Presented at the SNS: The 21s century’s new way of communication, 4 Nov 2010, SNUT, Korea
Mapping online social networks
a...
About this study
This research intends to study communication relations
of the Korean politicians in the 18th National Ass...
 The relations between political parties & citizens (esp. the
young generation) ➭ weakening
 Images & the profile of pol...
 Small & less-established parties benefit from the Internet &
other new technologies (Römmele, 2003),
 Internet technolo...
 Individuals & groups have an online presence easily
 Two-way communication improved
 UK ➭ Websites useful for back-ben...
 Many focus on its capacity of information dissemination
 important role in information diffusion (Yang & Counts, 2010)
...
 Individual’s behavior is related to the larger web of social
connection he/she fits into and affected by the types of
re...
Research Questions
 What are the structure of the homepage, blog and Twitter
networks like?
 Are there any similarities ...
Data collection
 Date of collection – from February to April 2010
 Homepage – LexiURL searcher to retrieve data from the...
Analysis Techniques
 Basic concepts of Social Network Analysis (SNA)
 Density – how network members connect with each ot...
Homepage Network
Blue: GNP
Yellow: MDP
Purple: Independent
Green: DLP
Grey: LDP
Red: NPP
Pink: FHA
Blog Network
Blue: GNP
Yellow: MDP
Purple: Independent
Twitter Network
Blue: GNP
Yellow: MDP
Green: DLP
Red: NPP
Pink: FHA
Purple: Independent
Basic network information
No. of nodes
(isolators
excluded)
No. of links
(Mean)
Density
Centralization
In Out
Homepage
(N=...
Party
No. of 18th
members
Homepage (%) Blog (%) Twitter (%)
무소속
(Independent)
8 7 87.50 6 75.00 3 37.50
민주노동당
(Democratic ...
Discussion (I) – Network Structure
 Homepage: the network was sparse
 some nodes (politicians) had higher degree central...
 The networks followed the Power-law theory
➭ the higher number of hyperlinks a politician attracts, the
more links he/sh...
 Top politicians based on degree centrality
 4 of the top politicians on Blog were affiliated with GNP
➭ reflect on GNP’...
 Homepage: Links were mainly created by GNP
 65 links (50% of total links in the network) among GNP politicians
 GNP to...
Twitter:
Mutual Relations with Citizens & Politicians
 Direct messages are important for political dialogue
 On Twitter,...
 A simple reference of popularity on Twitter: (3 indicators)
 Number of followings
 Number of followers
 Number of pos...
Discussion (IV)
 The number of followers, followings and tweets were
correlated in 2009 and 2010, however,
 The number o...
Our findings support some of the existing literatures on politics
& the Internet
 Offline & online social networks are re...
 195 politicians (Assembly members & several important people)
 Data collected between 29-30 Oct 2010 from Twitter
 Ind...
 Geun-Hye Park: rank 20th (Indegree centrality: 63)
Final Remarks (cont.)
Follower 50
 Geun-Hye Park: rank 139th (Outdegree centrality: 8)
Final Remarks (cont.)
Following 50
Thank you.
Mapping online social networks among Korean politicians: Homepage, blog, and twitter
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Mapping online social networks among Korean politicians: Homepage, blog, and twitter

  1. 1. Presented at the SNS: The 21s century’s new way of communication, 4 Nov 2010, SNUT, Korea Mapping online social networks among Korean politicians Chien-Leng Hsu (Post-Doctorate Research Fellow) Han Woo Park (Associate Professor) Department of Media & Communication, WCU Webometrics Institute, YeungNam University hanpark@ynu.ac.kr http://www.hanpark.net http://english-webometrics.yu.ac.kr
  2. 2. About this study This research intends to study communication relations of the Korean politicians in the 18th National Assembly  Any significant differences of online connections between the 18th National Assembly members?  Inter-party & intra-party communication  Twitter as a deliberative tool?
  3. 3.  The relations between political parties & citizens (esp. the young generation) ➭ weakening  Images & the profile of political parties can be positively constructed through the media, e.g. mass media & the Internet  Web 2.0 ➭ interactive communication & content creation tools (Herold, 2009)  Impact on different areas ➭ opinion formation, interest mediation, party organization etc  Primary goals of political parties (Römmele, 2003)  Social & political context determines the magnitude of the Internet’s impact (Tkach-Kawasaki, 2003) Politics & the Internet
  4. 4.  Small & less-established parties benefit from the Internet & other new technologies (Römmele, 2003),  Internet technologies are good for the major parties (Conway & Dorner, 2004)  Online resources mainly reach citizens who are already most likely to be politically active, interested & engaged (Norris, 2003)  The Internet strengthens the existing network ties (Pickerill, 2004) & empowered a small, particular group (Lin & Dutton, 2003)  New Media & Fat Democracy (Davis, 2010)  A further distancing of the less political engaged citizens from the political center  New media might fail to engage ordinary citizens Politics & the Internet (cont.)
  5. 5.  Individuals & groups have an online presence easily  Two-way communication improved  UK ➭ Websites useful for back-bench MPs (members of the Parliament) to participate in party-wide policy processes & discussions  Problems  UK (in 2005) ➭ 3% looked at party web sites ➭ 3.3% of the population used the Internet as their main source of political information (Davis, 2010)  Canada ➭ very few interactive features implemented by parties in their campaign websites (Small, 2008)  The general public mainly seek to be minimally informed of politics & rely on the traditional mass media (Lusoli et al, 2006) The Internet: a deliberative tool?
  6. 6.  Many focus on its capacity of information dissemination  important role in information diffusion (Yang & Counts, 2010)  Twitter as a mobilization tool in the Middle East (Pavel, 2009)  Twitter was used extensively for political deliberation during the parliament election in Germany & reflected the election outcome (Tumasjan et al, 2010)  Dynamic debates (Yardi & Boyd, 2010)  Although users were exposed to different views, meaningful discussions were few  Clear boundaries between ingroup & outgroup affiliation Twitter
  7. 7.  Individual’s behavior is related to the larger web of social connection he/she fits into and affected by the types of relations he/she has with others in the network (Freeman, 2008; Haythornthwaite, 1999)  Through identifying the structure of a network, how information is created & shared (or blocked) can be understand  Online networks are isomorphic to traditional/offline social networks  Hyperlinks can be seen as mediators of a wide range of associative relations between producers of web materials (Foot, Schneider, Dougherty, Xenos, & Larsen, 2003)  A link can mean an embracement, a recognition… etc Online Social Network
  8. 8. Research Questions  What are the structure of the homepage, blog and Twitter networks like?  Are there any similarities or differences in terms of network linking patterns from homepage, blog to Twitter?  Do offline relations influence the online sturcture?  What are online inter-party & intra-party communication like?  What is the communication between politicians & the general public like?
  9. 9. Data collection  Date of collection – from February to April 2010  Homepage – LexiURL searcher to retrieve data from the Yahoo! database  Blog – Manually collected by visiting Assembly members’ blog page  Twitter – An automated computer program using Twitter’s API (Application Programming Interface) to retrieve data from Twitter  Analysis & Visualization – UciNet
  10. 10. Analysis Techniques  Basic concepts of Social Network Analysis (SNA)  Density – how network members connect with each other  Degree centrality/centralization  The more neighbors one has, the more central he/she is in a network  To identify actors more central than others in a network  Degree of centralization ➭ the extent to which the whole network is grouped around the central point(s)
  11. 11. Homepage Network Blue: GNP Yellow: MDP Purple: Independent Green: DLP Grey: LDP Red: NPP Pink: FHA
  12. 12. Blog Network Blue: GNP Yellow: MDP Purple: Independent
  13. 13. Twitter Network Blue: GNP Yellow: MDP Green: DLP Red: NPP Pink: FHA Purple: Independent
  14. 14. Basic network information No. of nodes (isolators excluded) No. of links (Mean) Density Centralization In Out Homepage (N=281) 115 (40.92%) 130 (0.46) 0.0017 2.34% 9.15% Blog (N=173) 71 (41.01%) 149 (0.86) 0.005 5.34% 12.95% Twitter (N=72) 35 (48.61%) 983 (13.65) 0.1923 57.63% 54.77%
  15. 15. Party No. of 18th members Homepage (%) Blog (%) Twitter (%) 무소속 (Independent) 8 7 87.50 6 75.00 3 37.50 민주노동당 (Democratic Liberal Party, DLP) 5 4 80.00 4 80.00 4 80.00 한나라당 (Grand National Party, GNP) 169 160 94.67 102 60.36 26 15.38 민주당 (Merged Democratic Party, MDP) 87 85 97.70 53 60.92 32 36.78 자유선진당 (Liberty Forward Party, LFP) 18 17 94.44 5 27.78 2 11.11 진보신당 (New Progressive Party, NPP) 1 1 100.00 1 100.00 1 100.00 창조한국당 (Creative Korea Party, CKP) 2 2 100.00 2 100.00 2 100.00 친박연대 (Future Hope Alliance, FHA) 8 5 62.50 0 0.00 2 25.00 Sum 289 281 97.23 173 59.86 72 24.91 Overall Use of Homepage, Blog & Twitter – by party
  16. 16. Discussion (I) – Network Structure  Homepage: the network was sparse  some nodes (politicians) had higher degree centrality & acted as communication points for other politicians.  Blog: clear boundaries were found between political parties  Higher outdegree centralization (3x indegree centrality) ➭ certain blogs attracted less links from other politicians in the network, but they sent more links to others.  Twitter: the densest among the three networks  More connections between politicians of different parties.  Liberal parties used more alternative media than conservatives  GNP was more connected in homepage & blog network  MDP was more connected on Twitter
  17. 17.  The networks followed the Power-law theory ➭ the higher number of hyperlinks a politician attracts, the more links he/she would attract in the future ➭ “the rich gets richer” phenomena  Distance between any pair of politicians  Politicians were closers in Twitter (distance = 1.72) than in hompage (2.02) & blog (1.85)  Distance-based cohesion: Twitter was the most cohesive  Distance-weighted fragmentation: Twitter was least fragmented Discussion (II) – Network Structure
  18. 18.  Top politicians based on degree centrality  4 of the top politicians on Blog were affiliated with GNP ➭ reflect on GNP’s media strategy: every politician would have an online presence in the blog sphere  Liberal politicians occupied the top positions on Twitter, except GNP’s Woo-Yeo Hwang ➭ a larger offline social network ➭ had cultivated a large amount of social capitals (4 terms)  Geun-Hye Park ➭ the only female member among the central members ➭ leads a faction of 40-odd lawmakers & an intra-party rival for the current president Myung-Bak Lee ➭ part of her popularity gained from people who respect for her father – President Chung-Hee Park (1960s/1970s) Discussion (III) – Offline vs Online
  19. 19.  Homepage: Links were mainly created by GNP  65 links (50% of total links in the network) among GNP politicians  GNP to MDP (17 links); within MDP (8 links); MDP to GNP (4)  Blog:  the majority of links (127 of 149 links) within GNP  No links between different parties, except from GNP to Independent  Twitter: more links between different parties  MDP created more links (551 of 983 links) than any other parties  MDP received more links (546 of 983 links) than any other parties  Politicians still created more links to others of his/her own parties  413 (74.95%) of 551 links were created within MDP itself  193 (67.25%) of 287 links were created within GNP itself Inter-Party & Intra-Party Communication
  20. 20. Twitter: Mutual Relations with Citizens & Politicians  Direct messages are important for political dialogue  On Twitter, mutual ties is prerequisite for direct communication (sending direct messages)  The rate of mutual ties a politician has with others  an indicator ➭ measuring the degree of political deliberation  an indicator ➭ Twitter as a political platform  an indicator ➭ how interactive a politician/political party  Nearly all political parties had more mutual relations with politicians than with citizens ➭ the communication among politicians was stronger
  21. 21.  A simple reference of popularity on Twitter: (3 indicators)  Number of followings  Number of followers  Number of postings/tweets (activeness)  Correlation tests: Pearson Correlation & Spearman Correlation Coefficient  20 politicians who had a Twitter profile in both 2009 & 2010  Results: 3 indicators are significantly correlated  Examination of indegree centrality of individual politicians: higher 2009 indegree centrality ≠ higher 2010 indegree centrality  Limitations of this finding ➭ Small sample Twitter: Popularity
  22. 22. Discussion (IV)  The number of followers, followings and tweets were correlated in 2009 and 2010, however,  The number of followers does not grow over time:  Politicians need to be active to attract more audience (e.g. keep posting, initiate discussion topics citizens concern )  Not all politicians used Twitter limited our findings
  23. 23. Our findings support some of the existing literatures on politics & the Internet  Offline & online social networks are related  Liberal groups exploit new media more than conservatives  Media strategies of political parties affect online political behaviour  New technologies could contribute to parties of any kind  Different social, cultural & political etc context  Different goals ➭ Different strategic uses of technologies  Analysis on party communication  Communication in the political center ➭ strengthened  Are citizens increasingly excluded from the political processes?  Is Twitter not as deliberative as some scholars have argued? Final Remarks
  24. 24.  195 politicians (Assembly members & several important people)  Data collected between 29-30 Oct 2010 from Twitter  Indegree centralization: 33.29% Outdegree centralization: 74.73 Final Remarks (cont.) Followers Followings
  25. 25.  Geun-Hye Park: rank 20th (Indegree centrality: 63) Final Remarks (cont.) Follower 50
  26. 26.  Geun-Hye Park: rank 139th (Outdegree centrality: 8) Final Remarks (cont.) Following 50
  27. 27. Thank you.

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