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 PARKCyberEmotions 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.

Discussion and conclusions

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.

Discussion and conclusions

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).

Discussion and conclusions

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.

Thank you!

Q&A