmotions ResearchInstituteDept. of Media & CommunicationYeungNam University214-1 Dae-dong, Gyeongsan-si,Gyeongsangbuk-do 712-749Republic of Koreahttp://www.hanpark.nethttp://eastasia.yu.ac.krhttp://asia-triplehelix.org
    2. 2. 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.
    3. 3. History of SNA Borgatti et al (2009) 3
    4. 4. Health Networks Research Today• 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.
    5. 5. You and Your Friend’s Friend’s Friends• NY Times
    6. 6. 6
    7. 7. 7
    8. 8. 8
    9. 9. Comparison with other methods Scott (1991), p.3
    10. 10. Borgatti et al (2009) 10
    11. 11. Types of SNA data• Whole-network method- Measuring all connections with others in group- Population• Ego-centric method- Snowballing- Sample• A combined method
    12. 12. 12Hogan (2008)
    13. 13. Bi-linked network of politically activeA-list Korean citizen blogs (July 2005) URI=Centre DLP=Left GNP=Right Just A-list blogs exchanging links with politicians
    14. 14. Group, group member, liaison, isolates, dyad, tree Richards (1995)
    15. 15. * Co-inlink : a link to twodifferent nodes from a thirdnode* Co-outlink : A link from twodifferent nodes to a third node Björneborn (2003)
    16. 16. 관계모양별 네트워크 유형 C B C BC B A A A A B CD E D D D E E E < 스타형 > <Y 형 > < 체인형 > < 서클형 >
    17. 17. 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)
    18. 18. 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
    19. 19. 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
    20. 20. 구조적 공백 / 틈새 / 혈 A B A B C C
    21. 21. 구조적 공백 / 틈새 / 혈 open closed  왼쪽에 있는 Ego- 네트워크 : 많은 구조적 공백  오른쪽에 있는 Ego- 네트워크 : 적은 구조적 공백 Borgatti et al (2009)
    22. 22. 구조적 공백버 기업에서 승진을 잘 하는 사람이 실제로 연결망에 서 좋은 위치에 놓인 사람이라고 주장트 나 나
    23. 23. Centrality Indicators 지역단장  연결중심성 : 직접적으로 맺 은 관계가 많은 마당발 교육팀장신입가족  매개중심성 : 브로커 , 중개자 , 매니저 부사장 부회장 회장 통제자 , 수문장지점장 본부장  근접성 : 조직 구성원과 가장 빠르게 의사소통하는 확산자 , 팀장 방송국
    24. 24. 조직내 확산을 연결 / 차단할 적임자는 ? 연결성 매개성 근접성 지역단장 신입가족 4 0.83 52.94 지역단장 3 0.00 50.00 교육팀장신입가족 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 50.00지점장 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
    25. 25. NodeXLCluster Coefficient: 친구의 친구를 아느냐 를 보여주는 지표
    26. 26. 네트워크 유형에 따른 중심성 B C EG 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
    27. 27. 구글의 권위 지수 Page Rank Flow Betweenness high Level of Importance Level of Importance low
    28. 28. 네트워크 유형과 확산 조정 메커니즘 < 네트워크 A> < 네트워크 B>
    29. 29. 클러스터 ,구조적 등위성 , 블록 모델링
    30. 30. 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 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    31. 31. Group, group member, liaison, isolates, dyad, tree 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 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    32. 32. 7 2 61 8 3 11 5 4 10 9 22 23 21 25 24 12 13 19 20 18 14 17 26 15 16 Clustering Coefficient
    33. 33. 연결성에서 당신은 얼마나 중요한 존재인가 ?
    34. 34. 연결성에서 당신은 얼마나 중요한 존재인가 ?
    35. 35. 매개성에서 당신은 얼마나 중요한 존재인가 ?
    36. 36. 근접성에서 당신은 얼마나 중요한 존재인가 ?
    37. 37. Group, group member, liaison, isolates, dyad, tree 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 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    38. 38. 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 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    39. 39. Group, group member, liaison, isolates, dyad, tree 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 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    40. 40. 네트워크에서 당신은 얼마나 중요한 존재인가 ? 7 2 6 1 8 3 11 5 4 10 9 23 21 25 24 12 13 19 20 18 17 26 15 16 제거된 노드 수 2 파편화 정도 0.468 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    41. 41. Group, group member, liaison, isolates, dyad, tree 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 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    42. 42. 네트워크에서 당신은 얼마나 중요한 존재인가 ? 7 2 6 1 8 3 11 5 4 10 9 23 25 24 12 13 19 20 18 17 26 15 16 제거된 노드 수 3 파편화 정도 0.778 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    43. 43. Group, group member, liaison, isolates, dyad, tree 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 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    44. 44. 네트워크에서 당신은 얼마나 중요한 존재인가 ? 7 2 1 8 3 11 5 4 10 9 23 25 24 12 13 19 20 18 17 26 15 16 제거된 노드 수 4 파편화 정도 0.886 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    45. 45. Group, group member, liaison, isolates, dyad, tree 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 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    46. 46. 네트워크에서 당신은 얼마나 중요한 존재인가 ? 7 2 1 8 3 11 5 10 9 23 25 24 12 13 19 20 18 17 26 15 16 제거된 노드 수 5 파편화 정도 0.902 Group, group member, liaison, isolates, dyad, tree Richards (1995)
    47. 47. 네트워크에서 당신은 얼마나 중요한 존재인가 ? 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)
    48. 48. NodeXL
    49. 49. http://novaspivack.typepad.com/nova_spivacks_weblog/2007/02/steps_towards_a.html 에서 재인용
    50. 50. 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.
    51. 51. All models are wrong but some are useful - Emergence of data author on dataverse
    52. 52. 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 shouldnt forget the important role of specialists to contextualise and offer insights into what our data do, and maybe more importantly, dont tell us. http://www.guardian.co.uk/news/datablog/2012/mar/09/big-data-theory
    53. 53. Health Networks Research TodayCONNECTED: THE SURPRISING P OWER OFOUR SOCIAL NETWORKS AND HOW THEYSHAPE OUR LIVESBy Nicholas A. Christakis and James H. FowlerIllustrated. 338 pp. Little, Brown & Company Nicholas Christakis: How social networks predict epidemics James Fowler - Back to the Village
    54. 54. CSS Approach1. 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)
    55. 55. 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.
    56. 56. http://www.nature.com/news/compu-social-science-making-the-links-1.1
    57. 57. http://www.nature.com/news/computational-social-science-making-the-links-1.11243
    58. 58. http://www.nature.com/news/facebook-experiment-boosts-us-voter-turnout-1.114http://overstated.net/2010/11/04/how-voters-turned-out-on-facebook
    59. 59. “Webometrics refers to a set of research methods thatillustrates texts and their web linkages as a network andquantitatively 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)
    60. 60. 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).
    61. 61. 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
    62. 62. 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.
    63. 63. Mike Thelwall: WA 2.0http://lexiurl.wlv.ac.uk/index.html
    64. 64. 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 InstituteWCUWEBOMETRICSINSTITUTEINVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS

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