STUDYING SOCIAL SCIENCE USING E-TOOLS: CASES OF SOUTH KOREA & TAIWAN

Chien-leng Hsu

WCU Webometrics InstituteYeungNam University

ABOUT THIS PRESENTATION

E-research Development in Social Science Research examples WCU Webometrics Institute

Case 1: Network of politicians in Taiwan Case 2: Network of MPs in South Korea

WHAT IS E-RESEARCH?

A minor but growing approach

The use of new digital tools

E-research in Social Science development of online

tools to automate the research process

Experimentation with new types of data visualisation

Automation of the research process Communicion Research mnagement Data collection & analysis Publication software

Data visualisation Social network analysis Hyperlink analysis Multimedia & dynamic representation

DEVELOPMENT IN SOCIAL SCIENCE (1/2)

Gary King (Harvard University, Miller Converse Lecture Series talk, 2009)

The next 50 years: spectacular increase in new data source Improved, expanded & applied

In depth studies of individual places, people, events Aggregate government statistics Survey research

Data everywhere – the growing Internet The replication movement – academic data

sharing Advances in statistical methods, informatics &

software

DEVELOPMENT IN SOCIAL SCIENCE (2/2)

Examples Opinions of activists

Sample of a few thousand interviews ↪ millions of political opinions every day in

blogosphere Social contacts

Asking respondents to recall names over past years↪ emails, SMSs, social media connections, etc

Progress in the new data-rich world Large-scale, interdisciplinary research Computer-assisted & quantitative New statistical methods & engineering required

NEW TYPES OF DATA (EXAMPLES)

Unstructured text: emails, web pages Geographic location: mobiles Social media: facebook, twitter, virtual

worlds Web surfing artifacts: clicks, searches Government data: electronic data Scholarly data

RESEARCH AREAS (EXAMPLES)

Semantic context Modern text mining approaches to

ancient texts E-linguistics Data management/digital archive Visualisation of dialectology Digital arts & humanities

WCU WEBOMETRICS INSTITUTE

ProjectInvestigating Internet-based Politics with E-Research Tools

Objects To identify, track & analyse the effectiveness of networked political

campaigns across a range of web platforms in Korea To develop software tool

Major areas Semantic Networks on Political Webosphere Cyworld Twitter Korean Internet Network Miner (tool)

Website

http://english-webometrics.yu.ac.kr/

NETWORK OF POLITICIANS IN TAIWAN

Case 1

BRIEF BACKGROUND INFORMATION

Progressive groups use more alternative media

Political struggle between two political camps Conservative: pro-unification with China (Leader:

KMT) Progressive: pro-independence (Leader: DPP)

Approx 80% of mainstream media is biasd toward the conservatives

Research targets: Politicians Plurk

Microblogging sites:a mini form of traditional blogs. Microblogging allows users to send brief messages

WHAT IS PLURK

A popular microblogging site in Taiwan No. of Taiwanese user - 249,441 (July 2009)

03/09

5K

10K 10K

25K

RESULTS & FINDINGS

•Progressive politicians use more Plurk• Progressives: 37 politicians• Conservative: 7 politicians

Age Karma profile viewsfriends invited

no. of plurksno. of plurk

responsesno. of friends no. of fans

ALL(N=44)

46.80 64.77 10719.70 12.34 261.52 2158.30 1783.75 580.05

Progressive(Np=37)

46.11 67.25 12017.59 13.59 291.78 2417.35 2065.68 650.46

Conservative(Nc=7)

50.43 51.71 3859.43 5.71 101.57 789.00 293.57 207.86

Question:

DOES AGE MATTER?

AGE:PROGRESSIVES VS CONSERVATIVES

Age Freq Freq% CumFreq CumFreq%

21-30 1 2.70 1 2.70

31-40 12 32.43 13 35.14

41-50 12 32.43 25 67.57

51-60 7 18.92 32 86.49

61-70 4 10.81 36 97.30

71-80 1 2.70 37 100.0

Sum 37 100.00

Age Freq Freq% CumFreq CumFreq%

21-30 0 0 0 0

31-40 0 0 0 0

41-50 4 57.14 4 57.14

51-60 3 42.86 7 100.00

61-70 0 0 7 100.00

71-80 0 0 7 100.0

Sum 7 100.00

Pearson Correlation

Gender Party Age Plurks

Gender 1.000 -0.329 0.204 0.005

Party -0.329 1.000 -0.156 0.214

Age 0.204 -0.156 1.000 -0.258

Plurks 0.005 0.214 -0.258 1.000

Progressive Politicians Conservatives Politicians

EXCEPTION: HSIEH & SU

Age Karma profile views friends invited no. of plurks no. of plurk responses no. of friends no. of fans

ALL(N=44)

46.80 64.77 10719.70 12.34 261.52 2158.30 1783.75 580.05

Hsieh 63 97.32 65462 100 506 7267 10869 3393

Su 62 96.41 60444 90 405 631 9853 3708

Hsieh

Su

Profile views

Number of friends

Number of fans

DISCUSSIONS

Hsieh & Su: Candidates for the presidential election 2008

Progressive politicians use Plurk Express their opinion Cultivate new constituencies Link to opinion leaders of Taiwanese grassroot

groups The ‘rich get richer’ phenomenon

Plurkers that are the target of many visitors are disproportionately likely to be targeted by any new visitors

Case 2

NETWORK OF MPS IN SOUTH KOREA

WEB 1.0, WEB 2.0 &TWITTER

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 KoreaYear of observations:

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

Blue: GNP: Conservative: Opposition

Red: MDP: Liberal: Ruling

WEB 1.0: HOMEPAGE 2000 VS 2001

Star networks without any isolation

WEB 2.0: BLOGS 2005 VS 2006

2005 2006

TWITTER

WEB 1.0, WEB 2.0 &TWITTER

Web Types YearSum of links

(Mean)

Dens-ity

CentralisationGini

Coeffi-cientIN OUT

Web 1.0(Homepage

)

2000N=24

5

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)

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

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

CONCLUSIONS

Previous research: Results from web data are informative The Web reflects situations in the physical

world The validity of the results need to be

supported by various qualitative studies Further research:

Previous study areas are still ongoing Cyber emotions: online image content

analysis of web 2.0 politics

THANK YOU

AcknowledgmentsWCU 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