This document analyzes the relationship between political blog posts and voter turnout in South Korean national assembly elections. The study monitored over 62,000 blog posts related to 29 candidates over a 12-day period. It found a positive correlation between the number of blog posts about a candidate and the number of votes received. A simple regression model indicated the number of blog posts was a significant predictor of votes. The results suggest political blogs can influence elections and real-time blog monitoring provides insights into socio-political issues and campaigns.
1. Monitoring a Socio-political Blogosphere in South Korea Comparing a Metrics from Blogosphere with Voter Turnout Yon Soo Lim & Han Woo Park WCU WebometricsInstitutute Yeungnam University E-mail: yonsoolim@gmail.com
2. Blog? Online personal diary Social media: Interactive communication Online community Communal contents Expansion of social networks/Diffusion and sharing of information
4. Political Blog Campaign International studies on political blog campaign 2004U.S. presidential election (Blog for America) Anstead & Chadwick, 2008; Drezner & Farrell, 2008; Kerbel & Bloom, 2005; Trammell, 2006; Trammell et al., 2006 2005U.K. general election Coleman & Ward, 2005; Jackson, 2007; Stanyer, 2006 2005New Zealand general election Hopkins & Matheson, 2005 2005Danish parliamentary election Klastrup & Pedersen, 2007 2005German bundestag election Albrecht, Lübcke, & Hartig-Perschke, 2007 2007Australian federal election Kirchhoff, Nicolai, Bruns, & Highfield, 2009 2008presidential and congressional election Metaxas & Mustafaraj, 2009; Smith, 2009 However, the blogosphere research on Korean political elections has been rarely conducted.
5. Research Objective This study aims to empirically examine the effectiveness of political blog campaign during the 2009 Korean National Assembly by-election periods.
6. Method Data Blog postings related to 29 candidates for the 2009 Korean 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)
7. 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 candidate D.V.: the number of votes
8. Descriptive Information Real-time blog monitoring: total 20 times Cumulative number of blogs:total62,672 The average number of blogs:108.06(SD=81.96, N=29) Highest:Kim, YW (M=280.1) Lowest:Yoon, JY (M=11.8)
9. Trend Analysis Jangan district in Suwon City, Gyeonggi Province (Park, CS) (Lee, CY) (Ahn, DS) (Yoon, JY)
10. Trend Analysis Sangrok-B district in Ansan City, Gyeonggi Province (Song, JS) (Kim, YH) (Jang, KW) (Kim, SK) (Yoon, MW) (Lee, YH) (Lim, JI)
11. Trend Analysis Gangreung district in Gangwon Province (Kwon, SD) (Hong, JK) (Song, YC) (Shim, KS)
13. Trend Analysis Yangsan district in South Gyungsang Province (Park, HT) (Song, IB) (Park, SH) (Kim, SG) (Kim, YS) (Kim, YK) (Kim, JM) (Yoo, JM)
14. Blogs vs. Votes Jangan district in Suwon City, Gyeonggi Province N. of Votes N. of Blogs (Park, CS) (Lee, CY) (Ahn, DS) (Yoon, JY) (Park, CS) (Lee, CY) (Ahn, DS) (Yoon, JY)
15. Sangrok-B district in Ansan City, Gyeonggi Province Blogs vs. Votes N. of Votes N. of Blogs (Jang, KW) (Song, JS) (Kim, YH) (Kim, SK) (Yoon, MW) (Lee, YH) (Lim, JI) (Jang, KW) (Song, JS) (Kim, YH) (Kim, SK) (Yoon, MW) (Lee, YH) (Lim, JI)
16. Blogs vs. Votes Gangreung district in Gangwon Province N. of Votes N. of Blogs (Kwon, SD) (Hong, JK) (Song, YC) (Shim, KS) (Kwon, SD) (Hong, JK) (Song, YC) (Shim, KS)
17. Blogs vs. Votes Jeungpyeong-Jincheon-Geoisan-Eumsung district in North Chungcheong Province N. 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)
18. Blogs vs. Votes Yangsan district in South Gyungsang Province N. 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)
19. 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)
20. 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 relationship between blog postings and votes. From the results of a simple regression analysis, the number of blogs by candidatescan be regarded as a significant determinant of the number of votes.
21. Future Research Consider the qualitative approaches for blog contents. 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 collection and analysis of massive blogosphere.
22. Implication This study empirically investigated the effectiveness 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.
