Measuring Twitter-Based Political Participation and Deliberation in the South Korean Context by Using

Social Network and Triple Helix Indicators

Authors:

Minjeong Kim & Han Woo Park

Affiliations and Contact Information:

Minjeong Kim is Assistant Professor in the Department of Journalism and Technical

Communication at Colorado State University, USA. Her mailing address is 1785 Campus Delivery,

Colorado State University, Fort Collins, Colorado, 80523, U.S.A. She can be reached at

Minjeong.Kim@colostate.edu;

Han Woo Park (corresponding author) is Associate Professor in the Department of Media &

Communication at Yeungnam University, South Korea. He is also the director of the World Class

University (WCU) Webometrics Institute and CyberEmotions Research Center at Yeungnam University.

His mailing address is Dept of Media & Communication, YeungNam University,  214-1, Dae-dong,

Gyeongsan-si, Gyeongsangbuk-do, South Korea, Zip Code 712-749 . He can be reached at

hanpark@ynu.ac.kr, and his research is available at http://www.hanpark.net. His phone number is 82-53-

810-2275. His fax number is 82-53-810-2270.

Kim, M.J., & Park, H. W. (2012). Measuring Twitter-Based Political Participation

and Deliberation in the South Korean Context by Using Social Network and Triple

Helix Indicators. Scientometrics. 90 (1), 121-140.http://link.springer.com/article/10.1007%2Fs11192-011-0508-5#page-1

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Abstract

This study investigates the role of Twitter in political deliberation and participation by analyzing the ways

in which South Korean politicians use Twitter. In addition, the study examines the rise of Twitter as user-

generated communication system for political participation and deliberation by using the Triple Helix

indicators. For this, we considered five prominent politicians, each belonging to one of four political

parties, by using data collected in June 2010. The results suggest that non-mainstream, resource-deficient

politicians are more likely to take advantage of Twitter`s potential as an alternative means of political

participation and that a small number of Twitter users lead political discourse in the Twittersphere. We also

examined the occurrence and co-occurrence of politicians’ names in Twitter posts, and then calculate

entropy values for trilateral relationships. The results suggest that the level of political deliberation,

expressed in terms of the level of balance in the communication system, is higher when politicians with

different political orientations form the trilateral relationships.

Keyword: Twitter, Triple Helix, Politician, Korea, Polarization

MSC code: 94A02 [“Communication, Information” & research exposition]

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1. Introduction

Recent political events, including Barack Obama’s historical win in the 2008 presidential election in the U.

S, have demonstrated the increased use and importance of social media in political campaigns. Twitter, a

social networking and microblogging service, is the newest addition to a series of new communication

methods— websites in 1996, online fundraising in 2000, and blogs in 2004 (Tumasjan et al. 2010)—that

have helped politicians at both ends of the ideological spectrum to succeed in their campaigns.

Although the potential of digital media including Twitter as a tool for civic engagement has been praised

(e.g., Khan et al. 2011; Levi 2008; Ladhani 2010), it is less clear whether its potential has been realized

beyond anecdotal evidence. A few statistical snapshots of U.S. politicians’ Twitter use are illustrative. The

Los Angeles Times reported in January 2009 that only 13% of U.S. congressmen used Twitter (“Tweet

Congress” 2009). In addition, a study of the content of U.S. congressmen’s Tweets in June and August

2009 suggested that public figures use Twitter mainly for “self-promotion,” disseminating information

about themselves (Golbeck et al. 2009, p. 1). That is, direct communication between congressmen and

citizens tends to be less popular.

This study takes in the same debate but explores the role of Twitter in political deliberation and

participation by examining Twitter use among five prominent South Korean (hereafter “Korean”)

politicians and mapping Twitter networks of these politicians. Mapping-oriented visualization, together

with traditional narratives and numbers, should facilitate hermeneutic accessibility and engagement.

(Savage and Burrows 2007).

In addition, this study examines how the political communication system can be reshaped as a digitalized

network and how, within these complex social systems, the Twitter-based overlap between politicians can

be measured through TH indicators.

2. Literature Review

In their study of U.S. political blogs, Benkler and Shaw (2010) argued that the adoption and use of various

technologies can have different effects on democracy. One type of technology, for instance, may polarize

political discourse but may also enable a political actor to form a sufficient level of coherence around an

issue (Benkler and Shaw 2010). This suggests that a researcher studying the effect of technology on

democracy should clearly identify what aspect of democracy he or she intends to address. For instance, the

question of who has the opportunity to be heard at all is a question of political participation, whereas the

question of whether political discourse is polarized is a question of political deliberation (Benkler and Shaw

2010). Thus, we provide a review of previous studies based on the type of political effect they addressed.

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2.1. Political Participation

There are two important aspects of Twitter’s potential as a means of political participation. The first aspect

considers Twitter as a direct communication channel between politicians and citizens. The second one

views Twitter as an alternative means of political communication and mobilization.

First, Twitter can provide a platform for the public to voice its concerns directly and immediately to their

representatives (Ladhani 2010). Are politicians actually directly connected to their constituents? A few

studies have suggested that they are not. Kwak et al. (2010) provided the interesting in-depth quantitative

analysis of Twitter-based communication by crawling a big part of Twittersphere —41.7 million user

profiles, 1.47 billion social relations, and 106 million Tweets— in July 2009, Twitter differs from social

networking sites such as Facebook in terms of their reciprocity. This indicates that Twitter users do not

necessarily follow other Twitter users who follow them, that is, : the relationship between following and

being followed is not necessarily a two-way street on Twitter. Only 22.1% of Twitter users had a reciprocal

relationship, whereas 68% of Flickr users and 84% of Yahoo! 360° users had such a relationship (Kwak et

al. 2010). This finding is intriguing because Twitter’s low degree of reciprocity suggests that Twitter is not

being used as a primary interpersonal communication tool, although it was conceived as “a service that uses

SMS1 to tell small groups what you are doing” (Sagolla 2009). In other words, Twitter has become

something more than a tool for sending constant updates to a small circle of people with whom a Twitter

user (also known as a “Twitterian”) desires to share intimate details on his or her trivial daily activities; it

has become a tool of mass communication,2 reaching a large number of random audiences who choose to

follow a Twitter user with neither an interpersonal familiarity nor a reciprocal connection between the

follower and the followed. Moreover, as noted earlier, Golbeck, Grimes, and Rogers (2009) found that U.S.

congresspeople are less likely to use Twitter for communicating directly with the public.

Second, Twitter can be an alternative means of political communication and mobilization. Whereas some

have praised Twitter for facilitating political protests in oppressive regimes such as Iran and Moldova (e.g.,

Mungiu-Pippidi and Munteanu 2009; Shirky 2009), others have been skeptical of the so-called “Twitter

Revolution.” For one, Morozov (2009) criticized the view of “cyber-utopian Western commentators” (p.

11) and noted that the most frequent users of Twitter in Iran are “pro-Western, technology-friendly and

iPod-carrying young people” whose Twitter activities “may have little relevance to the rest of the country,

1 SMS: short message service. 2 Indeed, Twitter has been proven to be effective in spreading breaking news. For example, it spread the news about the 2008 Chinese earthquake quicker than any official news channels (Bradshaw, 2008). In addition, traditional news organizations, including the New York Times, CNN, and NPR, have been using Twitter to post breaking news alerts and provide updates on sports, business, and traffic (Tenore, 2007).

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including the masses marching in the streets” (p. 12). Similarly, Last (2009) denounced the media hype

about the role Twitter of Moldova and Iran.

Twitter—like the Internet in its earlier years—can serve as a vital communication means for politicians

who lack offline resources such as political funds and personnel. A group of U.K. researchers found that

Labor MPs (Members of Parliament) and Liberal Democrat MPs were more likely to use Twitter than

incumbent Conservative MPs (Williamson et al. 2010). This finding is different from that for U.S.

congressmen. According to the statistics available at TweetCongress.org as of August 2010, 127

Republicans, 103 Democrats, and two Independents used Twitter.

2.2. Political Deliberation

In addition to Twitter’s role in political participation, some scholars have noted the potential of Twitter as a

communication medium for political deliberation. Investigating whether Twitter can be a platform for

online political deliberation, four German researchers examined 104,003 political Tweets published in 2009

prior to the German national election and found that Tweets, despite their brevity, contained relevant and

substantive information but that only 4% of all users accounted for more than 40% of all messages

(Tumasjan et al. 2010). Thus, they concluded that Twitter is in fact a forum for political deliberation,

facilitating political discussion among users, but noted that this forum is dominated by a small number of

heavy users. In addition, another German study3 explored the relationship among 577 political Twitter

accounts (official Twitter accounts of parties and politicians in Germany) and found “significant overlaps

among followers of the Green party and Die Linke” but fewer overlaps among “the users of the two leftist

parties SPD and Die Linke.” (Tumasjan et. al. 2010, p. 3).

In another recent study of structural changes in online networks from Web 1.0 to Web 2.0, Park and Hsu

(2010) found that online social ties among Korean politicians on Web 2.0 applications, including Twitter,

tend to be fragmented, forming butterfly networks based on political homophily. In a similar vein,

Holmberg and Thelwall (2009) have also found that local governmental websites in Finland were clustered

based on geographical proximity as well as administrative cooperation. This geopolitical assortment

among governmental sites was further confirmed in terms of contents and services (Holmberg, 2010).

3 This German study is written and published in German, and thus, we rely on Tumasjan et al. (2010, p. 3) for the study’s findings and quotes. The full citation for this German study is as follows: Meckel, M., and Stanoevska-Slabeva K. 2009. AuchZwitschern muss man uben - Wie Politiker im deutschenBundestagswahlkampf twitterten. Retrieved December15 from: http://www.nzz.ch/nachrichten/kultur/medien/auch zwitschern muss man ueben 1.3994226.html.

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The following two findings of these studies are instructive for our examination of political deliberation via

Twitter. First, Twitter is a political forum dominated by a small number of heavy users. Second, there is

polarization in the Twittersphere. Other researchers have noted similar trends in their study of political

discourse in the blogosphere. In terms of the early finding, Hindman (2009) noted that blogs are written

mainly by elite white men such as op-ed columnists for leading newspapers. Sunstein (2007) warned

against polarization and fragmentation in online communication and their negative impact on political

deliberation. Verifying Sunstein’s argument through empirical data, Adamic and Glance (2005) found that

both liberal and conservative U.S. political bloggers tend to be linked primarily within their separate

communities.

2.3. Triple Helix Model

According to Leydesdorff (2006, p. 43), the TH model defines the primary institutions in knowledge-based

societies universities, academia, and governments. Each institution has different functions: novelty

generation by the academic community, wealth generation by industries, and regulation by nation-states.

Within a national political system, politicians may influence the interaction among these three institutional

actors by proposing various policy initiatives. Given the importance of politicians in any society, social

network researchers have documented how communication networks between politicians and those

between politicians and citizens constrain the each other’s behavior and shape internet-mediated innovation

systems (Park, Kim & Barnett, 2004; Park & Jankowski, 2008; Park & Kluver, 2009; Park & Thelwall,

2008; Park.2011; Park et al. 2011).In other words, previous studies have demonstrated that the interaction

between the communication structure represented by the Internet (e.g., homepages, blogs, and Twitter) and

the relationship can provide society with a new selection environment for innovation. However, few studies

have examined the development, measurement, and self-organization of political communication within the

TH framework.

In this regard, the present study applies the concept and method of the “Triple Helix overlay” of

negotiations and exchange relations on existing socio-ideological divisions between congressional

members to political communication. Congressional members in Korea are regarded as national political

agencies in that they are acknowledged as a constitutional institution. The TH model has typically been

used in science and technology studies for measuring the knowledge infrastructure of the university-

industry-government relationship. However, the TH thesis needs to be applied to various complex social

(e.g., new public-private interfaces that encourage knowledge-based innovation in digital societies). The

overlay of exchange relations and negotiations among these institutional actors ( in this case, politicians

and general citizens) has become increasingly important for understanding the dynamics of the overall

system (Leydesdorff & Etzkowitz, 2002).

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3. Research Questions

The present study addresses the following research questions. First, in terms of political participation, this

study determines whether and how frequently Twitter is used as a direct communication channel between

politicians and their followers by examining the degree of their reciprocity. In addition, the study examines

Twitter’s potential as an alternative means of political communication. Specifically, the study examines

whether non-mainstream, resource-deficient politicians are more likely to use Twitter and demonstrate a

higher degree of reciprocity than their mainstream counterparts.

In terms of political deliberation, this study explores whether and to what extent Twitter is a political forum

dominated by a small number of heavy users and whether and to what extent Twitter networks are

polarized according to the political orientation of users.

In terms of the application of the TH model, this study measures the Twitter-based overlap between

politicians. Specifically, we use the TH indicators developed by Leydesdorff (2003), who based the

indicators on Shannon’s information theory (Shannon, 1948; Shannon & Weaver, 1949). These TH

indicators have been employed in various Korean contexts (Khan & Park, 2011; Park, Hong &

Leydesdorff, 2005; Park & Leydesdorff, 2010).

In addressing the research questions, this study examines Korean politicians’ Twitter use. Korea makes “an

interesting and important study for global comparison” in that it (1) is one of the most highly connected

countries in the world and (2) has fully embraced the Internet in the realm of politics (Lee and Park 2010,

p. 30). In addition, an examination of Korean politicians’ Twitter use should contribute to the literature by

providing a better understanding of Twitter use in Asian societies, which few studies have considered

(Herold 2009).

4. Methodology

4.1. Five Politicians

We selected five prominent Korean politicians for our sample. This is an exploratory study, and thus, the

findings are not meant to be generalizable to the general population of Korean politicians. As such, we

chose five politicians with diverse backgrounds to maximize the exploratory nature of this study.

Each of the five politicians belonged to one of four political parties: the Grand National Party (GNP), the

Democratic Party (DP), the Democratic Labor Party (DLP), and the New Progress Party (NPP). The GNP,

the conservative ruling party, occupied 170 seats out of a total of 299 seats in the Korean National

Assembly as of September 2010. The DP, a moderate reform party with liberal views on some issues, had

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the second largest number of seats (87 seats as of September 2010). Both the DLP and the NPP are

progressive (left-wing) parties. As of September 2010, the DLP had 5 seats, and the NPP, only 1 seat.

However, these parties have played an important role in Korean politics in the sense that without them, the

GNP and the DP would have dominated Korea’s political agendas.

All of the five politicians in the sample were members of the 17th Korean National Assembly. Three were

members of the 18th Assembly (from May 2008 through May 2012). In addition, these five politicians had

distinct career backgrounds and different levels of offline resources. Table 1 lists these five politicians and

provides some background information.

All of the five politicians played an important role within their party and were well known in the national

political scene. The two GNP members—Kyeong-Won Na and Hee-Ryong Won—ran in the GNP’s Seoul

mayoral primary election in March 2010. Na and Won actively used various online media platforms for the

primary election. Indeed, they were famous for their active use of online platforms. Na was one of the top

10 politicians in terms of the number of visitor comments on the Cyworld4 minihompy between 2008 and

2009 (Park et al. 2011), and Won was widely known as an active blogger (Park and Kluver 2009; Park and

Thelwall 2008). It should be noted that the only female politician in the sample was Na.

Table 1. Background Information on Five Politicians

Name (Age, Sex) Party Affiliation Other Notable Information

Dong-Young Chung

(57 years old, Male)

Democratic Party (DP) Former TV journalist; the DP’s candidate

for the 2007 presidential election

Gi-Gap Kang*

(57 years old, Male)

Democratic Labor Party

(DLP), a progressive (left-

wing) party

Former president of the DLP; Well known

for his past as a farmer and his unexpected

win against a strong ruling party member

in the 2008 parliamentary election

Hoi-Chan Noh

(54 years old, Male)

New Progress Party (NPP),

a progressive (left-wing)

party

Long-time labor activist;

Co-president of the NPP

Kyeong-Won Na*

(47 years old, Female)

Grand National Party

(GNP), the conservative

ruling party

Former judge;

Candidate for the GNP’s 2010 Seoul

mayoral primary election

Hee-Ryong Won*

(46 years old, Male)

Grand National Party

(GNP), the conservative

ruling party

Former prosecutor;

Candidate for the GNP’s 2010 Seoul

Mayoral primary election; One of the

4 Cyworld is the most popular social networking site in South Korea.

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GNP’s four nominees for the 2007

presidential election

* Incumbent members of the Korean National Assembly.

4.2. Data Collection and Analysis

We collected the data in June 2010 by using an application programming interface (API) tool available in

NodeXL embedded in Excel 2007 (Hansen et al. 2010). Using this API tool, we collected data on each

politician’s Twitter use as follows: (1) the number of Twitter followers (i.e., the number of Twitter users

who followed each politician), (2) the list of Twitter followers’ ID, (3) the number of Twitter followings

(i.e., the number of Twitter users whom each politician followed), (4) the list of Twitter followings’ ID, and

(5) the number of Tweets each politician published.

Based on the data, we generated two matrices: the follower-based and following-based matrices. The

number of Twitter followers was incorporated into the follower-based matrix, and the number of Twitter

followings, into the following-based matrix. Such matrices can be visualized by using NodeXL, which can

be freely downloaded from http://nodexl.codeplex.com.

For TH indicators, the frequency with which the politicians’ names appeared in Twitter posts was

determined by using an advanced search option provided by Topsy.com, a specialized search engine for

SNSs. The search was conducted on July 21, 2011. Specifically, the occurrence and co-occurrence of

politician names were measured in terms of their trilateral relationships by using Boolean operators. For

example, we measured the number of Twitter mentions of politician A without any mention of politician B

or politician C. In this case, we measured the mutual information on two (e.g., politicians A and B) and

three (e.g., politicians A, B, and C) dimensions by using the mutual information transmission capacity

expressed in “T” values. The transmission T was measured by “mbits” of information. T values for bilateral

relationships are always positive, but they can be negative for trilateral relationships. We calculated the TH

indicators by using the relative frequency of communications or the probability distribution (for a

mathematical definition, see Leydesdorff, 2003) and T values by using a standard technique in the TH

program available at http://www.leydesdorff.net/th2/index.htm.

5. Findings and Discussion

This section addresses each research question separately, but we first provide an overview of the five

politicians’ Twitter use.

As of April 2010, Twitter was one of the most popular micro-blogging SNSs in Korea. Twitter had

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approximately 2.1 million unique visitors, and its pages were viewed approximately 49.5 million times per

day (National Information Society Agency, 2010, p. 235).

Among the five politicians, Dong-Young Chung was the first to use Twitter. He opened his Twitter account

on June 17, 2009. Less than one month later, Hoi-Chan Noh opened his Twitter account (July 6, 2009).

Kyeong-Won Na soon followed (July 22, 2009). The remaining two politicians—Gi-Gap Kang and Hee-

Ryong Won—opened theirs on the same day (January 29, 2010).

Because of the differences in the amount of time each politician spent using Twitter, we calculated the

average number of Tweets each politician sent per day. Two of the three early adopters were the most

prolific publishers of Tweets. Hoi-Chan Noh sent 17.9 Tweets per day, followed by Dong-Young Chung,

who sent 17.8 Tweets. Won and Kang sent 2.9 and 1.4 Tweets, respectively. Kyeong-Won Na, although

she opened her Twitter account in July 2009, was far less active (.3 per day or one Tweet every three or

four days).

The total number of Twitter followers for each politician ranged from 4,276 to 65,541. Hee-Ryong Won

had the smallest number of followers, whereas Hoi-Chan Noh had the largest number. Noh also had the

largest number of followings; he followed 65,514 Twitter users. By contrast, Kyeong-Won Na followed

only 611. Table 2 summarizes the total numbers of followers and followings for each politician, including

the total number of Twitter lists for each politician. Noh was the most listed politician, whereas Won was

the least listed.

Table 2. Numbers of Followers, Followings, and Lists

Politician Total # of Followers Total # of Followings Total # of Lists

Dong-Young Chung 15,302 15,266 1,706

Gi-Gap Kang 8,716 5,911 1,274

Hoi-Chan Noh 63,192 65,541 5,059

Kyeong-Won Na 6,084 611 709

Hee-Ryong Won 4,276 1,942 623

5.1. Twitter as a Means of Political Participation

In addressing Twitter as a means of political participation, we focused on the potential of Twitter as (1) a

direct communication channel between politicians and their followers and (2) an alternative communication

channel for non-mainstream, resource-deficient politicians. With respect to the former, we determined the

degree of reciprocity between politicians and their followers and examined the “ego network” for each

politician. The ego network, a network map in which a politician is the node at the center, visualizes the

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relationship among the politician, his or her followers, and his or her followings.

A politician can have three types of relationships with other Twitter users. The first type concerns a group

of Twitter users who follow a politician but are not followed by the politician. In this study, such Twitter

users are referred to as solitary Twitter followers of the politician. The second type reflects a reciprocal

relationship; Here, reciprocal Twitterians refer to a group of Twitter users who follow a politician and are

followed by the politician. The third type of relationship reflects the relationship between a politician and a

group of Twitter users who do not follow the politician but are followed by the politician. In this study,

these Twitter users are referred to as solitary Twitter followings of the politician.

Based on the numbers of Twitter users belonging to each of these three groups, we calculated the

politician’s Twitter reciprocity. Reciprocity indicates the proportion of reciprocal Twitterians to the sum of

solitary Twitter followers of the politician and reciprocal Twitterians. For example, if a politician’s Twitter

reciprocity is 50%, then he or she is following half the Twitter users who are following his or her Tweets.

The five Korean politicians showed varying degrees of Twitter reciprocity, making it difficult to determine

whether and how frequently Twitter was used as a direct communication channel between these politicians

and their followers. Kyeong-Won Na and Hee-Ryong Won (members of the conservative ruling party) had

the lowest Twitter reciprocity: 1% for Na and 2.9% for Won. This result is noteworthy in that these two

politicians, known to be active on online platforms, were expected to be active users of Twitter to connect

with their supporters. Clearly, these two politicians did not make use of Twitter as a direct communication

channel between themselves and their followers. Gi-Gap Kang’s Twitter reciprocity (10%) was higher than

that of Na or Won but was still too low to suggest a two-way communication channel between Kang and

his followers.

On the other hand, Hoi-Chan Noh and Dong-Young Chung had the highest Twitter reciprocity: 61% for

Noh and 56% for Chung. Thus, these two politicians were most likely to use Twitter as a direct

communication channel through which their Twitter followers could reach them. It remains unclear how

often reciprocal Twitterians sent messages via Twitter to Noh and Chung, but their communication

channels were more open than those of the other three politicians.

Although both Noh and Chung demonstrated a high degree of Twitter reciprocity, Noh was more active

than Chung in interacting with followers. Noh was quicker to respond to or comment on Tweets he

received than Chung. In other words, Noh engaged his Twitter followers by sending Tweets referring to

various issues mentioned in Tweets he received, whereas Chung tended to send Tweets at his own pace and

was less likely to respond to Tweets he received.

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In addition, to visualize the relationship between the politicians and their followers or followings, we

examined ego networks. Diagrams 1 and 2 show the five politicians’ ego networks. In the ego network map

for each politician, the politician is represented by the node at the center (the center square with the Twitter

profile picture of the politician). Other nodes in the map are Twitterians who either followed or were

followed by the politician. The size and color of each node correspond to the number of followers of each

Twitterian (see Diagrams 1 and 2 for details). For instance, a purple node indicates a Twitterian with

followers ranging from 10,001 to 100,000. To enhance visual quality, we did not textually label or thickly

color the nodes. In addition, the politician’s followings and followers were iteratively repositioned with the

relaxed length proportional to the edge length for the best visualization.

The five politicians’ following-based ego networks (Diagram 1) indicate some interesting trends. First, the

following-based ego networks of Noh and Chung indicate that these two politicians followed only a few

Twitterians who each had more than 10,000 followers. Noh’s ego network has no blue or pink nodes; all

are yellow (except for 5 purple nodes). Chung’s network is similar; his network has no blue or pink nodes

but has 8 purple nodes. These two politicians were the most active users of Twitter. Their following-based

ego networks indicate networks indicate that they were not likely to follow famous Twitterians with large

numbers of followers.

On the other hand, the remaining three politicians’ following-based ego networks indicate that Won, Na,

and Kang were more likely to follow famous Twitterians than Noh and Chung. Won’s ego network has 2

blue nodes, 3 pink nodes, and more than 30 purple nodes. Similarly, Na’s ego network has 1 blue node, 4

pink nodes, and more than 20 purple nodes, and Kang’s ego network has more than 20 purple nodes.

Thus, powerful nodes are more noticeable in the following-based ego networks of Won, Na, and Kang than

in those of Noh and Chung. This difference indicates that Won, Na, and Kang were more likely than Noh

and Chung to follow famous Twitterians with large numbers of followers.

Diagram 2 shows the five politicians’ follower-based ego networks. Although Kang’s follower-based ego

network has the smallest number of purple nodes and Na’s ego network has the largest number of purple

nodes, there is no clear difference among five politicians’ follower-based ego networks. All the five ego

networks consist of mostly yellow nodes with several purple nodes. None has a pink or blue node. This

indicates that the vast majority of followers of these five politicians were Twitterians with fewer than

10,000 followers.

Finally, the following-based ego networks of Noh and Chung are similar to their follower-based ego

networks. These two politicians showed the highest Twitter reciprocity, indicating that there were many

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overlaps between their followings and followers. As a result, their following-based ego networks are

similar to their follower-based ego networks.

Diagram 1. Five Politicians’ Following-Based Ego

Networks

The size and color of each node corresponds to the

number of followers as follows:

Size of

node

Color of

node

Number of followers

1.5 Yellow 0 to 10,000

3 0

Purple

10,001 to 100,000

3.5 Pink 100,001 to 1,000,000

4.0 Blue More than 1,000,000

Dong-Young Chung

Gi-Gap Kang Hoi-Chan Noh

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Kyeong-Won Na Hee-Ryong Won

Diagram 2. Five Politicians’ Follower-Based Ego Networks

The size and color of each node corresponds to the number of

followers as follows:

Size of

node

Color of

node

Number of followers

1.

Yellow

0 to 10,000

3.0 Purple 10,001 to 100,000

3.5 Pink 100,001 to 1,000,000

4.0 Blue More than 1,000,000

Dong-Young Chung

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Gi-Gap Kang Hoi-Chan Noh

Kyeong-Won Na Hee-Ryong Won

The other aspect of Twitter as a means of political participation relates to Twitter’s potential as an

alternative communication channel for non-mainstream, resource-deficient politicians. Among the five

politicians, two—Gi-Gap Kang (a DLP member) and Hoi-Chan Noh (an NPP member)—were underdogs.5

In addition, both the DLP and the NPP have been labor-oriented parties with insufficient offline resources.6

One of the non-mainstream, resource-deficient politicians, Hoi-Chan Noh, was the most active politician in

5 Their political parties held only 6 seats (5 for the DLP and 1 for the NPP) out of 299 seats in the Korean National Assembly.6 According to the National Election Committee’s (2009) report on the income and expenditure of each active political party in Korea, the DLP generated KRW 22.395 billion, and the NPP, KRW 3.375 billion KRW. The ruling party, the GNP, generated KRW 109.322 billion, which was 4.9 times that generated by the DLP and 32.4 times that by the NPP. The DP generated KRW 83.131 billion, which was 3.7 times that generated by the DLP and 24.6 times that by the NPP. In terms of the election campaign expenditure in 2008 (the year in which the 18th National Assembly election was held), The DP spent the most amount of money: KRW 20.812 billion. The GNP spent KRW 8.310 billion, followed by the DLP (KRW 6.715 billion) and the NPP (KRW 577 million).

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terms of almost all aspects of Twitter use. Noh was the second politician to adopt Twitter; sent the greatest

number of Tweets daily; had the greatest number of Twitter followers as well as followings; and showed

the highest degree of Twitter reciprocity. In terms of the characteristics of Noh’s followers and followings,

Noh communicated with various socioeconomic groups on a wide range of social and political issues.

Moreover, as indicated by Noh’s Twitter reciprocity (61%), Noh sought to strike a balance between those

to whom he sent his messages and those from whom he received messages.

The other non-mainstream, resource-deficient politician, Gi-Gap Kang, was not as active as Hoi-Chan Noh.

Kang started Tweeting later than the others and had not been sending many Tweets. However, he surpassed

both Kyeong-Won Na and Hee-Ryong Won in terms of reciprocity and the numbers of followers and

followings. Kang had 8,716 followers and 5,911 followings, whereas Won, who started Tweeting at the

same time as Kang, had 1,942 followers and 4,276 followings. Nah, who started Tweeting in July 2009,

had 6,084 followers and 611 followings. Kang’s Twitter reciprocity was 10%, Na’s was 1%, and Won’s

was 2.9%.

These results suggest that non-mainstream, resource-deficient politicians are more likely to use Twitter and

demonstrate a higher degree of reciprocity than mainstream politicians. Dong-Young Chung, a

mainstream politician with liberal political views, was a very active Twitter user. Of the five politicians, he

was the first politician to use Twitter, and he was the second most active Twitter user in terms of other

indicators of Twitter use. His active Twitter use suggests that in Korea, both liberal and progressive

politicians are more likely to use Twitter than conservative politicians, which is consistent with the findings

of Williamson, Miller, and Fallon (2010) concerning MPs in the U.K.

5.2. Twitter as a forum for political deliberation

In addressing Twitter as a forum for political deliberation, we focused on determining whether and to what

extent Twitter is a forum dominated by a small number of heavy users and whether and to what extent it is

a forum polarized according to the political orientation of users.

First, to determine whether a small number of Twitter users dominate political discourse in the

Twittersphere, we randomly selected 1,000 Twitterians from lists of followers and followings for each

politician and examined the Twitter activity of those Twitterians. In other words, for each politician, 2,000

Twitterians (1,000 from his or her followers and another 1,000 from his or her followings) were randomly

selected, except for Kyeong-Won Na, who had fewer than 1,000 followings. For Na, we randomly selected

1,000 Twitterians from her list of followers but only 611 from her list of followings. This process yielded a

total of 9,611 Twitterians.

16

We then calculated the number of Twitterians with more than 10,000 followers and the number of those

with more than 1,000 Tweets. In this study, we refer to those Twitterians with more than 10,000 followers

as widely connected Twitterians because they have the ability to deliver their Tweets to a large number of

followers. We refer to those Twitterians with more than 1,000 Tweets as prolific Twitterians because they

frequently contribute to political discourse by using Twitter.

Table 3 shows the numbers of widely connected Twitterians and prolific Twitterians for each politician.

There were very few widely connected Twitterians. Only .3% to 1.1% (an average of .76%) of Twitter

followers of the politicians were widely connected Twitterians. In addition, .5% to 4.6% (an average of

2.4%) of Twitter followings of the politicians were widely connected Twitterians.

On the other hand, Table 3 shows that all the politicians (except for Na) had more of prolific Twitterians

than widely connected Twitterians as their followers and followings. Na followed 28 widely connected

Twitterians but only 9 prolific Twitterians. On average, 7% of Twitter followers of politicians were prolific

Twitterians, and the number of prolific Twitterians following the politicians was approximately 10 times

that of widely connected Twitterians. There were more of prolific Twitterians among the politicians’

followings. Approximately 11.9% of Twitter followings of the politicians were prolific Twitterians.

Table 3. Numbers of Widely Connected and Prolific Twitterians

Politician Among Followers

(out of 1,000 Twitterians, except

where noted)

Among Followings

(out of 1,000 Twitterians, except

where noted)

Widely

connected

Twitterians

Prolific

Twitterians

Widely

connected

Twitterians

Prolific

Twitterians

Dong-Young Chung 8 59 8 70

Gi-Gap Kang 3 98 33 253

Hoi-Chan Noh 9 29 5 21

Kyeong-Won Na 11 77 28* 9*

Hee-Ryong Won 7 89 38 194

Total 38** 352** 112*** 547***

* Out of 611 Twitterians.

** Out of 5,000 Twitterians.

*** Out of 4,611 Twitterians.

The fact that approximately 10% of Twitter users were prolific Twitterians indicates that the vast majority

(almost 90%) of Twitter users might have simply received Tweets without contributing much to political

17

discourse through Twitter. A German study (Tumasjan et al., 2010) found that only 4% of Twitterians

accounted for more than 40% of Tweets examined.

Although this study classifies Twitterians into two categories—widely connected Twitterians and prolific

Twitterians—some Twitterians were widely connected as well as prolific. Thus, we refer to these

Twitterians (i.e., those with more than 10,000 followers and more than 1,000 tweets) as influential

Twitterians. We examined these influential Twitterians as followers and followings of each politician.

Each politician followed 5 influential Twitterians, but the total number of influential Twitterians followed

by the five politicians was 20, not 25, because some politicians followed the same influential Twitterians.

Two influential Twitterians—a famous Korean novelist, Oi-Soo Lee, and a well-known national TV news

anchor, Ju-Ha Kim—were followed by Kang, Na, and Won. Na and Won followed another influential

Twitterian, Je-Dong Kim, a popular TV personality and entertainer. The remaining influential Twitterians

consisted of five private firms, two international news outlets, two doctors, two IT-related businessmen,

two individuals of unknown affiliation, an international businessman, a Brazilian novelist, a singer, and a

religious leader.

Each politician had 5 influential Twitterians who followed him or her. There was no overlap between these

influential Twitterians. The 25 influential Twitterians were seven individuals of unknown affiliation, six

private firms, two businessmen, a comedian, a doctor, an IT professional, a journalist, a martial arts trainer,

a politician, a professor, a professional photographer, a provider of U.S. stock market news, and a visual

arts curator.

We then determined whether and to what extent Twitter was polarized according to the political orientation

of Twitterians who followed the five politicians. For this, we examined the overlap between Twitter

followers of the five politicians and that between Twitter followings of the politicians.

Table 4 shows the number of Twitterians who followed two politicians simultaneously. The figure in a cell

represents the number of Twitterians who followed both the politician whose name is given in the column

heading and the one whose name is given in the row heading. Similarly, Table 5 shows the number of

Twitterians who were followed by two politicians simultaneously. In addition, Diagrams 3 and 4 visualize

the intensity of the overlap between two politicians. Here, the thicker the line between two politicians, the

greater the overlap between the two politicians in terms of their followers (Diagram 3) and their followings

(Diagram 4).

The results in these tables and diagrams provide some evidence of polarization. For example, the two

progressive (left-wing) politicians—Gi-Gap Kang and Hoi-Chan Noh—had 8,716 and 63,192 Twitter

18

followers, respectively. Among Kang’s 8,716 followers, 34% also followed Hoi-Chan Noh, showing the

strongest overlap between the two progressive politicians. Further, 17% of Kang’s followers also followed

Dong-Young Chung, who was not progressive but tended to be liberal on some issues. By contrast, there

was little or no overlap between Kang’s followers and the followers of the two conservative politicians

Won and Na. Only 5% of Kang’s followers also followed Hee-Ryong Won. There was no overlap between

followers of Kang and Na.

In terms of Noh, the most active Twitter user, 6,721 and 2,957 of Noh’s followers also followed Chung and

Kang, respectively, but only 13 and 1,077 also followed Na and Won, respectively. That is, Noh’s

followers were more likely to also follow moderate or progressive politicians than conservative ones.

Noteworthy is that Noh overlapped more with Chung (a moderate politician) than with Kang (a progressive

(left-wing) politician). This may because Chung (15,302 Twitter followers) had many more Twitter

followers than Kang (8,716 Twitter followers).

These results are consistent with those for Twitter followings. As a result, Diagrams 3 and 4 show that the

three lines between Chung, Kang, and Noh are stronger than their lines with Na and Won.

Although there is some evidence of polarization in terms of the two progressive politicians’ Twitter

followers and followings, the results indicate no substantial overlap between the two conservative

politicians in terms of both their followers and followings. The overlap between Na’s followers and that

between her followings were simply too small for any meaningful comparison with those of the other

politicians’ followers and followings. In addition, the overlap between Won’s followers and that between

his followings were simply proportional to those of the other politicians’ followers and followings. Those

whose followers and followings overlapped the most with those of Won were Noh, Chung, Kang, and Na,

in that order. This order is consistent with that in terms of the number of politicians’ followers and

followings.

Table 4. Numbers of Overlaps Between Followers

Dong-Young

Chung

Gi-Gap Kang Hoi-Chan

Noh

Kyeong-Won

Na

Hee-Ryong

Won

Dong-Young Chung NA (Not

Applicable)

1,517 6,721 8 668

Gi-Gap Kang 1,517 NA 2,957 0 395

Hoi-Chan Noh 6,721 2,957 NA 13 1,007

Kyeong-Won Na 8 0 13 NA 5

Hee-Ryong Won 668 395 1,007 5 NA

19

Table 5. Numbers of Overlaps Between Followings

Dong-Young

Chung

Gi-Gap Kang Hoi-Chan

Noh

Kyeong-Won

Na

Hee-Ryong

Won

Dong-Young Chung NA (not

applicable)

1,523 6,879 11 541

Gi-Gap Kang 1,523 NA 2,982 1 321

Hoi-Chan Noh 6,879 2,982 NA 16 791

Kyeong-Won Na 11 1 16 NA 3

Hee-Ryong Won 541 321 791 3 NA

Diagram 3. Intensity of Overlaps (Followers)

Diagram 4. Intensity of Overlaps (Followings)

20

5.3. Twitter as a communication channel in terms of the TH model

We employed the TH method to develop a communication system based on the co-occurrence of the

politicians’ names in the Twitter sphere. Table 6 summarizes the entropy values for the politicians’

trilateral relationships. As discussed earlier, these relationships were calculated using the standard

algorithm in Leydesdorff’s TH.exe software package.

The lower the entropy value, the higher the (imbalance) of the communication system is. Further, entropy

values for bilateral relationships are, by definition, positive, whereas those for trilateral relationships can be

negative, positive, or zero. Thus, it is necessary to compare the absolute value of each entropy value when

entropy values are calculated for trilateral relationships. In the case of entropy values for trilateral

relationships, the higher the absolute entropy value, the more balanced the communication system is.

he trilateral relationship among Kyeong-Won Na (a conservative), Dong-Young Chung (a moderate), and

Hoi-Chan Noh (a progressive) showed the highest absolute entropy value (|-0.4| = 0.4), indicating that the

communication system was best balanced under a trilateral relationship among three politicians with

different political orientations.

The absolute entropy values were lower when the trilateral relationship included the two conservative

politicians: Na and Won. As indicated earlier, the lower the entropy value, the less stable the

communication system is. Thus, the communication system became more unbalanced in trilateral

relationships that included the two conservative politicians. On the other hand, in those trilateral

relationships including only one conservative politician, the entropy values were higher, and the

21

communication system was more stable. These results suggest that the level of political deliberation,

expressed in terms of the degree of stability in the communication system, increases when politicians with

different political orientations form trilateral relationships.

Table 6 Numbers of hits for TH components for five politicians

Politician (A B C) A B C AB AC BC ABC

Na, Won, Noh 18000 377 16000 898 118 50 32

Na, Won, Kang 16000 380 4438 898 1 1 1

Na, Won, Chung 16000 357 14000 898 63 68 1

Na, Noh, Kang 18000 15000 3817 118 1 571 0

Na, Noh, Chung 16000 14000 13000 118 63 737 0

Na, Kang, Chung 15000 3618 13000 1 63 280 1

Won, Noh, Kang 9208 19000 10000 50 1 571 0

Won, Noh, Chung 8353 18000 27000 50 68 737 1

Won, Kang, Chung 8154 10000 28000 1 68 280 1

No, Kang, Chung 18000 9224 27000 571 737 280 151

Diagram 5. A comparison of trilateral relationships of five politicians on Twitter

22

6. Discussion and Conclusions

The results of this exploratory study of Korean politicians’ Twitter use are consistent with the findings of

previous Twitter research. First, the results suggest that non-mainstream, resource-deficient politicians are

likely to maximize Twitter’s potential as an alternative means of political participation. Hoi-Chan Noh, an

NPP member (a labor party), exemplifies this trend. Noh’s Twitter reciprocity was 61%, which indicates

that Noh followed more than half of his followers and that his followers used Twitter as a direct

communication channel to connect with Noh. In addition, Noh sent Tweets in response to Tweets he

received, demonstrating that he sought a conversational and interactive relationship with his followers. On

the other hand, the two mainstream (conservative) politicians showed only 2.9% and 1.0% Twitter

reciprocity, respectively. The ego networks of the five politicians provide support for this trend.

Second, the results suggest that a small number of Twitterians may be leading political discourse in the

Twittersphere. Among the randomly selected 9,611 Twitterians who were either followers or followings of

the five politicians, only approximately 10% were prolific Twitterians who sent 1,000 Tweets since they

opened their Twitter accounts. We were unable to compare our findings with a statistical reference point

because, to our knowledge, no such reference exists. Thus, future research should verify the results of the

present study in terms of widely connected, prolific, and influential Twitterians to provide a better

understanding of the nature of political discourse in the Twittersphere.

Third, the results provide some evidence of polarization. For instance, approximately 34% of Twitterians

who followed Kang, a progressive (left-wing) politician, also followed Noh, another progressive politician.

However, only 5% also followed Won, a conservative politician. Overall, the intensity of the overlap

between followers of a progressive politician and those of another progressive or moderate politician was

stronger than that between followers of a progressive politician and those of a conservative politician.

Fourth, we examined the rise of Twitter as a user-generated communication system for political

participation and deliberation by using TH indicators. We measured the occurrence and co-occurrence of

the five politicians’ names in Twitter posts by their trilateral relations. The trilateral relationship among

three politicians with different political orientations showed the highest absolute entropy value, indicating

that they had the most balanced communication system. On the other hand, the absolute entropy values

were lower (i.e., less stable communication systems) when the trilateral relationship included the two

conservative politicians: Na and Won. These findings suggest that the level of political deliberation,

expressed in terms of the degree of stability in the communication system, increases when politicians with

different political orientations form trilateral relationships.

23

Because of the small sample size and purposive sampling, any generalization of the results to groups

outside the sample profile should be implemented with caution. Indeed, in this study, we focused on

maximizing the exploratory nature of the inquiry by suggesting the ways to determine whether and how

politicians use Twitter for political participation and deliberation. Specifically, we analyzed Twitter

reciprocity; drew ego networks; characterized and examined widely connected, prolific, and influential

Twitterians; and determined the overlap between followers and followings of five politicians. In addition,

we employed TH indicators to measure the degree of stability in the Twitter communication system. The

highest entropy value was found in the trilateral relationship among three politicians of three different

political orientations.

An increasing number of studies have examined various sociocultural and political issues surrounding the

adoption and application of SNSs in Western contexts (boyd & Ellison, 2007). Thus, for a more balanced

understanding of the role of various SNSs (including Twitter) in political communication, future research

should also consider non-Western contexts.

24

Acknowledgments:

This research was partly supported by the World Class University (WCU) project through the National

Research Foundation of Korea, funded by the Ministry of Education, Science and Technology (No. 515-82-

06574). The corresponding author is grateful for Ji-Young Park for data collection and visualization.

25

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