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Chien-leng Hsu (Post-doctoral research fellow)
Se Jung Park (PhD student)
Han Woo Park (Associate Professor)
Department of Media & Communication, WCU Webometrics Institute, Yeungnam University
[email protected] http://www.hanpark.net
http://english-webometrics.yu.ac.kr
Presented at the 5th Complexity Conference, 27 Nov 2010, Seoul, Korea
Identifying influential Twitter usersThe case of Sejong City in South Korea
About this study
This research explores influential Twitter users and Tweets by:
Using Sejong City ( 세종시 ) as the case study
Using different measurement methods
Studying topics (keywords) mentioned in the Tweets
The 2-step flow of communication theory (Katz & Lazarsfeld, 1964)
(Online) opinion leaders:determinants of rapid & sustained behavior change of
members in a community (Valent & Davis, 1999)Park, Jeong & Han (2008)
practices (political) participation deligentlyaggressively expresses opinions
Key Users on Twitter Communities
The “network structure” approach: fails to identify influential Twitter users (Leavitt et al, 2009;
Haddadi et al, 2010) The “actor relation” approach:
content reachabilityFisher & Gilbert (2009) replies, retweets, mentions & attributions➭
The majority of users: silent & passivea user’s influence: information forwarding activity (Romero et al, in
submission)Trace influential user over time
Identifying Key Twitter Users
The Sejong City Project
The original plan (Moo-Hyun Roh in 2005):To allocate 2/3 of government offices to Sejong, Chungnam (
충남 )Necessary for regional developmentThe excessive centralization of Seoul & its vicinity
limited innovation potential (Shapiro, So & Park, 2010)➭
The revised plan (Myung-Bak Lee):A center for education, scientific research & high-tech industriesPartitioning the capital would weaken Korea’s competitiveness &
innovation capability
Research Questions
Who are the influential users who produce Tweets related to the Sejong City project?
What are activities of the influential users?
What is the relationship between the influential users?
What are the keywords frequently used by the influential users in the Sejong City issue network?
Data collection & analytical techniques
Data collection
Analytical techniques
Dates of collection: 15 March ~ 12 April 2010Twitter scraper: An automated computer program to retrieve Tweets
from Twtkr (twitterkr.com)Twitter API: Twitter user’s public data
Basic data:LocationNumber of TweetsLists of followingsLists of followers
Pearson correlation test
Four posting activities:Normal tweetsBeing retweeted by othersBeing replied by othersBeing mentioned by others
Krkwic (keywords analysis)
(I) Identification of influential users
(II) Twitter activities of the influential users
(III) Relationships between the influential users
(IV) Keywords in the issue network of Sejong City
Amendment of Sejong City law & politicians
Critical reviews on Sejong City law
Controversies & solutions
Agreement & social welfare
Other social & political issues
Conflicts between political partiesPolitical ideologies & concerns on national debtNational policies
Influential users include media outlets & ordinary users
Correlation tests:The occurrence of Tweets vs. the number of Tweets
significantly correlated (Pearson correlation=0.663, p<.01)➭ ➭ Influential users tended to address public issues
The number of followers vs. the number of followings significantly correlated (Pearson correlation=0.871, p<.01)➭ ➭ Influential users had mutual ties in the network
Discussions (I)
Having mutual relations with other influential users may allow an influetial user to make his/her own opinions available to a wider audience
Influential users are likely to act as news brokers & deliver their views in a single-issue community
Discussions (II)
Referral activities/relationshipsMedia outlets normal tweets messages were not circulated well ➭ ➭
among other usersOrdinary users
normal tweets, retweets, mentions & replies More likely to interact with the indirect presence of media outlets
Keyword networkPoliticians, government projects & social-political issues mentionedInfluential user
some keywords specific to his/her clustersimilar keywords used a sense of community ➭
Thank you.