Overview Of Wcu Research (16 Dec2009)Sj

Investigating Internet-based Korean politics using e-research tools

Prof. Han Woo PARKAssociate Professor

Dept. of Media & Communication, YeungNamUniversity214-1 Dae-dong, Gyeongsan-si,Gyeongsangbuk-do 712-749, S.Korea

hanpark@ynu.ac.krhttp://www.hanpark.net

Director of WCU Webometrics Institutehttp://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!!

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Introduction• What is e-research?

• Development of Webometrics e-research tools (WeboNaver, Cyworld Extractor, Twitter Extractor)

• Online prominence of politicians across different kinds of platforms and services

• Semantic network analysis of socio-political related terms

• Sentimental analysis of user-generated messages

• Online Image content analysis of Web 2.0 politics

• Structure of hyperlink connectivity during election campaign

• Web 1.0, Web 2.0, and Twitter

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?

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.

Two areas of e-research in Social Science

• 1) development of online tools to automate the research process, such as communication, research management, data collection and analysis, and publication software

• 2) experimentation with new types of data visualization, such as social network and hyperlink analysis and multimedia and dynamic representations

http://participatorysociety.org/wiki/index.php?title=Online_Research

Type Traditional Science -------------------------> e-Science

Stage 1 2 3 4

Information gathering

Libraries; personal

conversationsOffline database

Online databases;

link collections; discussion lists

Digital libraries; Knowbots

Data production

Interviews; experiments

Electron, text analysis;

simulation/modeling

Internet surveysDistributed 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

<Table 1> Development stage of e-Science Nentwich(2003)

- National variations in e-science projects(Meyer & Schroeder, 2008)

UK(e-Social Science initiative): Focus on a broad range of social science disciplines

Germany There has been a major focus on e-science business applications

US(National Science Foundation): fund many e-science projects in the natural sciences and recently has begun funding development of e-research platforms analyzing the social network structure of the Web and collecting real-time multimodal behavioral data. (The UK arts and humanities e-science projects, Blanke et al)

Literature review

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.

The main objective of Korea’s national grid initiative, the K*Grid project, is to construct the next generation Internet and business applications

Literature review

Soon and Park (2009)

Korean scholarship in humanities and social sciences is not mature enough to accept the use of sophisticated digital technologies in its research.

Some proponents of e-science research practices in the humanities and social sciences are actually reluctant to promote e-science more actively.

Literature review

Soon and Park (2009)

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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 Cyberinfrastructure 8,210 296 230

사이버연구 Cyberresearch 65,900 285 219

디지털인문학 digital humanities 12,300 164 128

E- 사이언스 E-science 17,000 199 142

사이버과학연구 Cyberscience 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

Conducted a refined webometrics analysis of 1,055 webpages and 810 sites.

1) The most prominent words were extracted from the summary information about the returned webpages. Site sources were classified by authors into the following Categories:

mass media, technology-focused media, portals/blogs, public organizations/governmental sites, academic associations/universities, and private companies/industry sites.

3) Co-link, inter-link network analyses

Data analysis

Frequently occurring key words in e-science webpages in Korea

Words are larger according to the frequency of their occurrence but their positions are randomly-chosen for the best visualization

Results

Websites retrieved more than two times

Websites are larger according to their frequency of retrieval; however, heir colors and locations are randomly-chosen for the best visualization

Author types of Korea e-science websites

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 PercentMass media 27 26.0

Public/Government 18 17.3Technology Media 17 16.3

Portals/Search engines/Blogs 15 14.4Private/Industry 14 13.5

Academic/University 13 12.5Total 104 100.0

Co-link network analysis

The size of the ‘plus’: Individual sites corresponds to the frequency of their retrieval The size of the lines between sites : Number of external websites co-linking to the sites Sites tend to be closely clustered when they are often co-linked, but the location of Each

group on the diagram is randomly chosen.

Inter-link network analysis

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One large cluster: Eight organizational sites + One small cluster of three sites Seven out of the 18 sites are not connected with the other sites in the public domain and isolated in this particular online network. The relative positions of important e-science actors within the public domain

Why do we need e-research to investigate Internet-mediated politics in South Korea?

• Highest proportion of broadband users in the world– Unique evolution of online culture in Korean cyberspace– The country’s impressive level of technological uptake

Highly “digitalized” political activity- 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.

Korea’s politicized cyberspace

Core-peripheral structure of Core-peripheral structure of diffusiondiffusion

Similar to ‘Slash dot effect’ in Similar to ‘Slash dot effect’ in the US, web-based discussion the US, web-based discussion media have been playing a media have been playing a tremendous role in raising tremendous role in raising public awareness and providing public awareness and providing an investigative reporting abut an investigative reporting abut US beefUS beef

Blogging and citizenship• Free, easy blogging allows Internet-

connected citizens to become journalists– Breaks the monopoly of the capital-

intensive media?– Allows the creation of Habermas’s free

discussion Public Sphere?• But all political User-Generated Content

banned during South Korean elections!

Theoretical scenarios of the political role of the Internet

• Equalization VS Normalization–Focusing on the Internet’s effect on the campaign practice of formally recognized political actors (e.g., parties, candidates, NGOs)

• Mobilization VS Reinforcement–Focusing on the Internet’s effect on the citizen’s participation and political involvement

• Foot & Schneider (2006). Web campaigning

• Informing• Involving• Connecting• Mobilizing

Typology of Web features

Sunstein’s Republic.com 2.0• Argues (from a U.S. perspective) that

– the Internet supports diversity, but– individuals choose to cocoon themselves

in areas of agreement, so– the net result is protection from

exposure to differing opinions⇒the death of democracy

• Is this the case with a highly networked society (both technically and socially) like South Korea?

Sunstein’s Republic.com 2.0• Points (from a U.S. perspective) to

– Cybercascade– Cyberbalkanization– Echo chambers

• Is this the case with a highly networked society (both technically and socially) like South Korea?

Theoretical controversy of Balkanization

Unjustified temporal effect: Some Unjustified temporal effect: Some negativenegative evidences about evidences about deepening dividedeepening divide

Simplified media usage model: Simplified media usage model: Shifting from Shifting from DailyMeDailyMe to to ProdusageProdusage

Limited to political cooperation issues Limited to political cooperation issues in a well-connected social world: in a well-connected social world: ‘‘Collective intelligenceCollective intelligence’ certainly ’ certainly occurs in some (loose) contextsoccurs in some (loose) contexts

Theoretical controversy of Mobilization

Several factors Several factors involved:involved:Socio-political-cultural constraints and Socio-political-cultural constraints and

practices (e.g., regulation, policing, IT practices (e.g., regulation, policing, IT infra, power-distance etc.) infra, power-distance etc.)

Inter-media agenda setting: Influential Inter-media agenda setting: Influential conventional media’s impact on usersconventional media’s impact on users

Competitive media market: Obsolete Competitive media market: Obsolete DailyMeDailyMe

Increasing media education: Multiple site-Increasing media education: Multiple site-browsing and balanced approachbrowsing and balanced approach

Axel Brun’s produsage is defined as Axel Brun’s produsage is defined as "the collaborative and continuous building and extending of existing content in pursuit of further improvement", but that's only the starting , but that's only the starting point. point.

Again, it's important to note that the processes of Again, it's important to note that the processes of produsage are often massively distributed, and produsage are often massively distributed, and not all participants are even aware of their not all participants are even aware of their contribution to produsage projects; their contribution to produsage projects; their motivations may be mainly social or individual, motivations may be mainly social or individual, and still their acts of participation can be and still their acts of participation can be harnessed as contributions to produsageharnessed as contributions to produsage

Collective Intelligence Theory by Surowiecki

Three kinds of problems related to CIThree kinds of problems related to CI- Cognition, Coordination, Cognition, Coordination,

CooperationCooperation

Online environments where the crowd Online environments where the crowd becomes wise; becomes wise; Balkanization may Balkanization may decline but mobilization may decline but mobilization may increase increase

- Diversity, Independence, DecentralizationDiversity, Independence, Decentralization

WeboNaver: API-based search tool for the Naver

Why Naver?Why Naver?

WCUWEBOMETRICSINSTITUTEINVESTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS

•“Republic of Naver”“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)

•“Korea is a great laboratory of the digital age.” (Eric Schmidt, CEO Google, 30 May 2007)

•“Korea’s Naver is now the world’s 5th search service provider, behind Google, Yahoo, Baidu and Microsoft.” (The AP, 9 Oct 2007) •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).

How to use the data from WeboNaver• Web-mentions of ten Korean MPs on Naver during September 2009

Data : 3/Sep/2009 ~27/Sep/2009, 25 times

Query : 이명박 , 신종플루 , 신종인플루엔자 신종 인플루엔자

• Social issues: President Myung-Bak Lee, Swine flu

Website

Knowledge-In( 지식인 ) VS Naver Scholar( 전문지식 )

Open API reliability• 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)

• There are always differences between the an 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)

Open API studies• Google Web APIs – an Instrument for

Webometric Analyses? (2005) - Philipp Mayr, Fabio Tosques

• Automated Web issue analysis: A nurse prescribing case study(2006)

- Mike Thelwall, Saheeda Thelwall, Ruth Fairclough

Cyworld Extractor - OverviewJava-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

①②③

The status of mini-homepy①How active ②How famous ③How

friendly

Gender

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, cafearticle)

SearchRes

Cyworld Extractor – Data

One example of possible uses for the collected data is to determine the region of posters commenting from Korea

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 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

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

Online prominence of politicians across different kinds of platforms and services

1. Top politicians on cyworld mini-hompy

2. Top politicians on Naver

As of 28 October 2009

Related literatureRelated literature

•The importance of technology and the visibility of the firms on Web. (Martinez-Ruiz & Thelwall,2009):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.

•Scoping the Online Visibility of e-Research by Means of e-Research Tools. (Ackland, Fry, & Schroeder,2007):The e-Science and e-Social Science programmes form two separateclusters meaning that the diffusion of generic tools and infrastructure developed under the e-Science programme have not yet diffused to the social sciences.

•Measurement of online visibility and its impact on internet traffic. (Dreze & Zufryden,2004):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.

Related literatureRelated literature• Park and his colleagues (Park & Thelwall, 2008, 2009) found that Korean politicians try to strengthen their competitive positions online for the purpose of complementing the offline weakness of individual actors.

•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)

•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.

Background information of 18Background information of 18thth Korean MPs Korean MPsGender Age

Type Male Female 30-39 40-49 50-59 60-69 70-79Frequency (%) 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 7thFrequency (%) 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%)

Korean politicians’ activity index on mini-hompy captured on 19th June, 2009

Cyworld presence of Korean politiciansCyworld Comments

Visitor counts

Bookmarked by Others

Scraped Posting

Submission Date Active Score

Famous Score

Friendly Score

Kyoeng-Won Na

Geun-Hye Park

Sung-Tae Kim

Geun-Hye Park

Hoi-Chang Lee

Jung-Wook Hong

Guk-Hyun Moon Ju-Young Lee

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

Kyeong-Tae Jo

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 KangWoon-Tae

Kang Hee-Ryong

Won Eul-Dong

Kim Seok-Yong

Yoon Jin-ha

Hwang Hee-Ryong

Won Mong-Jun

Sook-Mi SonKyung-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

Captured on 19th June, 2009

*Female: Red, Male: Blue, Ruling party: italic

Cyworld presence of Korean politiciansCyworld 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

NaKyoeng-Won

NaGuk-Hyun

Hoi-Chang Lee Kyung-Won Na Guk-Hyun

Moon Jung-Wook

Hong Dong-Yong

ChungDong-Young

JungDong-Young

Kyeong-Tae JoDong-Young

JungDong-Young

Jung Hoi-Chang Lee Soo-hee Jin Guk-Hyun

Moon Kyoeng-Won

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 KangWoon-Tae

Kang Hee-Ryong

Won Eul-Dong Kim Jin-ha Hwang Hee-Ryong

Won Mong-Jun

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

Captured on 19th June, 2009

CorrelationsWeb

visibilityVisitorCount

Bookmarked

ScrapedPostings

ActiveScore

FamousScore

FriendlyScore

Webvisibility

Pearson Correlation

1 .814** .787** .783** .793** .812** .824**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000N 88 88 88 88 74 76 74

VisitorCount

Pearson Correlation

.814** 1 .990** .988** .989** .999** .935**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000N 88 90 90 90 75 77 75

Bookmarked

Pearson Correlation

.787** .990** 1 .998** .996** .999** .924**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000N 88 90 90 90 75 77 75

ScrapedPostings

Pearson Correlation

.783** .988** .998** 1 .998** .995** .914**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000N 88 90 90 90 75 77 75

ActiveScore

Pearson Correlation

.793** .989** .996** .998** 1 .994** .921**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000N 74 75 75 75 75 75 74

FamousScore

Pearson Correlation

.812** .999** .999** .995** .994** 1 .925**

Sig. (2-tailed) .000 .000 .000 .000 .000 .000N 76 77 77 77 75 77 75

FriendlyScore

Pearson Correlation

.824** .935** .924** .914** .921** .925** 1

Sig. (2-tailed) .000 .000 .000 .000 .000 .000N 74 75 75 75 74 75 75

**. Correlation is significant at the 0.01 level (2-tailed).

CorrelationsWeb

visibilityVisitorCount

Bookmarked

ScrapedPostings

ActiveScore

FamousScore

FriendlyScore

Spearman

Webvisibility

Correlation Coefficient

1.000 .303** .461** .307** .076 .320** .327**

Sig. (2-tailed) . .004 .000 .004 .517 .005 .004N 88 88 88 88 74 76 74

VisitorCount

.303** 1.000 .875** .833** .573** .997** .921**

Sig. (2-tailed) .004 . .000 .000 .000 .000 .000N 88 90 90 90 75 77 75

Bookmarked

.461** .875** 1.000 .838** .484** .902** .871**

Sig. (2-tailed) .000 .000 . .000 .000 .000 .000N 88 90 90 90 75 77 75

ScrapedPostings

.307** .833** .838** 1.000 .626** .843** .791**

Sig. (2-tailed) .004 .000 .000 . .000 .000 .000N 88 90 90 90 75 77 75

ActiveScore

.076 .573** .484** .626** 1.000 .586** .519**

Sig. (2-tailed) .517 .000 .000 .000 . .000 .000N 74 75 75 75 75 75 74

FamousScore

.320** .997** .902** .843** .586** 1.000 .929**

Sig. (2-tailed) .005 .000 .000 .000 .000 . .000N 76 77 77 77 75 77 75

FriendlyScore

.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).

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Case 2. Cyworld Mini-hompies of Korean Legislators

Figure 4: Cyworld Mini-hompies of Korean legislators

FindingsAs seen in Figure 4, the network structure shows a clear butterfly pattern. There is one hub (ghism) that belongs to Park Gyun-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.

　 Online presence of Korean politiciansRanking 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 Chung9 Hyung-O Kim Jin-ha Hwang Je Se Oh

10 Jun-Pyo Hong Sun-Sook Park Yang Seok Jung

From 27th Aug to 10th Sep, 2009

Politician Male Female Unknown Total나경원 (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

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.

Why do Kyeong-Tae Jo and Kyoeng-Won Na have so many comments?

• 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.

• As graph indicates, compared to before, the biggest number of comments was recorded on all assembly members’ Minihompies in May and June, 2008.

• Among of them, specially, the biggest number of comments is recorded on mini-hompy of Kyung-TaeJo and Kyeong-Won Na.

South Koreans fearing 'mad cow disease' fight US beef imports in May and June 2008

• 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.

• He severely criticized the minister for the American beef imports with considerable potential possibilities of ‘mad cow decease’.

• 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.

-> Offline political events influenced online space

• In June 2008, Kyeong-Won Na received the biggest comments from the citizens among all politicians using mini-hompy.

• We analyzed all comments made by others on mini-hompy during the June of 2008.

• 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.

• Many citizens were angry and criticized her attitude and mentions when they visited her mini-hompy and leave many negative messages.

Why do they have so many comments?

Date Total IrrelevantRelated in issue on American beef

Positive Negative

June, 2008 9935 2309 23.24% 378 3.80% 7248 72.95%

Date Total IrrelevantRelated in Issue on American beefPositive Negative

7th May, 08 7,545 23 0.30% 7,514 99.59% 8 0.11%

8th May, 08 2,744 6 0.22% 2,734 99.64% 4 0.15%9th 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%

Discussion• Important socio-political issues are

instantly reproduced in social network sites such as Cyworld.

• Offline politics strongly influenced online political landscape especially in sentimental message trend.

• Online user comments can determine certain politician’s popularity and reputation.

Web visibility• Data collection from NaverData collection from Naver- Web-mentions of individual politician names across various Naver services (Blog, Image, Knowledge-in, Scholar, News, Video, Website).

- Data were weekly collected in three points between 27 Aug and 10 Sep 2009

- Visibility is the average value of the sum of each categories

RQ1-The web's 20 The web's 20 mostmost--visiblevisible individuals individuals in South Koreain South Korea

RQ1-The web's 20 The web's 20 mostmost--visiblevisible individualsindividuals in South Korea in South Korea

top online visibility Party Gender Term Constituency1 박근혜 Park Geun Hye Grand National Party F 4 Daegu2 진영 Jin Young Grand National Party M 2 Seoul3 정동영 Jung Dong Young Independent M 3 Jeollabuk-do4 이회창 Lee Hoi Chang Liberty Forward Party M 3 Chungcheongnam-do5 정세균 Jung Sek Kyun Democratic Party M 4 Jeollabuk-do6 김정훈 Kim Jung Hoon Grand National Party M 2 Busan7 김영진 Kim Young Jin Democratic Party M 5 Gwangju8 김성수 Kim Sung Soo Grand National Party M 1 Gyeonggi-do9 김형오 Kim Hyung O Independent M 5 Busan10 홍준표 Hong Jun Pyo Grand National Party M 4 Seoul11 이영애 Lee Young Ae Liberty Forward Party F 1 proportional representation12 이정현 Lee Jung Hyun Grand National Party M 1 proportional representation13 이정희 Lee Jung Hee Democratic Labor

Party F 1 proportional representation14 박지원 Park Ji Won Democratic Party M 2 Jeollanam-do15 김태환 Kim Tae Hwan Grand National Party M 2 Gyeongsangbuk-do16 이상민 Lee Sang Min Liberty Forward Party M 2 Daejeon17 박선영 Park Sun Young Liberty Forward Party F 1 proportional representation18 안상수 An Sang Soo Grand National Party M 4 Gyeonggi-do19 정몽준 Jung Mong Jun Grand National Party M 6 Seoul20 전여옥 Jeon Yeo Ok Grand National Party F 2 Seoul

Visibility Age TermVisibility - 　　Age 0.062 - 　Term .223** .373** -

Spearman Visibility Age TermVisibility - 　　

Age 0.012 - 　

Term .364** .292** -

**.Correlation is significant at the 0.01 level (2-tailed)

Table 2 •Table2 shows the comparison between Pearson correlation and Spearman correlation of online visibility, age, and term.

•Furthermore the term has a strong correlation with online visibility and age respectively.

yFemale

RQ2 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.

WCUWEBOMETRICSINSTITUTEINSTIGATING INTERNET-BASED POLITIC WITH E-RESEARCH TOOLS

RQ2- 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.

RQ2-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.

Overall ResultsOverall Results•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.

•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.

DiscussionDiscussion•Politicians use the internet to promote their ideas, enhance their awareness index, expand their interpersonal relationship and communicate with the citizen and so on.

Giving that politicians’ online visibility will be changed constantly, it is important for us to trace and study those changes.

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.

Limitation and future researchLimitation and future research• Accuracy of search results- Both automatic crawling and manual/semi-

supervised parsing are needed

• Needs to measure online visibilities across many different web services

- Some politicians have multiple accounts, for example, Cyworld minihomy, Tistory blog, Daum Café, Naver blog, Twitter, etc

Semantic network analysis of socio-political related terms

1.Media-law

2.2009 By-elections

Method• Data: 62 blogs & 101 online news (English)• Google news and blog search engines• Combined search words

– Korea, Media, law, bill, reform, revision, regulation

• Time period: June 1st ~ August 31st • Research tools

– CATPAC, UCInet, & Netdraw

Blogs vs. News (Dendogram)

Blogs News

Semantic network of Blogs

Centralization = 19.6%Centralization = 19.6%

Sentimental analysis of user-generated messages

Cyworld mini-hompy

Research Methods All comments between 1 April 2008 and 14 June 2009 from

the visitor boards of politicians were extracted using java-based e-research tool.

200comments from each politician’s visitor board were randomly selected

Conducted textual analysis to locate the main keywords of comments and semantic networks.

- 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

The Result of Sentimental Analysis

53% 51% 54%

80%72%

70%83%

44% 1% 5% 1%

59% 88%

47% 44% 46%

19% 17%26%

29%16%

IrrelevantNegativePositive

• Number of labels by category

Mong-jun Chung Dong-yong Chung Geun-Hye Park Hoi-chang Lee Kyeong-tae Cho J eong-wook Hong Kyeung-won Na Sook-mi Son Kook-hyn Moon Gi-gap KangPositive 62 105 99 107 160 141 29 1 138 166Negative 84 1 10 2 3 20 113 170 3 2Irrelevant 47 94 85 91 37 34 49 23 57 31The number of all corder disagreement 7 0 6 0 0 5 9 6 2 1Sum 193 200 194 200 200 195 191 194 198 199

Positive Negative IrrelevantF 19 20 15M 43 64 32F 0 46M 52 1 48F 38 6 39M 61 4 46F 47 1 31M 60 1 60F 79 1 10M 81 2 27F 78 9 13M 63 11 21F 10 54 23M 19 59 26F 0 68 6M 1 102 17F 82 1 39M 56 2 18F 82 0 8M 84 2 23

Kyeung-won Na

Sook-mi Son

Kook-hyn Moon

Gi-gap Kang

Mong-jun Chung

Dong-yong Chung

Geun-Hye Park

Hoi-chang Lee

Kyeong-tae Cho

Jeong-wook Hong

Sentimental Analysis of Korean Politicians’ Cyworld mini-hompy

n = 650n = 756

Chi-squareChi-square = 11.472, = 11.472, dfdf = 1, = 1, pp<.01, two-tailed<.01, two-tailed

• The results indicates a significant relationship between gender and online comments.

GenderTotalMale Female

Comments

Positive 509 491 1000

Negative 247 159 406

Total 756 650 1406

Positive comments Negative comments• 안녕하세요 ^^ 힘내시고요 . 화이팅 !! • 존경해요 !!!!!!!!!!! • 의원님 너무 멋지십니다 ^^• 멋지십니다 !! 최고 !!^^ • 사랑하는 의원님 ! 오늘하루도 힘내세요 ! • 응원합니다 . ^0^ • 힘내세요 당신을 믿습니다 .^^ • 당선 축하드립니다 ^^ 정말 멋지신 분 ! • 감사합니다 . 사랑합니다♡ • 쏘핫 .. 머싯쓰세영ㅋ 저흰일촌 ..♡ ㅋㅋ

• XX 야 ! 쌍판 내밀지 마라 ! 토나온다 • 역겨워 ..• 창피한 줄 아세요 • 대가리 먹물깨나 든거 같은데 헛지랄했구나• 그대가 짱먹으세요 빈정대기짱 말꼬리잡기짱 • 우즈 플리즈 ! 닥쳐줄래 ??? 실실 쪼개지도 말고 가만있어 ! • 니들은 짖어라 그거군 ㅋㅋㅋㅋ 인간부터 되시오 X 양 !• 그저 웃긴다 참나

*Occurred Words at least 10 times in each politician’s comments

positive

negative

center

female

**Occurred Words at least 15 times in each politician’s comments

positive

negative

center

female

Types ContentSmiling 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.

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

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

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)

■ The result of image analysis of randomly extracted ten politicians

• At the moment, Pearson correlations were tested in between numerical scaled variables: Age, Naver Visibility, Web Page Number and In Link Counts.

• There is no statistically significant correlation in between Age and other variables.

• However, as expected there are statistically significant positive correlations in between the others.

• The distribution of values of facial expressions reveals some outliers.

• They might be particular interesting to see if there is any meaningful relation of those politicians' extreme facial expression to the other variables.

Structure of hyperlink connectivity during election campaign

1. Primary election within GNP in 20072. Presidential election in 2007

• What are advantages of massively-collected hyper-link data

using search engines for political and electoral communication research?

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/

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

• 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

Case 1. 2007 Korean Presidential ElectionBackground

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.

Background

Case 1. 2007 Korean Presidential Election

Table 1. Websites of Presidential Candidates and PartiesCandidate Website Candidate’s Party WebsiteLee 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

2 Dec 2007

11 Dec 2007

17 Dec 2007

Network measures 2 Dec 07 11 Dec 2007 17 Dec 2007Clustering coefficient 2.581 2.368 1.777Average distance

(Cohesion value)

(0.215)

(0.273)

(0.346)Degree centralities

of sites

ijworld.or.kr

leehc.org

ckp.kr

Network Measures with Three Different Points

118Hogan (2008)

Network of bilinked citizen blogs

URI=CentreDLP=LeftGNP=Right

Just A-list blogs exchanging links with politicians

Bi-linked network of politically active A-list Korean citizen blogs (July 2005)

URI=CentreDLP=LeftGNP=Right

Just A-list blogs exchanging links with politicians

Inter-linking associations among political actors

Web 1.0, Web 2.0, and Twitter

• Compete

Web 1.0, Web 2.0 &Twitter (1/7)• Research purpose:

To investigate structural changes in hyperlink networks from Web 1.0 to Web 2.0 in South Korean Politics

• Units of analysis: –Congress members of South Korea–Year of observations:

• Web 1.0: homepage, 2000 & 2001• Web 2.0: blogs, 2005 & 2006• Twitter: 2009

2000 VS 2001

Blue: GNP: Conservative: Opposition

Red: MDP: Liberal: Ruling 128

Star networks without any isolation

2005 VS 2006

Size of node: number of tweets

Size of node: number of followers

Web 1.0, Web 2.0 &Twitter (6/7)

Web Types YearSum of links

(Mean)Densit

Centralisation Gini Coefficien

tIN OUT

Web 1.0(Homepage

2000N=24

(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)

2005N=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

Web 1.0, Web 2.0 & Twitter (7/7)

• Web 1.0: Hub, but sparse network• Web 2.0: Hub disappearing, but

becoming dense• Twitter: similar to Web 2.0 structure,

and denser• More to work (example):

– To compare top 10 politicians ego-networks and investigate how they change

* A type of tweets

-A case Study on twitter of 18th National Assembly Members

* Audiences of tweets

* Topic of tweets

Thank you for listening!Thank you for listening!

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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