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Investigating Internet-based  Korean politics using e-research tools Prof.  Han Woo PARK Associate Professor  Dept. of  Me...
Introduction <ul><li>What is e-research? </li></ul><ul><li>Development of Webometrics e-research tools   </li></ul><ul><li...
How is different from e-science,  e-humanities, e-social science? What is  e-research ? What is current status of  e-resea...
 
What is e-research? A minor but growing approach to the study of e-science is  the  methodological perspective  based on t...
Two areas of e-research in Social Science <ul><li>1) development of online tools  to automate the research process , such ...
http://participatorysociety.org/wiki/index.php?title=Online_Research
<Table 1> Development stage of e-Science  Nentwich(2003)  Type Traditional Science   ------------------------->  e-Science...
<ul><li>- National variations in e-science projects(Meyer & Schroeder, 2008) </li></ul><ul><li>UK (e-Social Science initia...
<ul><li>Korea’s e-science program has evolved in the natural, biomedical and engineering sciences with a strong emphasis o...
<ul><li>Korean scholarship in humanities and social sciences is  not mature enough   to accept the use of sophisticated di...
WCUBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITICS WITH E-RESEARCH TOOLS Empirical findings on current status of...
<ul><li>Conducted a refined webometrics analysis of 1,055 webpages  </li></ul><ul><li>and 810 sites. </li></ul><ul><li>1) ...
<ul><li>Frequently occurring key words in e-science webpages in Korea </li></ul>WCU WEBOMETRICS INSTITUTE Results Words ar...
<ul><li>Websites retrieved more than two times </li></ul>WCU WEBOMETRICS INSTITUTE Websites are larger according to their ...
Author types of Korea e-science websites   WCU WEBOMETRICS INSTITUTE Media sites were the most frequently retrieved, with ...
<ul><li>The size of the ‘plus’: Individual sites corresponds to the frequency of their retrieval </li></ul><ul><li>The siz...
Inter-link network analysis WU WEBOMETRICS INSTITUTE <ul><li>One large cluster: Eight organizational sites + One small clu...
Why do we need e-research to investigate Internet-mediated politics in South Korea?   <ul><li>Highest proportion of broadb...
Korea’s politicized cyberspace Core-peripheral structure of diffusion Similar to ‘Slash dot effect’ in the US, web-based d...
Blogging and citizenship <ul><li>Free, easy blogging allows Internet-connected citizens to become journalists </li></ul><u...
Theoretical scenarios of  the political role of the Internet <ul><li>Equalization VS Normalization </li></ul><ul><ul><li>F...
<ul><li>Foot & Schneider (2006). Web campaigning </li></ul><ul><li>Informing </li></ul><ul><li>Involving </li></ul><ul><li...
Sunstein’s  Republic.com 2.0 <ul><li>Argues (from a U.S. perspective) that  </li></ul><ul><ul><li>the Internet supports di...
Sunstein’s Republic.com 2.0 <ul><li>Points (from a U.S. perspective) to  </li></ul><ul><ul><li>Cybercascade </li></ul></ul...
Theoretical controversy of  Balkanization Unjustified temporal effect: Some  negative  evidences about deepening divide Si...
Theoretical controversy of  Mobilization Several factors  involved: Socio-political-cultural constraints and practices (e....
Axel Brun’s produsage is defined as  &quot;the collaborative and continuous building and extending of existing content in ...
Collective Intelligence Theory by Surowiecki  <ul><li>Three kinds of problems related to CI </li></ul><ul><li>Cognition, C...
WeboNaver: API-based search tool for the Naver
Why Naver? WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS <ul><li>“ Republic of Nave...
 
 
 
How to use the data from WeboNaver <ul><li>Web-mentions of ten Korean MPs on Naver during September 2009 </li></ul>
 
Data :  3/Sep/2009 ~27/Sep/2009, 25 times Query :  이명박 , 신종플루 , 신종인플루엔자 신종 인플루엔자 <ul><li>Social issues: President Myung-Ba...
Open API reliability <ul><li>An accessible API (application programming interface) allows customization and development of...
Open API studies <ul><li>Google Web APIs – an Instrument for Webometric Analyses? (2005)  </li></ul><ul><li>- Philipp Mayr...
Cyworld Extractor - Overview Java-based software tool that, given the URL of a politician on Cyworld, extracts comments gi...
 
s
① ② ③ The status of mini-homepy ① How active ②How famous ③How friendly Gender Name Geun-Hye Park’s mini-hompy Visitor count
IP address Cyworld-IP screen capture Seong-Min Yoo’s mini-hompy
NaverSearch Extractor This is automatic collection program for naver search result  (site, web, knowledge-in, blog, café, ...
SearchResult
 
Cyworld Extractor  –  Data One example of possible uses for the collected data is to determine the region of posters comme...
Cyworld Extractor - Data The country of origin of those users commenting from outside Korea is also possible
Twitter Extractor - Overview Sharing a similar interface and extraction mechanism with the Cyworld extractor, this applica...
Twitter Extractor - Data A simple use for this data would be to visualize a user’s network and ascertain which users are r...
Online prominence of politicians across different kinds of platforms and services <ul><li>Top politicians on  cyworld mini...
Related literature WCUWEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS <ul><li>The importa...
Related literature <ul><li>Park and his colleagues (Park & Thelwall, 2008, 2009) found that  </li></ul><ul><li>Korean poli...
WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS Background information of 18 th  Kore...
Korean politicians’ activity index on mini-hompy captured on 19 th  June, 2009
Korean politicians’ activity index on mini-hompy captured on 19 th  June, 2009
 
 
Captured on 19th June, 2009 * Female: Red ,  Male: Blue ,  Ruling party: italic Cyworld presence of Korean politicians Cyw...
Captured on 19th June, 2009 * Female: Red ,  Male: Blue ,  Ruling party: italic Cyworld presence of Korean politicians Cyw...
Correlations Web visibility Visitor Count Book marked Scraped Postings Active Score Famous Score Friendly Score Web visibi...
Correlations Web visibility Visitor Count Book marked Scraped Postings Active Score Famous Score Friendly Score Spearman W...
WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITICS WITH E-RESEARCH TOOLS Case 2.  Cyworld Mini-hompies of Kor...
* Female: Red ,  Male: Blue ,  Ruling party: italic From 27th Aug to 10th Sep, 2009   Online presence of Korean politician...
Table 1.   Summary of comments posted on ten political profile pages between  April 2008 and June 2009 . One politician wa...
Why do Kyeong-Tae Jo and Kyoeng-Won Na  have so many comments? <ul><li>After South Korean government concluded negotiation...
South Koreans fearing 'mad cow disease' fight US beef imports in May and June 2008
<ul><li>On May 7th, 2008, Kyeong-Tae Joe disputed with Woon-choen Chung, a minister of Ministry for food, agriculture, for...
<ul><li>In June 2008, Kyeong-Won Na received the biggest comments from the citizens among all politicians using mini-hompy...
Why do they have so many comments? <Comments on Kyeong-Won Na’s mini-hompy>  <Comments on Kyeong-Tae Jo’s mini-hompy> Date...
Discussion <ul><li>Important socio-political issues are instantly reproduced in social network sites such as Cyworld. </li...
Web visibility <ul><li>Data collection from Naver </li></ul><ul><li>Web-mentions of individual politician names across var...
WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS RQ1- The web's 20  most - visible  in...
RQ1- The web's 20  most - visible  individuals   in South Korea WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POL...
WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS **.Correlation is significant at the ...
Online visibility Female
Male Online visibility
WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS RQ2  –   Constituency : Provincial re...
WCU WEBOMETRICS INSTITUTE INSTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS RQ2-  Constituency:  Seoul and its vici...
WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS RQ2-   Constituency : Proportional re...
WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS Overall Results <ul><li>These results...
WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS Discussion <ul><li>Politicians use th...
Limitation and future research <ul><li>Accuracy of search results </li></ul><ul><li>Both automatic crawling and manual/sem...
Semantic network analysis of  socio-political related terms   <ul><li>Media-law </li></ul><ul><li>2009 By-elections </li><...
Method <ul><li>Data: 62 blogs & 101 online news (English) </li></ul><ul><li>Google news and blog search engines </li></ul>...
Blogs vs. News (Dendogram)   Blogs News
Semantic network of Blogs Centralization = 19.6%
Sentimental analysis of  user-generated messages Cyworld mini-hompy As of 28 October 2009
Research Methods  <ul><li>All comments between 1 April 2008 and 14 June 2009 from the visitor boards of politicians were e...
-  Sentimental Analysis Comments were categorized in one of  Following three groups (1)  Positive  : the post shows respec...
 
<ul><li>Number of labels by category </li></ul>
Sentimental Analysis of Korean Politicians ’  Cyworld mini-hompy
Sentimental Analysis of Korean Politicians ’  Cyworld mini-hompy
Sentimental Analysis of Korean Politicians ’  Cyworld mini-hompy n = 650 n = 756
Sentimental Analysis of Korean Politicians ’  Cyworld mini-hompy <ul><li>The results indicates a significant relationship ...
Sentimental Analysis of Korean Politicians ’  Cyworld mini-hompy Positive comments Negative comments <ul><li>안녕하세요 ^^  힘내시...
*Occurred Words at least 10 times in each politician’s comments  positive negative center male female
**Occurred Words at least 15 times in each politician’s comments  positive negative center male female
What types of facial expressions are displayed on official homepages of politicians? More specifically, how do facial imag...
Politicians’ facial expressions were categorized in one of following three groups: Non face,  Smile face,  frown face Onli...
Number and percentage of facial images by type (only on front page) Types Frowning No-expression Smiling Sum Frequency (Pe...
■  The result of image analysis of randomly extracted ten politicians
 
 
<ul><li>At the moment, Pearson correlations were tested in between numerical scaled variables: Age, Naver Visibility, Web ...
<ul><li>The distribution of values of facial expressions reveals some outliers.  </li></ul><ul><li>They might be particula...
Structure of hyperlink connectivity during election campaign <ul><li>Primary election within GNP in 2007 </li></ul><ul><li...
<ul><li>What are advantages of  </li></ul><ul><li>massively-collected hyper-link data using search engines for political a...
Difference between public opinion survey and actual turnout in GNP primary  <ul><li>Contrary to public opinion survey,  Pa...
Affiliation network diagram using pages   linked to Lee’s and Park’s sites   N = 901 (Lee: 215, Park: 692, Shared: 6)
Changes of co-link networks during presidential campaign period   <ul><li>Co-(in)link analysis of the 20 websites of the c...
WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITICS WITH E-RESEARCH TOOLS Case 1. 2007 Korean Presidential Ele...
WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITICS WITH E-RESEARCH TOOLS Background Case 1. 2007 Korean Presi...
2  Dec 2007 11  Dec 2007 17  Dec 2007
Network Measures with Three Different Points Network measures 2 Dec 07 11 Dec 2007 17 Dec 2007 Clustering coefficient 2.58...
Hogan (2008)
 
 
Network of bilinked citizen blogs URI=Centre DLP=Left GNP=Right Just A-list blogs exchanging links with politicians
Bi-linked network of politically active  A-list Korean citizen blogs (July 2005) URI=Centre DLP=Left GNP=Right Just A-list...
Inter-linking associations  among political actors Web 1.0, Web 2.0, and Twitter As of 28 October 2009
 
 
<ul><li>Compete </li></ul>
Web 1.0, Web 2.0 &Twitter  (1/7) <ul><li>Research purpose: </li></ul><ul><ul><li>To investigate structural changes in hype...
2000 VS 2001  Blue: GNP: Conservative: Opposition Red: MDP: Liberal: Ruling Star networks without any isolation
2005 VS 2006
<ul><li>Size of node: number of tweets </li></ul>
<ul><li>Size of node: number of followers </li></ul>
Web 1.0, Web 2.0 &Twitter  (6/7) Web Types Year Sum of links (Mean) Density Centralisation Gini Coefficient IN OUT Web 1.0...
Web 1.0, Web 2.0 & Twitter  (7/7) <ul><li>Web 1.0: Hub, but sparse network </li></ul><ul><li>Web 2.0: Hub disappearing, bu...
* A type of tweets - A case Study on twitter of 18th National Assembly Members * Audiences of tweets * Topic of tweets
Thank you for listening!   WCU WEBOMETRICS INSTITUTE Acknowledgments.   WCU Webometrics Institute acknowledges that  this ...
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  • http://www.democracykorea.org/blog/2008/07/04/peace-continues/
  • http://henryjenkins.org/2008/05/interview_with_axel_bruns.html
  • 이승욱 슬라이드에서 ..
  • Alexa data: how accurate is it – using audited ABCe figures to check? http://www.malcolmcoles.co.uk/blog/alexa-data-accuracy/ lets you compare stats about websites. But how representative is its data? It&apos;s hard to know as it gives figures as %s rather than absolute numbers. So, to find out, I&apos;ve compared Alexa with the ABCe official audited data for UK newspaper sites - using the figure for the %age of each site&apos;s visitors from the UK. As the table shows, Alexa is good but not brilliant. In particular, Alexa consistently underestimates the proportion of users who are from the UK (maybe reflecting its American roots?). However, the Mirror apart, the spread of errors is reasonably consistent.
  • In journal of interactive marketing pp20-37.
  • Park &amp; Thelwall, 2008 Park &amp; Thelwall, 2009, JCMC
  • 정당이름 ?
  • 각종 지수들 정리한 것 다 넣기 .
  • 각종 지수들 정리한 것 다 넣기 .
  • 싸이월드 만든 날짜 삽입함
  • Correlations between indicators?
  • 그림에 대한 설명이 필요함 X, 축과 Y 축에 대한 설명이 필요함
  • 그림의 위 부분은 필요 없다 . 그림 넣으라면 깨끗하게 넣던지 ..
  • By-elections 결과는 없네
  • Visitor’s textual comments were gathered and analyzed to determine the conceptual framework of collective identity.
  • Visitor’s textual comments were gathered and analyzed to determine the conceptual framework of collective identity.
  • As a matter of fact, the authors attempted to take a random selection of 10 members and examined all facial images displayed within the confines of different homepage menus. Our preliminary results suggest that there are substantial differences in the use of facial images among Assembly members of different party affiliations. For example, there is a distinct difference between the ruling party and the opposition party. We saw the frequent use of non-smiling faces by the opposition members in comparison to the ruling party members. This pilot test is significant, given both the progressive nature of the opposition party and the widespread use of frowning images on its members’ homepages.
  • I have also visualized distributions of  above variables. There are some interesting observations: - Age of politicians are normally distributed. Mean and median is around 56. - The distribution of other variables are skewed, I have not tested yet but it seems as if there is a power law distribution. I might statistically test it later when necessary. - There seems to be a pattern of skewness, see the graph attached.
  • If there are any significant differences in between gender, party affiliation, constituency, experience, hometown and above variables and in between them.
  • Lee lost in the vote by delegates, party members and invited non-partisan participants by 432 votes. Their votes accounted for 80 percent of the total score in selecting the nominee. But he won a public opinion poll by 8.5 percentage points over Park. http://gopkorea.blogs.com/south_korean_politics/2007/09/ex-seoul-mayor-.html
  • Note : Data were dichotomized for the calculation of clustering coefficient and geodesic distance values, and degree centralities were normalized for comparison across networks.
  • Transcript of "Overview Of Wcu Research (16 Dec2009)Sj"

    1. 1. Investigating Internet-based Korean politics using e-research tools Prof. Han Woo PARK Associate Professor Dept. of Media & Communication, YeungNamUniversity 214-1 Dae-dong, Gyeongsan-si,Gyeongsangbuk-do 712-749, S.Korea [email_address] http://www.hanpark.net Director of WCU Webometrics Institute http://english-webometrics.yu.ac.kr This is in collaboration with Dr. Yon-Soo Lim, Dr. Chieng-Leng Hsu, DPhil. Steven Sams, DPhil, Se-Jung Park, and Ting Wang. Many thanks to my colleagues and assistants!! WCU WEBOMETRICS INSTITUTE
    2. 2. Introduction <ul><li>What is e-research? </li></ul><ul><li>Development of Webometrics e-research tools </li></ul><ul><li>(WeboNaver, Cyworld Extractor, Twitter Extractor) </li></ul><ul><li>Online prominence of politicians across different kinds of platforms and services </li></ul><ul><li>Semantic network analysis of socio-political related terms </li></ul><ul><li>Sentimental analysis of user-generated messages </li></ul><ul><li>Online Image content analysis of Web 2.0 politics </li></ul><ul><li>Structure of hyperlink connectivity during election campaign </li></ul><ul><li>Web 1.0, Web 2.0, and Twitter </li></ul>
    3. 3. How is different from e-science, e-humanities, e-social science? What is e-research ? What is current status of e-research in South Korea?
    4. 5. What is e-research? A minor but growing approach to the study of e-science is the methodological perspective based on the use of new digital tools available online for conducting humanities and social science research. While the first two strands are closely associated with the natural and engineering science community, the third approach is less connected to that community and more associated with the broader interdisciplinary research community .
    5. 6. Two areas of e-research in Social Science <ul><li>1) development of online tools to automate the research process , such as communication, research management, data collection and analysis, and publication software </li></ul><ul><li>2) experimentation with new types of data visualization , such as social network and hyperlink analysis and multimedia and dynamic representations </li></ul>
    6. 7. http://participatorysociety.org/wiki/index.php?title=Online_Research
    7. 8. <Table 1> Development stage of e-Science Nentwich(2003) Type Traditional Science -------------------------> e-Science Stage 1 2 3 4 Information gathering Libraries; personal conversations Offline database Online databases; link collections; discussion lists Digital libraries; Knowbots Data production Interviews; experiments Electron, text analysis; simulation/ modeling Internet surveys Distributed computing; virtual reality Data management Card files; lists Hypertextual card files; databases Networked card files; de-central databases Data processing/ analysis With paper and pencil Electron, data-processing; expert systems Modelling; simulations Artificial intelligence
    8. 9. <ul><li>- National variations in e-science projects(Meyer & Schroeder, 2008) </li></ul><ul><li>UK (e-Social Science initiative): Focus on a broad range of </li></ul><ul><li>social science disciplines </li></ul><ul><li>Germany There has been a major focus on e-science business </li></ul><ul><li>applications </li></ul><ul><li>US (National Science Foundation): fund many e-science projects </li></ul><ul><li>in the natural sciences and recently has begun funding </li></ul><ul><li>development of e-research platforms analyzing the social </li></ul><ul><li>network structure of the Web and collecting real-time </li></ul><ul><li>multimodal behavioral data. </li></ul><ul><li>(The UK arts and humanities e-science projects, Blanke et al) </li></ul>Literature review
    9. 10. <ul><li>Korea’s e-science program has evolved in the natural, biomedical and engineering sciences with a strong emphasis on high-performance computing and advanced research networks for long-distance collaboration . </li></ul><ul><li>The main objective of Korea’s national grid initiative, the K*Grid project, is to construct the next generation Internet and business applications </li></ul>Literature review Soon and Park (2009)
    10. 11. <ul><li>Korean scholarship in humanities and social sciences is not mature enough to accept the use of sophisticated digital technologies in its research. </li></ul><ul><li>Some proponents of e-science research practices in the humanities and social sciences are actually reluctant to promote e-science more actively . </li></ul>Literature review Soon and Park (2009)
    11. 12. WCUBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITICS WITH E-RESEARCH TOOLS Empirical findings on current status of e-research in South Korea - Search queries and returned webpages and websites Korean English webpages found webpages returned sites 사이버인프라 C yberinfrastructure 8,210 296 230 사이버연구 C yberresearch 65,900 285 219 디지털인문학 digital humanities 12,300 164 128 E - 사이언스 E -science 17,000 199 142 사이버과학연구 C yberscience 58 43 35 E - 인프라 E -infrastructure 98 39 35 E - 리서치 E -research 102 28 20 E - 인문학 E -humanities 1 1 1 E - 사회과학 E -social science 0 0 0 Total   103,709 1,055 810
    12. 13. <ul><li>Conducted a refined webometrics analysis of 1,055 webpages </li></ul><ul><li>and 810 sites. </li></ul><ul><li>1) The most prominent words were extracted from the summary </li></ul><ul><li>information about the returned webpages. </li></ul><ul><li>Site sources were classified by authors into the following Categories: </li></ul><ul><li>mass media, technology-focused media, portals/blogs, </li></ul><ul><li>public organizations/governmental sites, academic </li></ul><ul><li>associations/universities, and private companies/industry sites. </li></ul><ul><li>3) Co-link, inter-link network analyses </li></ul>Data analysis WCU WEBOMETRICS INSTITUTE
    13. 14. <ul><li>Frequently occurring key words in e-science webpages in Korea </li></ul>WCU WEBOMETRICS INSTITUTE Results Words are larger according to the frequency of their occurrence but their positions are randomly-chosen for the best visualization
    14. 15. <ul><li>Websites retrieved more than two times </li></ul>WCU WEBOMETRICS INSTITUTE Websites are larger according to their frequency of retrieval; however, heir colors and locations are randomly-chosen for the best visualization
    15. 16. Author types of Korea e-science websites WCU WEBOMETRICS INSTITUTE Media sites were the most frequently retrieved, with slightly less than half of the sites for this study (44 out of 104 sites) Author types No. of sites Percent Mass media 27 26.0 Public/Government 18 17.3 Technology Media 17 16.3 Portals/Search engines/Blogs 15 14.4 Private/Industry 14 13.5 Academic/University 13 12.5 Total 104 100.0
    16. 17. <ul><li>The size of the ‘plus’: Individual sites corresponds to the frequency of their retrieval </li></ul><ul><li>The size of the lines between sites : Number of external websites </li></ul><ul><li>co-linking to the sites </li></ul><ul><li>Sites tend to be closely clustered when they are often co-linked, but the location of Each group on the diagram is randomly chosen. </li></ul>Co-link network analysis WCU WEBOMETRICS INSTITUTE
    17. 18. Inter-link network analysis WU WEBOMETRICS INSTITUTE <ul><li>One large cluster: Eight organizational sites + One small cluster of </li></ul><ul><li>three sites </li></ul><ul><li>Seven out of the 18 sites are not connected with the other sites in </li></ul><ul><li>the public domain and isolated in this particular online network. </li></ul><ul><li>The relative positions of important e-science actors within the </li></ul><ul><li>public domain </li></ul>
    18. 19. Why do we need e-research to investigate Internet-mediated politics in South Korea? <ul><li>Highest proportion of broadband users in the world </li></ul><ul><ul><li>Unique evolution of online culture in Korean cyberspace </li></ul></ul><ul><ul><li>The country’s impressive level of technological uptake </li></ul></ul><ul><li>Highly “digitalized” political activity </li></ul><ul><li>- We need to conduct “live research” to understand the dynamics of Korea politics through tracking how web objects, related terms, and hyperlinks are circulated in Korea's webosphere. </li></ul>WCU WEBOMETRICS INSTITUTE
    19. 20. Korea’s politicized cyberspace Core-peripheral structure of diffusion Similar to ‘Slash dot effect’ in the US, web-based discussion media have been playing a tremendous role in raising public awareness and providing an investigative reporting abut US beef
    20. 21. Blogging and citizenship <ul><li>Free, easy blogging allows Internet-connected citizens to become journalists </li></ul><ul><ul><li>Breaks the monopoly of the capital-intensive media? </li></ul></ul><ul><ul><li>Allows the creation of Habermas’s free discussion Public Sphere ? </li></ul></ul><ul><li>But all political User-Generated Content banned during South Korean elections! </li></ul>
    21. 22. Theoretical scenarios of the political role of the Internet <ul><li>Equalization VS Normalization </li></ul><ul><ul><li>Focusing on the Internet’s effect on the campaign practice of formally recognized political actors (e.g., parties, candidates, NGOs) </li></ul></ul><ul><li>Mobilization VS Reinforcement </li></ul><ul><ul><li>Focusing on the Internet’s effect on the citizen’s participation and political involvement </li></ul></ul>
    22. 23. <ul><li>Foot & Schneider (2006). Web campaigning </li></ul><ul><li>Informing </li></ul><ul><li>Involving </li></ul><ul><li>Connecting </li></ul><ul><li>Mobilizing </li></ul>Typology of Web features
    23. 24. Sunstein’s Republic.com 2.0 <ul><li>Argues (from a U.S. perspective) that </li></ul><ul><ul><li>the Internet supports diversity, but </li></ul></ul><ul><ul><li>individuals choose to cocoon themselves in areas of agreement, so </li></ul></ul><ul><ul><li>the net result is protection from exposure to differing opinions </li></ul></ul><ul><ul><li>the death of democracy </li></ul></ul><ul><li>Is this the case with a highly networked society (both technically and socially) like South Korea? </li></ul>
    24. 25. Sunstein’s Republic.com 2.0 <ul><li>Points (from a U.S. perspective) to </li></ul><ul><ul><li>Cybercascade </li></ul></ul><ul><ul><li>Cyberbalkanization </li></ul></ul><ul><ul><li>Echo chambers </li></ul></ul><ul><li>Is this the case with a highly networked society (both technically and socially) like South Korea? </li></ul>
    25. 26. Theoretical controversy of Balkanization Unjustified temporal effect: Some negative evidences about deepening divide Simplified media usage model: Shifting from DailyMe to Produsage Limited to political cooperation issues in a well-connected social world: ‘ Collective intelligence ’ certainly occurs in some (loose) contexts
    26. 27. Theoretical controversy of Mobilization Several factors involved: Socio-political-cultural constraints and practices (e.g., regulation, policing, IT infra, power-distance etc.) Inter-media agenda setting: Influential conventional media’s impact on users Competitive media market: Obsolete DailyMe Increasing media education: Multiple site-browsing and balanced approach
    27. 28. Axel Brun’s produsage is defined as &quot;the collaborative and continuous building and extending of existing content in pursuit of further improvement&quot; , but that's only the starting point. Again, it's important to note that the processes of produsage are often massively distributed, and not all participants are even aware of their contribution to produsage projects; their motivations may be mainly social or individual, and still their acts of participation can be harnessed as contributions to produsage
    28. 29. Collective Intelligence Theory by Surowiecki <ul><li>Three kinds of problems related to CI </li></ul><ul><li>Cognition, Coordination, Cooperation </li></ul><ul><li>Online environments where the crowd becomes wise; Balkanization may decline but mobilization may increase </li></ul><ul><li>Diversity, Independence, Decentralization </li></ul>
    29. 30. WeboNaver: API-based search tool for the Naver
    30. 31. Why Naver? WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS <ul><li>“ Republic of Naver” </li></ul><ul><li>“ Experts point out that Naver has achieved the economies of scale -- the first in the Korean Internet business--which is of advantage to the country as a whole.” (Kim & Sohn, 2007) </li></ul><ul><li>“ Korea is a great laboratory of the digital age.” (Eric Schmidt, CEO Google, 30 May 2007) </li></ul><ul><li>“ Korea’s Naver is now the world’s 5th search service provider, behind Google, Yahoo, Baidu and Microsoft.” </li></ul><ul><li>(The AP, 9 Oct 2007) </li></ul><ul><li>Naver is one of the top-ranked Web portal sites in Korea. And the Korean Web search service of Naver is in the first of NCSI (National Customer Satisfaction Index, 2003). </li></ul>
    31. 35. How to use the data from WeboNaver <ul><li>Web-mentions of ten Korean MPs on Naver during September 2009 </li></ul>
    32. 37. Data : 3/Sep/2009 ~27/Sep/2009, 25 times Query : 이명박 , 신종플루 , 신종인플루엔자 신종 인플루엔자 <ul><li>Social issues: President Myung-Bak Lee, Swine flu </li></ul>Website Knowledge-In( 지식인 ) VS Naver Scholar( 전문지식 )
    33. 38. Open API reliability <ul><li>An accessible API (application programming interface) allows customization and development of useful tools and interfaces based on the publicly available features of the search engine. - Bar-Ilan, J. a,(2005) </li></ul><ul><li>There are always differences between the an </li></ul><ul><li>API's results and the normal search results, but these are miniscule differences in comparison with the APIs of Google, Yahoo, & Bing - Mayr, P. (2009) </li></ul>
    34. 39. Open API studies <ul><li>Google Web APIs – an Instrument for Webometric Analyses? (2005) </li></ul><ul><li>- Philipp Mayr, Fabio Tosques </li></ul><ul><li>Automated Web issue analysis: A nurse prescribing case study(2006) </li></ul><ul><li>- Mike Thelwall, Saheeda Thelwall, Ruth Fairclough </li></ul>
    35. 40. Cyworld Extractor - Overview Java-based software tool that, given the URL of a politician on Cyworld, extracts comments given by citizens along with related profile attributes. The stored data, which can amount to thousands of records, is stored in a suitable format for import into statistical software
    36. 42. s
    37. 43. ① ② ③ The status of mini-homepy ① How active ②How famous ③How friendly Gender Name Geun-Hye Park’s mini-hompy Visitor count
    38. 44. IP address Cyworld-IP screen capture Seong-Min Yoo’s mini-hompy
    39. 45. NaverSearch Extractor This is automatic collection program for naver search result (site, web, knowledge-in, blog, café, cafearticle)
    40. 46. SearchResult
    41. 48. Cyworld Extractor – Data One example of possible uses for the collected data is to determine the region of posters commenting from Korea
    42. 49. Cyworld Extractor - Data The country of origin of those users commenting from outside Korea is also possible
    43. 50. Twitter Extractor - Overview Sharing a similar interface and extraction mechanism with the Cyworld extractor, this application requires the URL of a user on Twitter. It is then possible to collect all tweets and determine the attributes of the user’s follower / following network
    44. 51. Twitter Extractor - Data A simple use for this data would be to visualize a user’s network and ascertain which users are reciprocal in their friendships
    45. 52. Online prominence of politicians across different kinds of platforms and services <ul><li>Top politicians on cyworld mini-hompy </li></ul><ul><li>Top politicians on Naver </li></ul>As of 28 October 2009
    46. 53. Related literature WCUWEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS <ul><li>The importance of technology and the visibility of the firms on Web. (Martinez-Ruiz & Thelwall,2009): </li></ul><ul><li>Firms investing more in R&D and/or developing more high-impact technology are more visible on the web, but these relationships are mainly due to larger firms having higher values for all three indicators. </li></ul><ul><li>Scoping the Online Visibility of e-Research by Means of e-Research Tools. (Ackland, Fry, & Schroeder,2007): </li></ul><ul><li>The e-Science and e-Social Science programmes form two separate </li></ul><ul><li>clusters meaning that the diffusion of generic tools and infrastructure developed under the e-Science programme have not yet diffused to the social sciences. </li></ul><ul><li>Measurement of online visibility and its impact on internet traffic. (Dreze & Zufryden,2004): </li></ul><ul><li>The visibility measure we develop captures the extent to which a user is likely to come across an online reference to a company’s Web site. It is based on data collected from multiple sources that include search engine results, Web-site contents, and online directory listings. </li></ul>
    47. 54. Related literature <ul><li>Park and his colleagues (Park & Thelwall, 2008, 2009) found that </li></ul><ul><li>Korean politicians try to strengthen their competitive positions online for the purpose of complementing the offline weakness of individual actors. </li></ul><ul><li>Furthermore, online networking activities of politicians were significantly related to website contents and/or services as well as politician attributes (e.g., party affiliation, gender, term) </li></ul><ul><li>The key contributions of Webometrics to hyperlink analysis have been the development of methods for data collection, processing and validation. In addition, a range of general results has been generated about how the Web is used, primarily in academia, and establishing factors that influence web use or impact, as measured by hyperlink counts. </li></ul>
    48. 55. WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS Background information of 18 th Korean MPs Gender Age Type Male Female 30-39 40-49 50-59 60-69 70-79 Frequency (%) 251 (86.0%) 41 (14.0%) 3 (1.0%) 52 (17.8%) 144 (49.3%) 80 (27.4%) 13 (4.5%) The number of terms (how many times he/she has been elected for the national assembly) Term 1th 2th 3th 4th 5th 6th 7th Frequency (%) 130 (44.5%) 89 (30.5%) 44 (15.1%) 19 (6.5%) 6 (2.1%) 3 (1.0%) 1 (0.3%) Party Frequency (%) Constituency Classification Frequency (%) Grand National Party 168(57.5%) Seoul and its vicinity -Seoul, Incheon, Gyeonggi-do 110(37.7%) Democratic Labor Party 5(1.7%) Provincial regions -Daejeon, Ulsan, Gwangju, Busan, Daegu, Gangwon-do, Chungcheongnam/buk-do, Gyeongshangnam/buk-do, Jeollanam/buk-do, Jeju-do 131(44.9%) Democratic Party 84(28.8%) Liberty Forward Party 18(6.2%) Pro-Park Geun-hye Coalition 5(1.7%) Renewal of Korea Party 3(1.0%) Proportional representation 51(17.5%) New Progressive Party 1(.3%) Independent 8(2.7%)
    49. 56. Korean politicians’ activity index on mini-hompy captured on 19 th June, 2009
    50. 57. Korean politicians’ activity index on mini-hompy captured on 19 th June, 2009
    51. 60. Captured on 19th June, 2009 * Female: Red , Male: Blue , Ruling party: italic Cyworld presence of Korean politicians Cyworld Comments Visitor counts Bookmarked by Others Scraped Posting Submission Date Active Score Famous Score Friendly Score Kyoeng-Won Na Geun-Hye Park Geun-Hye Park Geun-Hye Park Sung-Tae Kim Geun-Hye Park Geun-Hye Park Geun-Hye Park Geun-Hye Park Hoi-Chang Lee Jung-Wook Hong Guk-Hyun Moon Ju-Young Lee Kyoeng-Won Na Kyoeng-Won Na Guk-Hyun Moon Hoi-Chang Lee Kyung-Won Na Guk-Hyun Moon Jung-Wook Hong Jin Park Dong-Yong Chung Dong-Young Jung Dong-Young Jung Kyeong-Tae Jo Dong-Young Jung Dong-Young Jung Hoi-Chang Lee Heung-Gil Ko Soo-hee Jin Guk-Hyun Moon Kyoeng-Won Na Dong-Yong Chung Guk-Hyun Moon Kyoeng-Won Na Dong-Yong Chung Geun-Hye Park Hee-Ryong Won Woon-Tae Kang Gi-Gab Kang Kook-Hyn Moon Jung-Wook Hong Hoi-Chang Lee Kyoeng-Won Na Dong-Young Jung Hong-jun An Kyung-Tae Cho Hee-Ryong Won Gi-Gab Kang Woon-Tae Kang Hee-Ryong Won Eul-Dong Kim Seok-Yong Yoon Jin-ha Hwang Hee-Ryong Won Mong-Jun Chung Sook-Mi Son Kyung-Tae Cho Mong-Jun Chung Sun-Kyo Han Jae-Chul Sim Jae-chul Sim Eul-Dong Kim Jae-chul Sim Mong-Jun Chung Hee-Ryong Won Eul-Dong Kim Mong-Jun Chung Woo-Yeo Hwang Woon-tae Kang Mong-Jun Chung Sun-Kyo Han Jeong-Wook Hong Eul-Dong Kim Sun-Kyo Han Gi-Gab Kang Jin-Pyo Kim Sun-Kyo Han Jun-pyo Hong Jun-pyo Hong
    52. 61. Captured on 19th June, 2009 * Female: Red , Male: Blue , Ruling party: italic Cyworld presence of Korean politicians Cyworld Comments Visitor counts Bookmarked by Others Scraped Posting Active Score Famous Score Friendly Score Kyoeng-Won Na Geun-Hye Park Geun-Hye Park Geun-Hye Park Geun-Hye Park Geun-Hye Park Geun-Hye Park Geun-Hye Park Hoi-Chang Lee Jung-Wook Hong Guk-Hyun Moon Kyoeng-Won Na Kyoeng-Won Na Guk-Hyun Moon Hoi-Chang Lee Kyung-Won Na Guk-Hyun Moon Jung-Wook Hong Dong-Yong Chung Dong-Young Jung Dong-Young Jung Kyeong-Tae Jo Dong-Young Jung Dong-Young Jung Hoi-Chang Lee Soo-hee Jin Guk-Hyun Moon Kyoeng-Won Na Dong-Yong Chung Guk-Hyun Moon Kyoeng-Won Na Dong-Yong Chung Hee-Ryong Won Woon-Tae Kang Gi-Gab Kang Kook-Hyn Moon Jung-Wook Hong Hoi-Chang Lee Kyoeng-Won Na Hong-jun An Kyung-Tae Cho Hee-Ryong Won Gi-Gab Kang Woon-Tae Kang Hee-Ryong Won Eul-Dong Kim Jin-ha Hwang Hee-Ryong Won Mong-Jun Chung Sook-Mi Son Kyung-Tae Cho Mong-Jun Chung Sun-Kyo Han Jae-chul Sim Eul-Dong Kim Jae-chul Sim Mong-Jun Chung Hee-Ryong Won Eul-Dong Kim Mong-Jun Chung Woon-tae Kang Mong-Jun Chung Sun-Kyo Han Jeong-Wook Hong Eul-Dong Kim Sun-Kyo Han Gi-Gab Kang Sun-Kyo Han Jun-pyo Hong Jun-pyo Hong
    53. 62. Correlations Web visibility Visitor Count Book marked Scraped Postings Active Score Famous Score Friendly Score Web visibility Pearson Correlation 1 .814 ** .787 ** .783 ** .793 ** .812 ** .824 ** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 88 88 88 88 74 76 74 Visitor Count Pearson Correlation .814 ** 1 .990 ** .988 ** .989 ** .999 ** .935 ** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 88 90 90 90 75 77 75 Book marked Pearson Correlation .787 ** .990 ** 1 .998 ** .996 ** .999 ** .924 ** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 88 90 90 90 75 77 75 Scraped Postings Pearson Correlation .783 ** .988 ** .998 ** 1 .998 ** .995 ** .914 ** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 88 90 90 90 75 77 75 Active Score Pearson Correlation .793 ** .989 ** .996 ** .998 ** 1 .994 ** .921 ** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 74 75 75 75 75 75 74 Famous Score Pearson Correlation .812 ** .999 ** .999 ** .995 ** .994 ** 1 .925 ** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 76 77 77 77 75 77 75 Friendly Score Pearson Correlation .824 ** .935 ** .924 ** .914 ** .921 ** .925 ** 1 Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 74 75 75 75 74 75 75 **. Correlation is significant at the 0.01 level (2-tailed).
    54. 63. Correlations Web visibility Visitor Count Book marked Scraped Postings Active Score Famous Score Friendly Score Spearman Web visibility Correlation Coefficient 1.000 .303 ** .461 ** .307 ** .076 .320 ** .327 ** Sig. (2-tailed) . .004 .000 .004 .517 .005 .004 N 88 88 88 88 74 76 74 Visitor Count Correlation Coefficient .303 ** 1.000 .875 ** .833 ** .573 ** .997 ** .921 ** Sig. (2-tailed) .004 . .000 .000 .000 .000 .000 N 88 90 90 90 75 77 75 Book marked Correlation Coefficient .461 ** .875 ** 1.000 .838 ** .484 ** .902 ** .871 ** Sig. (2-tailed) .000 .000 . .000 .000 .000 .000 N 88 90 90 90 75 77 75 Scraped Postings Correlation Coefficient .307 ** .833 ** .838 ** 1.000 .626 ** .843 ** .791 ** Sig. (2-tailed) .004 .000 .000 . .000 .000 .000 N 88 90 90 90 75 77 75 Active Score Correlation Coefficient .076 .573 ** .484 ** .626 ** 1.000 .586 ** .519 ** Sig. (2-tailed) .517 .000 .000 .000 . .000 .000 N 74 75 75 75 75 75 74 Famous Score Correlation Coefficient .320 ** .997 ** .902 ** .843 ** .586 ** 1.000 .929 ** Sig. (2-tailed) .005 .000 .000 .000 .000 . .000 N 76 77 77 77 75 77 75 Friendly Score Correlation Coefficient .327 ** .921 ** .871 ** .791 ** .519 ** .929 ** 1.000 Sig. (2-tailed) .004 .000 .000 .000 .000 .000 . N 74 75 75 75 74 75 75 **. Correlation is significant at the 0.01 level (2-tailed).
    55. 64. WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITICS WITH E-RESEARCH TOOLS Case 2. Cyworld Mini-hompies of Korean Legislators Figure 4: Cyworld Mini-hompies of Korean legislators Findings As seen in Figure 4, the network structure shows a clear butterfly pattern. T here is one hub (ghism) that belongs to Park Gy un-Hye (Park GH, www.cyworld.com/ghism), the daughter of ex-president Park Jeong-Hee and one of two major GNP candidates (along with president-elect Lee MB) in the 2007 presidential race.
    56. 65. * Female: Red , Male: Blue , Ruling party: italic From 27th Aug to 10th Sep, 2009   Online presence of Korean politicians Ranking Web visibility No. of Inlinks No. of webpages 1 Geun-Hye Park Geun-Hye Park Young Sun Park 2 Jin Park Dong-Yong Chung Chun Jin Kim 3 Dong-Yong Chung Moon-Soon Choi Jung Bae Cheon 4 Hoi-Chang Lee Gi-Gab Kang Yu Chul Won 5 Sek-Kyun Jung Jung-Sook Kwak Geun-Hye Park 6 Jung-Hoon Kim Gun-Hyun Lee Geun Chan Ryu 7 Young-Jin Kim Woo-Yeo Hwang Dong Chul Kim 8 Sung-Soo Kim Woon-Tae Kang Dong-Yong Chung 9 Hyung-O Kim Jin-ha Hwang Je Se Oh 10 Jun-Pyo Hong Sun-Sook Park Yang Seok Jung
    57. 66. Table 1. Summary of comments posted on ten political profile pages between April 2008 and June 2009 . One politician was selected at random from the eighty-one successfully scraped political profiles and the male and female comments posted were taken as the dataset. Politician Male Female Unknown Total 나경원 ( Kyeong-Won Na) 10547 6611 2288 19446 박근혜 ( Geun-Hye Park) 10086 7199 1651 18936 이회창 ( Hoi-Chang Lee) 8970 6284 2380 17634 조경태 ( Kyeong-Tae Cho) 2889 2412 11101 16402 정동영 ( Dong-Yong Chung) 4872 4430 981 10283 문국현 ( Kook-Hyn Moon) 3104 4229 711 8044 강기갑 ( Gi-Gap Kang) 1405 1065 3997 6467 손숙미 ( Sook-Mi Son) 1634 771 586 2991 정몽준 ( Mong-Jun Chung) 1146 409 842 2397 홍정욱 ( Jeong-Wook Hong) 913 753 126 1792
    58. 67. Why do Kyeong-Tae Jo and Kyoeng-Won Na have so many comments? <ul><li>After South Korean government concluded negotiation of American beef import in April, there are many conflicts between government and public opinion during the May, June, 2008. </li></ul><ul><li>As graph indicates, compared to before, the biggest number of comments was recorded on all assembly members’ Minihompies in May and June, 2008. </li></ul><ul><li>Among of them, specially, the biggest number of comments is recorded on mini-hompy of Kyung-TaeJo and Kyeong-Won Na. </li></ul>
    59. 68. South Koreans fearing 'mad cow disease' fight US beef imports in May and June 2008
    60. 69. <ul><li>On May 7th, 2008, Kyeong-Tae Joe disputed with Woon-choen Chung, a minister of Ministry for food, agriculture, forestry and fisheries, on TV forum. </li></ul><ul><li>He severely criticized the minister for the American beef imports with considerable potential possibilities of ‘mad cow decease’. </li></ul><ul><li>Thanks to this event, he became a star who represents citizens’ sound though he had relatively had little popularity and reputation in the real world. </li></ul><ul><li>-> Offline political events influenced online space </li></ul>
    61. 70. <ul><li>In June 2008, Kyeong-Won Na received the biggest comments from the citizens among all politicians using mini-hompy. </li></ul><ul><li>We analyzed all comments made by others on mini-hompy during the June of 2008. </li></ul><ul><li>We found most comments are also related to the American beef issue. On 5th June, she supported the import of American beef on TV discussion program. </li></ul><ul><li>Many citizens were angry and criticized her attitude and mentions when they visited her mini-hompy and leave many negative messages. </li></ul>
    62. 71. Why do they have so many comments? <Comments on Kyeong-Won Na’s mini-hompy> <Comments on Kyeong-Tae Jo’s mini-hompy> Date Total Irrelevant Related in issue on American beef Positive Negative June, 2008 9935 2309 23.24% 378 3.80% 7248 72.95% Date Total Irrelevant Related in Issue on American beef Positive Negative 7th May, 08 7,545 23 0.30% 7,514 99.59% 8 0.11% 8 th May, 08 2,744 6 0.22% 2,734 99.64% 4 0.15% 9 th May, 08 826 2 0.24% 818 99.03% 6 0.73% Total 11,115 31 0.28% 11,066 99.56% 18 0.16%
    63. 72. Discussion <ul><li>Important socio-political issues are instantly reproduced in social network sites such as Cyworld. </li></ul><ul><li>Offline politics strongly influenced online political landscape especially in sentimental message trend. </li></ul><ul><li>Online user comments can determine certain politician’s popularity and reputation. </li></ul>
    64. 73. Web visibility <ul><li>Data collection from Naver </li></ul><ul><li>Web-mentions of individual politician names across various Naver services (Blog, Image, Knowledge-in, Scholar, News, Video, Website). </li></ul><ul><li>Data were weekly collected in three points between 27 Aug and 10 Sep 2009 </li></ul><ul><li>Visibility is the average value of the sum of each categories </li></ul>
    65. 74. WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS RQ1- The web's 20 most - visible individuals in South Korea
    66. 75. RQ1- The web's 20 most - visible individuals in South Korea WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS top online visibility Party Gender Term Constituency 1 박근혜 Park Geun Hye Grand National Party F 4 Daegu 2 진영 Jin Young Grand National Party M 2 Seoul 3 정동영 Jung Dong Young Independent M 3 Jeollabuk-do 4 이회창 Lee Hoi Chang Liberty Forward Party M 3 Chungcheongnam-do 5 정세균 Jung Sek Kyun Democratic Party M 4 Jeollabuk-do 6 김정훈 Kim Jung Hoon Grand National Party M 2 Busan 7 김영진 Kim Young Jin Democratic Party M 5 Gwangju 8 김성수 Kim Sung Soo Grand National Party M 1 Gyeonggi-do 9 김형오 Kim Hyung O Independent M 5 Busan 10 홍준표 Hong Jun Pyo Grand National Party M 4 Seoul 11 이영애 Lee Young Ae Liberty Forward Party F 1 proportional representation 12 이정현 Lee Jung Hyun Grand National Party M 1 proportional representation 13 이정희 Lee Jung Hee Democratic Labor Party F 1 proportional representation 14 박지원 Park Ji Won Democratic Party M 2 Jeollanam-do 15 김태환 Kim Tae Hwan Grand National Party M 2 Gyeongsangbuk-do 16 이상민 Lee Sang Min Liberty Forward Party M 2 Daejeon 17 박선영 Park Sun Young Liberty Forward Party F 1 proportional representation 18 안상수 An Sang Soo Grand National Party M 4 Gyeonggi-do 19 정몽준 Jung Mong Jun Grand National Party M 6 Seoul 20 전여옥 Jeon Yeo Ok Grand National Party F 2 Seoul
    67. 76. WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS **.Correlation is significant at the 0.01 level (2-tailed ) Table 2 <ul><li>Table2 shows the comparison between Pearson correlation and Spearman correlation of online visibility, age, and term. </li></ul><ul><li>Furthermore the term has a strong correlation with online visibility and age respectively. </li></ul>  Visibility Age Term Visibility -     Age 0.062 -   Term .223 ** .373 ** - Spearman Visibility Age Term Visibility -     Age 0.012 -   Term .364 ** .292 ** -
    68. 77. Online visibility Female
    69. 78. Male Online visibility
    70. 79. WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS RQ2 – Constituency : Provincial region Online visibilities of the female politicians of the ruling party are higher than those of the opposition parties. Male politicians are just the opposite.
    71. 80. WCU WEBOMETRICS INSTITUTE INSTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS RQ2- Constituency: Seoul and its vicinities Female politicians of the opposition parties have higher online visibilities than female politicians of the ruling party do. Male politicians are just the opposite.
    72. 81. WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS RQ2- Constituency : Proportional representation The opposition female politicians ’ online visibilities are higher than female politicians of the ruling party. Male politicians are just the opposite. By comparing the results of the three different types of constituency (i.e. Provincial region, Seoul and its vicinities and Proportional representation) Our data suggest that the politicians of proportional representation are less visible online.
    73. 82. WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS Overall Results <ul><li>These results prove that online visibility of a Korean politician is affected by three factors – the financial strength of his/her party, importance of a given politician to his/her own party, and individual popularity off-line. </li></ul><ul><li>Online visibility of a politician statistically differs by his or her socio-demographic variables of term, gender, party and constituency. Politicians’ term is a major variable of online visibilities. </li></ul>
    74. 83. WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS Discussion <ul><li>Politicians use the internet to promote their ideas, enhance their awareness index, expand their interpersonal relationship and communicate with the citizen and so on. </li></ul><ul><li>Giving that politicians’ online visibility will be changed constantly, it is important for us to trace and study those changes. </li></ul><ul><li>In the future more information can be collected for carrying out a long-term research and to compare the online and offline visibility of the politicians. </li></ul>
    75. 84. Limitation and future research <ul><li>Accuracy of search results </li></ul><ul><li>Both automatic crawling and manual/semi-supervised parsing are needed </li></ul><ul><li>Needs to measure online visibilities across many different web services </li></ul><ul><li>Some politicians have multiple accounts, </li></ul><ul><li>for example, Cyworld minihomy, Tistory </li></ul><ul><li>blog, Daum Café, Naver blog, Twitter, etc </li></ul>WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS
    76. 85. Semantic network analysis of socio-political related terms <ul><li>Media-law </li></ul><ul><li>2009 By-elections </li></ul>As of 28 October 2009
    77. 86. Method <ul><li>Data: 62 blogs & 101 online news (English) </li></ul><ul><li>Google news and blog search engines </li></ul><ul><li>Combined search words </li></ul><ul><ul><li>Korea, Media, law, bill, reform, revision, regulation </li></ul></ul><ul><li>Time period: June 1 st ~ August 31 st </li></ul><ul><li>Research tools </li></ul><ul><ul><li>CATPAC, UCInet, & Netdraw </li></ul></ul>
    78. 87. Blogs vs. News (Dendogram) Blogs News
    79. 88. Semantic network of Blogs Centralization = 19.6%
    80. 89. Sentimental analysis of user-generated messages Cyworld mini-hompy As of 28 October 2009
    81. 90. Research Methods <ul><li>All comments between 1 April 2008 and 14 June 2009 from the visitor boards of politicians were extracted using java-based e-research tool. </li></ul><ul><li>200comments from each politician’s visitor board were </li></ul><ul><li>randomly selected </li></ul><ul><li>Conducted textual analysis to locate the main keywords </li></ul><ul><li>of comments and semantic networks . </li></ul>
    82. 91. - Sentimental Analysis Comments were categorized in one of Following three groups (1) Positive : the post shows respect, support, or rapport with the National Assembly Member. It may suggest policy issues with gentle words or polite words. (2) Negative : the post is hostile, adversarial, or rapport with the National Assembly Member. It may be trying to slander the National Assembly Member, or includes curse words. (3) Irrelevant : The post has nothing to do with the National Assembly Member or his or her policy issues. It is a general comment on politics, or may be SPAM. 4. A case of the Internet politics - A Study on mini-hompy of 18th National Assembly Members
    83. 93. <ul><li>Number of labels by category </li></ul>
    84. 94. Sentimental Analysis of Korean Politicians ’ Cyworld mini-hompy
    85. 95. Sentimental Analysis of Korean Politicians ’ Cyworld mini-hompy
    86. 96. Sentimental Analysis of Korean Politicians ’ Cyworld mini-hompy n = 650 n = 756
    87. 97. Sentimental Analysis of Korean Politicians ’ Cyworld mini-hompy <ul><li>The results indicates a significant relationship between gender and online comments. </li></ul>Chi-square = 11.472, df = 1, p <.01, two-tailed Gender Total Male Female Comments Positive 509 491 1000 Negative 247 159 406 Total 756 650 1406
    88. 98. Sentimental Analysis of Korean Politicians ’ Cyworld mini-hompy Positive comments Negative comments <ul><li>안녕하세요 ^^ 힘내시고요 . 화이팅 !! </li></ul><ul><li>존경해요 !!!!!!!!!!! </li></ul><ul><li>의원님 너무 멋지십니다 ^^ </li></ul><ul><li>멋지십니다 !! 최고 !!^^ </li></ul><ul><li>사랑하는 의원님 ! 오늘하루도 힘내세요 ! </li></ul><ul><li>응원합니다 . ^0^ </li></ul><ul><li>힘내세요 당신을 믿습니다 .^^ </li></ul><ul><li>당선 축하드립니다 ^^ 정말 멋지신 분 ! </li></ul><ul><li>감사합니다 . 사랑합니다♡ </li></ul><ul><li>쏘핫 .. 머싯쓰세영ㅋ 저흰일촌 ..♡ ㅋㅋ </li></ul><ul><li>XX 야 ! 쌍판 내밀지 마라 ! 토나온다 </li></ul><ul><li>역겨워 .. </li></ul><ul><li>창피한 줄 아세요 </li></ul><ul><li>대가리 먹물깨나 든거 같은데 헛지랄했구나 </li></ul><ul><li>그대가 짱먹으세요 빈정대기짱 말꼬리잡기짱 </li></ul><ul><li>우즈 플리즈 ! 닥쳐줄래 ??? 실실 쪼개지도 말고 가만있어 ! </li></ul><ul><li>니들은 짖어라 그거군 ㅋㅋㅋㅋ 인간부터 되시오 X 양 ! </li></ul><ul><li>그저 웃긴다 참나 </li></ul>
    89. 99. *Occurred Words at least 10 times in each politician’s comments positive negative center male female
    90. 100. **Occurred Words at least 15 times in each politician’s comments positive negative center male female
    91. 101. What types of facial expressions are displayed on official homepages of politicians? More specifically, how do facial images differ among politicians based on their socio-political-demographic attributes? Online Image content analysis of Web 2.0 politics Types Content Smiling face Turning up the corners of the mouth, usually showing their teeth; an upward curving of the corners of the mouth, revealing pleasure, happiness, or amusement; a downward curving of the corners of the eyes, expressing moderate joy. Frowning face Wrinkling of the brow, showing displeasure, anger, unrest, disapproval, and tiredness; a downward curving of the corners of the mouth; staring at something with anger, discontent, or unkindness. No-expression No movement around mouth, eyes, or brow, revealing no emotional information.
    92. 102. Politicians’ facial expressions were categorized in one of following three groups: Non face, Smile face, frown face Online Image content analysis of Web 2.0 politics
    93. 103. Number and percentage of facial images by type (only on front page) Types Frowning No-expression Smiling Sum Frequency (Percent) 154 (8.20) 471 (25.07) 1,254 (66.74) 1,879 (100.00)
    94. 104. ■ The result of image analysis of randomly extracted ten politicians
    95. 107. <ul><li>At the moment, Pearson correlations were tested in between numerical scaled variables: Age, Naver Visibility, Web Page Number and In Link Counts. </li></ul><ul><li>There is no statistically significant correlation in between Age and other variables. </li></ul><ul><li>However, as expected there are statistically significant positive correlations in between the others. </li></ul>
    96. 108. <ul><li>The distribution of values of facial expressions reveals some outliers. </li></ul><ul><li>They might be particular interesting to see if there is any meaningful relation of those politicians' extreme facial expression to the other variables. </li></ul>
    97. 109. Structure of hyperlink connectivity during election campaign <ul><li>Primary election within GNP in 2007 </li></ul><ul><li>Presidential election in 2007 </li></ul>As of 28 October 2009
    98. 110. <ul><li>What are advantages of </li></ul><ul><li>massively-collected hyper-link data using search engines for political and electoral communication research? </li></ul>
    99. 111. Difference between public opinion survey and actual turnout in GNP primary <ul><li>Contrary to public opinion survey, Park ran neck-and-neck with Lee </li></ul><ul><ul><li>Lee defeated Park only by 1 .5% point (2,452 votes) </li></ul></ul><ul><ul><li>Furthermore, Park obtained 423 votes more than Lee from delegates, party members, and invited non-partisan participants </li></ul></ul><ul><ul><li>http://gopkorea.blogs.com/south_korean_politics / </li></ul></ul>
    100. 112. Affiliation network diagram using pages linked to Lee’s and Park’s sites N = 901 (Lee: 215, Park: 692, Shared: 6)
    101. 113. Changes of co-link networks during presidential campaign period <ul><li>Co-(in)link analysis of the 20 websites of the candidates/parties using the Yahoo </li></ul><ul><ul><li>Also web size, incoming links, visitor traffic </li></ul></ul><ul><li>Qualitative complements </li></ul><ul><li>Particularly usefulness : Public opinion surveys could not be published within six days before the 2007 election </li></ul>
    102. 114. WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITICS WITH E-RESEARCH TOOLS Case 1. 2007 Korean Presidential Election Background Korea boasts the highest proportion of broadband users in the world, and there is a unique evolution of online culture in Korean cyberspace. The country’s impressive level of technological development includes a vibrant online communication environment. The online political climate during the 2007 Korean presidential election can be examined effectively using web-based data analysis. In particular, there were 12 candidates who ran for president and several parties were created in 2007 to support these candidates (see Table 1). The candidates and the parties had to compete against each other to win public attention, particular since it was difficult for citizens to differentiate their stances on issues. Particularly useful for web analysis was the fact that public opinion surveys could not be published within six days before the 2007 election. In 2002, surveys could not be published within 22 days of the presidential election. We will examine how the popularity of individual candidates and parties developed during the 2007 presidential election campaign in South Korea using web-based data collection.
    103. 115. WCU WEBOMETRICS INSTITUTE INVESTIGATING INTERNET-BASED POLITICS WITH E-RESEARCH TOOLS Background Case 1. 2007 Korean Presidential Election Table 1. Websites of Presidential Candidates and Parties Candidate Website Candidate’s Party Website Lee Myung-Bak (Lee MB) www.mbplaza.net Grand National Party (GNP) www.hannara.or.kr Chung Dong-Young (Chung DY) www.cdy21.net United New Democratic Party (UNDP) www.undp.kr Lee Hoi-Chang (Lee HC) www.leehc.org Independent Moon Kook-Hyun (Moon KH) www.moon21.kr Creative Party (CKP) www.ckp.kr Kwon Young-Ghil (Kwon YG) www.ghil.net Democratic Labor Party (DLP) www.kdlp.org Rhee In-Je (Rhee IJ) www.ijworld.or.kr Democratic Party (DP) www.minjoo.or.kr Huh Kyung-Young (Huh KY) Same as party site Economy & Republican Party (ERP) www.gonghwa.com Geum Min (Geum M) www.minnmin.net Socialist Party (KSP) www.sp.or.kr Chung Kun-Mo (Chung KM) www.bestjung.kr True Owner Coalition (TOC) www.chamjuin.or.kr Chun Kwan (Chun K) www.chamsaram.or.kr Chamsaram Society Full True Act (CSFTA) Same as candidate site Sim Dae-Pyeng (Sim DY) www.dpsim.co.kr People First Party (PFP) www.mypfp.or.kr Lee Soo-Sung (Lee SS) www.leesoosung.com People’s Coalition (PC) Same as candidate site
    104. 116. 2 Dec 2007 11 Dec 2007 17 Dec 2007
    105. 117. Network Measures 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 (Cohesion value) 1.564 (0.215) 1.821 (0.273) 1.681 (0.346) Degree centralities of sites ijworld.or.kr leehc.org ckp.kr 0.158 0.000 0.000 0.263 0.053 0.053 0.684 0.263 0.053
    106. 118. Hogan (2008)
    107. 121. Network of bilinked citizen blogs URI=Centre DLP=Left GNP=Right Just A-list blogs exchanging links with politicians
    108. 122. 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
    109. 123. Inter-linking associations among political actors Web 1.0, Web 2.0, and Twitter As of 28 October 2009
    110. 126. <ul><li>Compete </li></ul>
    111. 127. Web 1.0, Web 2.0 &Twitter (1/7) <ul><li>Research purpose: </li></ul><ul><ul><li>To investigate structural changes in hyperlink networks from Web 1.0 to Web 2.0 in South Korean Politics </li></ul></ul><ul><li>Units of analysis: </li></ul><ul><ul><li>Congress members of South Korea </li></ul></ul><ul><ul><li>Year of observations: </li></ul></ul><ul><ul><ul><li>Web 1.0: homepage, 2000 & 2001 </li></ul></ul></ul><ul><ul><ul><li>Web 2.0: blogs, 2005 & 2006 </li></ul></ul></ul><ul><ul><ul><li>Twitter: 2009 </li></ul></ul></ul>
    112. 128. 2000 VS 2001 Blue: GNP: Conservative: Opposition Red: MDP: Liberal: Ruling Star networks without any isolation
    113. 129. 2005 VS 2006
    114. 130. <ul><li>Size of node: number of tweets </li></ul>
    115. 131. <ul><li>Size of node: number of followers </li></ul>
    116. 132. Web 1.0, Web 2.0 &Twitter (6/7) Web Types Year Sum of links (Mean) Density Centralisation Gini Coefficient IN OUT Web 1.0 (Homepage) 2000 N=245 373 (1.52) 0.006 1.84 69.33 0.984 2001 515 (2.10) 0.009 1.19 99.55 0.996 Web 2.0 (Blog) 2005 N=99 652 (6.59) 0.067 22.07 41.66 0.759 2006 589 (5.95) 0.061 20.67 35.10 0.763 Twitter 2009 111 (5.05) 0.240 24.72 39.68 0.408
    117. 133. Web 1.0, Web 2.0 & Twitter (7/7) <ul><li>Web 1.0: Hub, but sparse network </li></ul><ul><li>Web 2.0: Hub disappearing, but becoming dense </li></ul><ul><li>Twitter: similar to Web 2.0 structure, and denser </li></ul><ul><li>More to work (example): </li></ul><ul><ul><li>To compare top 10 politicians ego-networks and investigate how they change </li></ul></ul>
    118. 134. * A type of tweets - A case Study on twitter of 18th National Assembly Members * Audiences of tweets * Topic of tweets
    119. 135. Thank you for listening! WCU WEBOMETRICS INSTITUTE Acknowledgments. WCU Webometrics Institute acknowledges that this research is supported from the WCU project investigating internet-based politics using e-research tools granted from South Korean Government
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