WEBOMETRICSINSTITUTE

Monitoring a Socio-political Blogosphere in South Korea

Comparing a Metrics from Blogosphere with Voter Turnout

Yon Soo Lim & Han Woo ParkWCU Webometrics Institutute

Yeungnam UniversityE-mail: yonsoolim@gmail.com

Blog? Online personal diary Social media:

Interactive communication Online community Communal contents Expansion of social networks / Diffusion and sharing of in-

formation

Political Blog Campaign

Civic Political Engagement

Appeal Political Beliefs and Sup-ports

Construct Online Communication Network Between Politicians and

Citizens

Political Blog Campaign International studies on political blog campaign

2004 U.S. presidential election (Blog for America) Anstead & Chadwick, 2008; Drezner & Farrell, 2008; Kerbel & Bloom, 2005;

Trammell, 2006; Trammell et al., 2006 2005 U.K. general election

Coleman & Ward, 2005; Jackson, 2007; Stanyer, 2006 2005 New Zealand general election

Hopkins & Matheson, 2005 2005 Danish parliamentary election

Klastrup & Pedersen, 2007 2005 German bundestag election

Albrecht, Lübcke, & Hartig-Perschke, 2007 2007 Australian federal election

Kirchhoff, Nicolai, Bruns, & Highfield, 2009 2008 presidential and congressional election

Metaxas & Mustafaraj, 2009; Smith, 2009

However, the blogosphere research on Korean political elections has been rarely conducted.

Research Objective

This study aims to empirically examine the ef-

fectiveness of political blog campaign during

the 2009 Korean National Assembly by-elec-

tion periods.

Method Data

Blog postings related to 29 candidates for the 2009 Ko-rean National Assembly by-election.

Data gathering Korean-language based blog search engine by Naver.-

com Real-time blog monitoring program by WWI Search queries: the name of candidate + “candidate” Search date: After Oct. 8, 2009 Data collection periods: Oct. 16 – Oct. 27, 2009 (12

days) Cycle: Twice per a day (AM 00:00, PM 12:00)

Analysis Trend analysis

Tracing change over time

Correlation analysis (Pearson & Spearman) Regarding 29 candidacies,

the average number of blogs per candidate & the number of votes

Simple regression analysis I.V.: the average number of blogs in each candi-

date D.V.: the number of votes

Descriptive Information Real-time blog monitoring: total 20 times

Cumulative number of blogs: total 62,672

The average number of blogs: 108.06 (SD=81.96, N=29) Highest: Kim, YW (M=280.1) Lowest: Yoon, JY (M=11.8)

Trend Analysis Jangan district in Suwon City, Gyeonggi

Province(Park, CS)(Lee, CY)

(Ahn, DS)(Yoon, JY)

Trend Analysis Sangrok-B district in Ansan City, Gyeonggi

Province(Song, JS)(Kim, YH)(Jang, KW)(Kim, SK)(Yoon, MW)(Lee, YH)(Lim, JI)

Trend Analysis Gangreung district in Gangwon Province

(Kwon, SD)(Hong, JK)(Song, YC)(Shim, KS)

Trend Analysis Jeungpyeong-Jincheon-Geoisan-Eumsung

district in North Chungcheong Province(Kyoung, DS)(Chung, BG)(Chung, WH)(Park, KS)

(Lee, TH)(Kim, KH)

Trend Analysis Yangsan district in South Gyungsang Prov-

ince(Park, HT)(Song, IB)(Park, SH)(Kim, SG)(Kim, YS)(Kim, YK)(Kim, JM)(Yoo, JM)

박찬숙 이찬열 안동섭 윤준영

33,106

38,187

5,570

716

Blogs vs. Votes Jangan district in Suwon City, Gyeonggi

ProvinceN. of Votes

N. of Blogs

(Park, CS)(Lee, CY)(Ahn, DS)(Yoon, JY)

(Park, CS)(Lee, CY) (Ahn, DS)(Yoon, JY)

Sangrok-B district in Ansan City, Gyeonggi Province

송진섭 김영환 장경우 김석균 윤문원 이영호 임종인

11,420

14,176

1,145 896 439 987

5,363

Blogs vs. Votes

(Song, JS)

(Kim, YH)

(Jang, KW)

(Kim, SK)

(Yoon, MW)

(Lee, YH)

(Lim, JI)

(Song, JS)

(Kim, YH)

(Jang, KW)

(Kim, SK)

(Yoon, MW)

(Lee, YH)

(Lim, JI)

N. of Votes

N. of Blogs

Blogs vs. Votes Gangreung district in Gangwon Province

권성동 홍재경 송영철 심기섭

29,010

2,100

19,867

6,054

N. of Votes

N. of Blogs

(Kwon, SD)(Hong, JK)(Song, YC)

(Shim, KS)

(Kwon, SD)(Hong, JK)(Song, YC)

(Shim, KS)

경대수 정범구 정원헌 박기수 이태희 김경회

19,427

29,120

3,071 2,125504

14,218

Blogs vs. Votes Jeungpyeong-Jincheon-Geoisan-Eumsung

district in North Chungcheong ProvinceN. of Votes

N. of Blogs

(Kyoung, DS)

(Chung, BG)

(Chung, WH)

(Park, KS)

(Lee, TH)

(Kim, KH)

(Kyoung, DS)

(Chung, BG)

(Chung, WH)

(Park, KS)

(Lee, TH)

(Kim, KH)

박희태 송인배 박승흡 김상걸 김양수 김용구 김진명 유재명

16,59715,577

1,550900

5,875

234 325

2,710

Blogs vs. Votes Yangsan district in South Gyungsang Prov-

inceN. of Votes

N. of Blogs

(Park, HT)

(Song, IB)

(Park, SH)

(Kim, SG)

(Kim, YS)

(Kim, YK)

(Kim, JM)

(Yoo, JM)

(Park, HT)

(Song, IB)

(Park, SH)

(Kim, SG)

(Kim, YS)

(Kim, YK)

(Kim, JM)

(Yoo, JM)

Results Correlation Analysis (N. of Blogs & N. of Votes)

Pearson r = .586, p < .01 (N=29) Spearman rho = .797, p < .01 (N=29)

Simple Regression Analysis N. of Votes = 1,055.56 + 79.99(N. of Blogs) R2 = .344 (F = 14.128, p < .01) ß = .586 (t = 3.759, p < .01)

Summary Overall, the number of blogs by candidates has a

tendency to increase over time.

By districts, the candidate who has the largest blog postings won the election.

The results of correlation analyses (Pearson and Spearman) significantly indicate the positive relation-ship between blog postings and votes.

From the results of a simple regression analysis, the number of blogs by candidates can be regarded as a significant determinant of the number of votes.

Future Research

Consider the qualitative approaches for blog con-

tents.

Develop a more sophisticated model for prediction

and analysis, considering various variables (socio-

demographic and off-line campaign activities).

Require advanced e-research tools for data collec-

tion and analysis of massive blogosphere.

Implication

This study empirically investigated the effec-

tiveness of political blog campaign, based on

a case study of South Korea.

Real-time online monitoring can be applied for

tracing and analyzing various socio-political

issues.

This study suggests a possibility for predictive

modeling related to blog marketing.

Thank you for your attention.