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Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)
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Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)

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Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)

Political Hyperlinking In Web 1.0 And Web 2.0 (21 May2009)

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  • 1. Virtual Knowledge Studio (VKS) Political hyperlinks in Web 1.0 and Web 2.0 Dr. Han Woo PARK Visiting Research Fellow Oxford Internet Institute, UK Associate Professor Department of Media & Communication YeungNam University 214-1 Dae-dong, Gyeongsan-si, Gyeongsangbuk-do 712-749 Republic of Korea hanpark@ynu.ac.kr http://www.hanpark.net A co-leader of WCU Project: Investigating Internet-based Politics with e-Research Tools. Invited speech, Statistical Cybermetrics Research Group, University of Wolverhampton, UK, 27 May, 2009 1
  • 2. Three topics for today • Is a hyper-linking socio-political or technical behavior? • How different/similar are hyper-linking practices between Web 1.0 and Web 2.0? • What are advantages of massively- collected hyper-link data using search engines for political and electoral communication research? 2
  • 3. A comment from those who are NOT doing a link analysis • In a chapter of The Sage Handbook of Online Research Methods edited by Fielding et al. (2008), Horgan emphasizes that „link analysis‟ has become an active research domain in examining social behavior online. 3
  • 4. http://participatorysociety.org/wiki/index .php?title=Online_Research
  • 5. Part 1 This part is based on • Kim, H., & Park, H. W. (2007). Web sphere analysis for political websites: The 2004 national assembly election in South Korea. In Kluver, R., Jankowski, N., Foot, K., & Schneider, S. (Eds.). The internet and national elections: A comparative study of web campaigning. London: Routledge. pp. 226-239. • Park, H. W., & Kluver, R. (2008). Affiliation in political blogs in South Korea: Comparing online and offline social networks. In Goggin, G., & McLelland, M. (Eds.). Internationalizing Internet Studies. London: Routledge. pp.252-263. 5
  • 6. How political are hyperlinks? • Social roles of hyperlinks in Internet- mediated political communication (Foot & Schneider, 2006, Web campaigning) • Informing • Involving • Connecting • Mobilizing 6
  • 7. Link to central government (66 out of 77 links to Blue-House came from ruling party members) 7
  • 8. Connecting: Endorsement 8
  • 9. Connecting: Endorsement 9
  • 10. Mobilizing: Send a link to local friends who can vote 10
  • 11. Mobilizing: Send a link telling that this is a good homepage 11
  • 12. Mobilizing: Send a link sharing emotional support 12
  • 13. South Korean and Japanese political cultures: Same? Different? Relationships South Korea Japan Party-politician  Weak  Weak/strong (major/  Leader has strong minor parties) influence on party  Mediated leader role Political parties-  Ideologically  Ruling-opposition public oriented (recently)  Emerging two-party  Two-party system system Politicians-public  Increasingly  Increasingly transparent transparent  Strong civil society  Weak civil society  Local support Election regulations  Strict (media,  Strict (media, timing, donations, and F2F) and distribution) 13
  • 14. Results (3a): Linking Linking strategies South Korean Japanese politicians (N=100) politicians (N=100) Own political party 89* 70* Other political parties 4 4 Other politicians in the same party 3* 18* (national level) Other politicians in the same party (local 0* 12* level) Other politicians in a different party 0 0 (national level) Other politicians in a different party 0 0 (national level) Central government 52 38 Local governments 60 44 Local assemblies 8 3 Election-related government bodies 11 3 National assemblies 67* 37* Parliamentary committees 11 4 14 * = sig. p<0.01
  • 15. Results (3b): Linking Linking strategies South Korean politicians Japanese politicians (N=100) (N=100) Broadcasters 3 6 Internet broadcasters 0* 27* Newspapers 9 7 Internet newspapers 9 1 Civic & advocacy 37 23 groups Wireless sites 2* 15* Blogs 57* 20* International bodies 4 7 General public 3* 20* Other 43 28 15 * = sig. p<0.01
  • 16. Part 2 This part has been newly prepared for this presentation using the data of the following publications: Park, H. W., Kim, C. S., & Barnett, G. A. (2004). Socio-communicational structure among political actors on the web in South Korea: The dynamics of digital presence in cyberspace, New Media & Society. 6(3), 403-423 Park, H. W., & Kluver, R. (2009 forthcoming). Trends in online networking among South Korean politicians-A mixed method approach. Government Information Quarterly. * Regarding the National Assembly of South Korea, refer to Wikipedia‟s entry 16
  • 17. Data collection for Web 1.0 • Official homepages of S. Korean MPs • Manual collection: Observation • Inter-linkage: Who links to whom matrix • Explicit links excluding links in board • 2-Year tracking of same MPs: 2000-2001 17
  • 18. Web Year Sum Densit Centraliza types of y tion links (%) (Mean) In Out Web 2000 373 0.006 1.84 69.33 1.0 N=245 (1.52) Home 2001 515 0.009 1.19 99.55 page (2.10) 18
  • 19. Network map of 2000 Blue: GNP: Conservative: Opposition Red: MDP: Liberal: Ruling 19
  • 20. Network map of 2001 Star networks without any isolation 20
  • 21. Data modification • Network metrics and diagrams can be heavily influenced by outliers - 김홍신(Kim) Outdegree: 170 in 2000-2001 - 박원홍(Park) Outdegree: 0 -> 244 (Outlier?) - 한승수(Han) Outdegree: 0 -> 99 (Outlier?) • Free to link, and they may not be outlier • Their sites might have been refurbished to increase SEO(Search Engine Optimization) 21
  • 22. Web Year Sum Densit Centraliza types of y tion links (%) (Mean) In Out Web 2000 373 0.006 1.84 69.33 1.0 N=245 (1.52) Home 2001 267 0.002 1.20 69.67 page N=243 (1.10) 22
  • 23. Network map of 2001 before VS after modification 23
  • 24. 2000 VS 2001 (after modification) Blue: GNP: Conservative: Opposition Red: MDP: Liberal: Ruling 24
  • 25. Data collection for Web 2.0 • Personal blogs of S. Korean MPs • Manual collection: Observation • Blogroll links: Excluding links in postings • Inter-linkage: Who links to whom matrix • 2-Year tracking of same MPs: 2005-2006 • Phone interview about usage behaviors 25
  • 26. Web Year Sum Density Centraliza types of tion links (%) (Mean) In Out Web 2005 652 0.067 22.07 41.66 2.0 N=99 (6.59) Blog 2006 589 0.061 20.67 35.10 (5.95) 26
  • 27. 2005 VS 2006 Blue: GNP: Conservative: Opposition Yellow: Uri: Liberal: Ruling Green: DLP: Progressive: Opposition 27
  • 28. Six-degree on blog? • Mean and density values indicate that politicians have about 6 friends on blogrolls 28
  • 29. Web Year Sum of Density Centralization Note types links (%) (Mean) In Out Web 1.0 2000 373 0.006 1.84 69.33 Hub but, (Home N=245 (1.52) overall, page) sparse network 2001 515 0.009 1.19 99.55 (2.10) Web 2.0 2005 652 0.067 22.07 41.66 Disappe (Blog) N=99 (6.59) aring hub but getting 2006 589 0.061 20.67 35.10 denser (5.95) 29
  • 30. Types Year Gini Characteristics Web 2000 0.984 Sparse knitted 1.0 N=245 Hub-spike network (Home Winner-take-all page) 2001 0.996 Navigation-ability Website interface Web 2005 0.759 Fairly connected 2.0 N=99 Buffer-fly network (Blog) Participatory 2006 0.763 Homophily-based Personal-tie interface 30
  • 31. To what degree do Korean National Assembly members update a blog? Measurement item Answer Frequency Percent Content-creating Rarely 5 11.4 Activities Occasionally 6 13.6 About half 16 36.4 Frequently 5 11.4 Very Frequently 12 27.3 Hyperlinking Rarely 2 4.5 activities Occasionally 4 9.1 About half 13 29.5 Frequently 7 15.9 Very Frequently 18 40.9 No answer 3 6. 31
  • 32. Part 3 This part is based on Park, H. W. and Lee, Y. (2008). The Korean Presidential Election of 2007: Five Years on from the “Internet Election”. Journal of Contemporary Eastern Asia. 7 (1), 1-4. Lee, Y.-O., & Park, H. W. (2010 forthcoming). The Reconfiguration of E- Campaign Practices in Korea: A Case Study of The Presidential Primaries of 2007. International Sociology. Park, H. W. (2009, work-in-progress). How do social scientists use link data from search engines to understand Internet-based political and electoral communication. 32
  • 33. Background & Data collection • The Internet represents a massive storehouse of social networking data, enabling researchers to capture political communications by individual officials, politicians, and activists. • This is particularly true in South Korea, where political participants almost always have online representation. • Hyper-linking maps are indicative of the relative level of public awareness and the ideological orientation of candidates and parties. 33
  • 34. The 2007 presidential race within the opposition Grand National Party • Profiles of candidates – Two major candidates: Myung-Bak Lee and Geun- Hye Park • MB Lee is ex-mayor of Seoul and ex-CEO of Hyundai • GH Park is a daughter of ex- president Jeong-Hee Park – Two minor candidates but the two major candidates were selected for this research
  • 35. Difference between public opinion survey and actual turnout in GNP primary • Contrary to public opinion survey, Park ran neck-and- neck with Lee – Lee defeated Park only by 1.5% point (2,452 votes) – Furthermore, Park obtained 423 votes more than Lee from delegates, party members, and invited non- partisan participants http://gopkorea.blogs.com/south_korean_politics/
  • 36. Affiliation network diagram using pages linked to Lee’s and Park’s sites N = 901 (Lee: 215, Park: 692, Shared: 6)
  • 37. Findings & Discussions • Cyber-balkanization was observed. – Only a few webpages/sites are shared between the two campaign sites. – The campaign sites tend to attract links from like- minded sites. • The case study can be taken as an ‘evidence’ to show the existence of the theoretical correlation between the result of this particular election and hyperlink network analysis
  • 38. Data collection for the 2007 presidential election • Co-(in)link analysis of the 20 websites of the candidates/parties using the Yahoo – Also web size, incoming links, visitor traffic • Qualitative complements • Particularly usefulness: Public opinion surveys could not be published within six days before the 2007 election 38
  • 39. Changes of co-link networks during presidential campaign period 2 Dec 2007
  • 40. 11 Dec 2007
  • 41. 17 Dec 2007 D-2 day
  • 42. 11 Dec 2007 2 Dec 2007 17 Dec 2007 42
  • 43. Network Measures of Colink Networks with Three Different Points Network measures 2 Dec 07 11 Dec 2007 17 Dec 2007 Clustering coefficient 2.581 2.368 1.777 Average distance 1.564 1.821 1.681 (Cohesion value) (0.215) (0.273) (0.346) Degree centralities of sites ijworld.or.kr 0.158 0.263 0.684 leehc.org 0.000 0.053 0.263 ckp.kr 0.000 0.053 0.053 43
  • 44. Discussions Should hyperlink data collection be always conducted using (commercial) search engines? How do we know the extent to which a search engine influences/distorts research results? Is search engine value- free research tool? 44
  • 45. My answer is • Science organizes, structures, and evaluates information to develop a systematic body of knowledge. • It is up to the researcher to draw the appropriate conclusions, using his or her expertise, about the information gathered from the web using search engines. • While search engines collect data from the entire web, finding the truth from the information is the business of academics 45
  • 46. The end Thank you for listening, and thank you to my assistants Han Woo Park, Ph.D. Email: hanpark@ynu.ac.kr Website: www.hanpark.net Partially supported by a Korea Research Foundation Grant 46

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