Triple helix 2012 president

Mapping Election Campaigns Through Negative Entropy:
Triple and Quadruple Helix Approach
to Korea’s 2012 Presidential Election
Virtual Knowledge Studio (VKS)
Asso. Prof. Dr. Han Woo PARK
CyberEmotions Research Institute
Dept. of Media & Communication
YeungNam University
214-1 Dae-dong, Gyeongsan-si,
Gyeongsangbuk-do 712-749
Republic of Korea
www.hanpark.net
eastasia.yu.ac.kr
asia-triplehelix.org

Introduction
 In recent presidential elections in the
U.S., social media have served as an
important communication channel for
individuals to discuss their preferences for
candidates and voting experiences (Pew
Internet & American Life Project, 2012).
 Similarly, Korean voters have increasingly
shared their thoughts on candidates
during elections.

 Social media platforms have become a notable venue for Korean
voters wishing to share their opinions and predictions with others
(Park et al., 2011; Sams & Park, 2013).
 Politicians have made increasingly use of SNSs to provide updates
and communicate with citizens (Hsu & Park, 2012).
 With the increasing proliferation of smartphones and portable
computers in Korea, SNSs have been widely used for facilitating
political discourse.
 Prior studies have found that Web 1.0 contents tended to contain the
more enduring political and electoral statements of the public in
various contexts.
Introduction

 To better understand the dynamics of the 2012 presidential election
in Korea, this study estimates the web visibility of the three major
candidates— Geun-Hye Park (PARK), Cheol-Soo Ahn (AHN), and
Jae-In Moon (MOON)—in the entire digital sphere.
Introduction

 As Lim & Park (2011, 2013)
claim, the use of web
mentions of politicians’ names
is particularly useful for
hierarchically ranking
individual politicians.
 However, it may not
sufficiently capture the
entropy probability of an
event (hidden in changing
communication structures)
resulting from the amount of
information conveyed by the
occurrence of that event
(Shannon, 1948).
Literature Review

 Taleb (2012) argues that society
can be conceived as a complex
fabric consisting of the extended
disorder family including
uncertainty, chance, entropy, etc.
 Therefore, such disorder system
can be better derived from
empirical data mining, not
obtained by a priori theorem.
Literature Review

Literature Review
 In social and communication
sciences, entropy-based indicators
have been widely used for exploring
entropy values generated from
university-industry-government (UIG)
relationships.
 This “Triple Helix” (TH) system is
based on the concurrence of a pair of
two or three terms (e.g., UI or UIG) in
the public search engine database
(Khan & Park, 2011; Park, Hong, &
Leydesdorff, 2005).

 According to Leydesdorff (2006, p. 43), universities, firms, and
governments are the primary institutions in knowledge-based societies.
 Novelty generation (universities), wealth generation (firms), and
regulation (governments).
Literature Review

 Decisions on whether to cite a given paper are
made by readers, and from this audience
perspective, co-citations partially reflect
authors’ intentional construction.
 The emerging network of aggregated co-
citation relationships between scientists is
known to be highly unstable and thus reflects
a high level of uncertainty.
 Uncertainty exists when three or more events
take place simultaneously and is increasingly
beyond the control of individual events
(Leydesdorff, 2008).
Literature Review

 The total probabilistic entropy (uncertainty) produced by changes in one or
two dimensions is always positive, which is in accordance with the second
law of thermodynamics (Theil, 1972, p. 59).
 On the other hand, the relative contribution of each event to the
summation in three or four dimensions can be positive, zero, or negative
(configurational information).
 This configurational information provides a measure of synergy within a
complex communication system. Network effects occur in a systemic and
nonlinear manner when loops in the configuration generate redundancies
in relationships between three or four events (Leydesdorff, 2008).
Literature Review

Literature Review
 Entropy-based indicators have been widely used in studies of the
Scientometrics to measure the knowledge infrastructure of the UIG relationship
(Kwon et al., 2011; Leydesdorff, 2003; Park & Leydesdorff, 2010).
 However, this model has recently been applied to some complex social
contexts, including the use of music festivals by popular communications and
entertainment industries (Khan, Cho & Park, 2011),
 The trilateral overlay of exchange relationships on existing socio-ideological
divisions between congressional members with similar/different political
affiliations (Kim & Park, 2011), and the dynamics of Twitter-mediated
communication encouraging knowledge-based innovation in digital societies
(Choi, Park, & Park, 2011).

Literature Review
 Twitter can be very effective to amplify messages particularly in terms of their
one-to-many mode of communication (Barash & Golder, 2010).
 Twitter is viable both as a political news and communication channel
(González-Bailón, Borge-Holthoefer, Rivero & Moreno, 2011; Hsu & Park,
2011, 2012; Otterbacher, Shapiro, & Hemphill, 2013)
 and to citizens who look for platforms for political participation and engagement
(Hsu, Park, & Park, 2013; Kim & Park, 2011; Tufekci& Wilson, 2012).

Literature Review
 The mode of information sharing on Facebook differs from that on Twitter.
Facebook functions as a living room where friends talk to one another.
 Facebook can be a mixture of interpersonal and mass channels for the sharing of
informational as well as social messages in a context of political campaign (Bond
et al., 2012; Effing, van Hillegersberg, & Huibers, 2011; Robertson, Vatrapu, &
Medina, 2010; Vitak et al., 2011).
 Both Twitter and Facebook communications seem to be biased because two
platforms have been particularly dominated by the “2040 Generation”, who are
generally categorized as political liberals in Korea (Kwak et al., 2011).

Research questions
 Therefore, it is important to examine what (social) media
conversations are more likely to generate more entropies that
others and which politician:
 RQ 1) What (social) media generate (negative) entropy more than
others across different periods?
 RQ 2) Which politician (or which pair of politicians) generates
entropy more than others for bilateral, trilateral, or quadruple
relationships across various media and periods?

Method: Data collection
 Therefore, it is important to
examine what (social) media
conversations are more likely to
generate more entropies that
others and which politician:
 There are two types of datasets
in the research:
 November 3, 2012.
 December 6, 12, and 17.

Method: Data collection
 The number of hits for each search query per media channel (Facebook,
Twitter, and Google) was harvested.
 The hit counts obtained from Google.com were employed to look
primarily at entropies represented on a set of digitally accessible
documents (e.g., online versions of newspapers, online word-of-mouth,
Web 1.0 contents, etc.).
 We measured the occurrence and co-occurrence of the politicians’
names based on their bilateral, trilateral, and quadruple relationships by
using Boolean operators.

Method: Measuring (negative) enthropy
 Figure 1. Binary Entropy Plot

Results
 Figure 2. Entropy Values Across Media Channels and Time Periods

Results
 Figure 3. T Values for Bilateral and Trilateral Relationships on November 3.

Results
 Figure 4. T Values for Bilateral Relationships between Park and Moon

Discussion and conclusions
 Twitter has scored the most negative entropy values and
Facebook followed. Google came last. This indicates that
Twitter is the most open communication system.
 The entropy values for liberal candidates (AHN and
MOON) have been higher than their conservative
opponent PARK on social media than Google sphere.
 This may not be surprising because both Twitter and
Facebook have particularly appeared to the Korean
citizens in the age of late teenagers to early 40s.

 PARK’s entropy has been slightly higher
on Google than her liberal challenger
MOON.
 Park was successful in garnering a strong
support from senior voters in their 50s
and 60s accounted for 39% of the
population, up from 29% a decade ago
(Wall Street Journal, 2012).
 Exit poll also revealed that PARK gained
a support from 62% of voters in their 50s
and 72% of voters in their 60s. Indeed,
the most significant statistic on the
election was that South Koreans in their
20s, 30s, and 40s actually voted 65.2%,
72.5%, and 78.7% respectively but 89.9%
in 50s and 78.8% over 60s went to the
polling booth.

 No survey can accurately measure outcomes, but Koreans have
increasingly expressed doubts about such survey-based reports (Nam, Lee,
& Park, 2013; Sams, Lim, & Park, 2011).
 Web and social media allow voters to debate one another and change their
views, thereby offering a better understanding of election (Kobayashi &
Boase, 2012; Okumura, 2007; Skoic, 2012; Zhu et al., 2011, 2012).
 As demonstrated by the case of Nate Silver’s 538 blog in the recent U.S.
presidential election, depending solely on traditional techniques may fail to
appreciate the breadth and depth of an election campaign (Silver, 2012).

 This suggests that conventional political theories and methodological
details may be wrong. With all the multisensory interactions
surrounding the Internet and social media, it may be naive to depend
only on traditional tools.
 A candidate’s handshakes and street speeches have shifted rapidly to
cyberspace since the 2002 presidential election in Korea (Lee & Park,
2013; Park & Lee, 2008).
 Regardless of their political affiliations or leanings, Koreans have
become active participants in the online campaign process (Park et
al., 2011).
 Researchers as well as political analysts have increasingly turned to
new indicators that can better reflect this new political phenomenon.
This study proposes negative entropy not as a comprehensive or
representative index of elections but as an experimental and
innovative measure for events occurring in social media
environments.

Triple helix 2012 president

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