Mapping Election Campaigns Through Negative Entropy:Triple and Quadruple Helix Approachto Korea’s 2012 Presidential ElectionVirtual Knowledge Studio (VKS)Asso. Prof. Dr. Han Woo PARKCyberEmotions Research InstituteDept. of Media & CommunicationYeungNam University214-1 Dae-dong, Gyeongsan-si,Gyeongsangbuk-do 712-749Republic of Koreawww.hanpark.neteastasia.yu.ac.krasia-triplehelix.org
Introduction In recent presidential elections in theU.S., social media have served as animportant communication channel forindividuals to discuss their preferences forcandidates and voting experiences (PewInternet & American Life Project, 2012). Similarly, Korean voters have increasinglyshared their thoughts on candidatesduring elections.
Social media platforms have become a notable venue for Koreanvoters 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 updatesand communicate with citizens (Hsu & Park, 2012). With the increasing proliferation of smartphones and portablecomputers in Korea, SNSs have been widely used for facilitatingpolitical discourse. Prior studies have found that Web 1.0 contents tended to contain themore enduring political and electoral statements of the public invarious contexts.Introduction
To better understand the dynamics of the 2012 presidential electionin Korea, this study estimates the web visibility of the three majorcandidates— Geun-Hye Park (PARK), Cheol-Soo Ahn (AHN), andJae-In Moon (MOON)—in the entire digital sphere.Introduction
As Lim & Park (2011, 2013)claim, the use of webmentions of politicians’ namesis particularly useful forhierarchically rankingindividual politicians. However, it may notsufficiently capture theentropy probability of anevent (hidden in changingcommunication structures)resulting from the amount ofinformation conveyed by theoccurrence of that event(Shannon, 1948).Literature Review
Taleb (2012) argues that societycan be conceived as a complexfabric consisting of the extendeddisorder family includinguncertainty, chance, entropy, etc. Therefore, such disorder systemcan be better derived fromempirical data mining, notobtained by a priori theorem.Literature Review
Literature Review In social and communicationsciences, entropy-based indicatorshave been widely used for exploringentropy values generated fromuniversity-industry-government (UIG)relationships. This “Triple Helix” (TH) system isbased on the concurrence of a pair oftwo or three terms (e.g., UI or UIG) inthe public search engine database(Khan & Park, 2011; Park, Hong, &Leydesdorff, 2005).
According to Leydesdorff (2006, p. 43), universities, firms, andgovernments are the primary institutions in knowledge-based societies. Novelty generation (universities), wealth generation (firms), andregulation (governments).Literature Review
Decisions on whether to cite a given paper aremade by readers, and from this audienceperspective, co-citations partially reflectauthors’ intentional construction. The emerging network of aggregated co-citation relationships between scientists isknown to be highly unstable and thus reflectsa high level of uncertainty. Uncertainty exists when three or more eventstake place simultaneously and is increasinglybeyond the control of individual events(Leydesdorff, 2008).Literature Review
The total probabilistic entropy (uncertainty) produced by changes in one ortwo dimensions is always positive, which is in accordance with the secondlaw of thermodynamics (Theil, 1972, p. 59). On the other hand, the relative contribution of each event to thesummation in three or four dimensions can be positive, zero, or negative(configurational information). This configurational information provides a measure of synergy within acomplex communication system. Network effects occur in a systemic andnonlinear manner when loops in the configuration generate redundanciesin relationships between three or four events (Leydesdorff, 2008).Literature Review
Literature Review Entropy-based indicators have been widely used in studies of theScientometrics 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 socialcontexts, including the use of music festivals by popular communications andentertainment industries (Khan, Cho & Park, 2011), The trilateral overlay of exchange relationships on existing socio-ideologicaldivisions between congressional members with similar/different politicalaffiliations (Kim & Park, 2011), and the dynamics of Twitter-mediatedcommunication 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 theirone-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 ofinformational as well as social messages in a context of political campaign (Bondet 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 twoplatforms have been particularly dominated by the “2040 Generation”, who aregenerally categorized as political liberals in Korea (Kwak et al., 2011).
Research questions Therefore, it is important to examine what (social) mediaconversations are more likely to generate more entropies thatothers and which politician: RQ 1) What (social) media generate (negative) entropy more thanothers across different periods? RQ 2) Which politician (or which pair of politicians) generatesentropy more than others for bilateral, trilateral, or quadruplerelationships across various media and periods?
Method: Data collection Therefore, it is important toexamine what (social) mediaconversations are more likely togenerate more entropies thatothers and which politician: There are two types of datasetsin 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 lookprimarily at entropies represented on a set of digitally accessibledocuments (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 byusing Boolean operators.
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 andFacebook followed. Google came last. This indicates thatTwitter is the most open communication system. The entropy values for liberal candidates (AHN andMOON) have been higher than their conservativeopponent PARK on social media than Google sphere. This may not be surprising because both Twitter andFacebook have particularly appeared to the Koreancitizens in the age of late teenagers to early 40s.
Discussion and conclusions PARK’s entropy has been slightly higheron Google than her liberal challengerMOON. Park was successful in garnering a strongsupport from senior voters in their 50sand 60s accounted for 39% of thepopulation, up from 29% a decade ago(Wall Street Journal, 2012). Exit poll also revealed that PARK gaineda support from 62% of voters in their 50sand 72% of voters in their 60s. Indeed,the most significant statistic on theelection was that South Koreans in their20s, 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 thepolling booth.
Discussion and conclusions No survey can accurately measure outcomes, but Koreans haveincreasingly 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 theirviews, 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 toappreciate the breadth and depth of an election campaign (Silver, 2012).
Discussion and conclusions This suggests that conventional political theories and methodologicaldetails may be wrong. With all the multisensory interactionssurrounding the Internet and social media, it may be naive to dependonly on traditional tools. A candidate’s handshakes and street speeches have shifted rapidly tocyberspace since the 2002 presidential election in Korea (Lee & Park,2013; Park & Lee, 2008). Regardless of their political affiliations or leanings, Koreans havebecome active participants in the online campaign process (Park etal., 2011). Researchers as well as political analysts have increasingly turned tonew indicators that can better reflect this new political phenomenon.This study proposes negative entropy not as a comprehensive orrepresentative index of elections but as an experimental andinnovative measure for events occurring in social mediaenvironments.