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Chapter XX. NodeXL Scholarship in Korea

Kyujin Jung, Ph.D.Tennessee State University

Weiai Xu, Ph.D.Northeastern University

Han Woo Park, Ph.D.YeungNam University

In Korea, NodeXL has been not only widelyutilized for linking theory to practice but alsoapplied for investigating diverse features derivedfrom social media. To our surprise, NodeXLmanuals in Korean were proudly included in themost viewed slides of 2013 according toSlideshare.net. Furthermore, NodeXL Korea usergroup has been formed in 2014 in order topromote the NodeXL in terms of open tool insocial media network analysis. Regarding the

details of the NodeXL’s popularity in Korea,please refer to the following document available atthe website:http://www.slideshare.net/hanpark/note-about-formation-of-node-xl-korea-users-group-hanwoopark. This chapter aims to examinethoroughly how NodeXL has been used foranalyzing political phenomena, public policy, andcultural innovation cases under Korean socio-economic contexts. In the following section, we

provide a review of the literature using NodeXLon social media in the field of politicalcommunication, public policy, and culturalinnovation. Then, this chapter concludes withdiscussion of implications and directions for futureresearch.

X.1 NodeXL in Political Science and PublicPolicy

In the Korean field of political science and publicpolicy, NodeXL has played a pivotal role inexamining communication patterns amongpoliticians and communities [1][2][3][4], politicalparticipation [5], governments’ social media use[6], and homeland security policy [7] on socialmedia such as Twitter and Facebook. Particularly,

previous researches focused on both introducingNodeXL to the Korean academia and proposing amethodological strategy to collect social medianetwork data. For instance, Kim and Park [5]introduced NodeXL as a tool based on theapplication programming interface (API) toreaders which helps researchers to collect datafrom Twitter users. Using NodeXL, they not onlycollected the number of Twitter followers andfollowings, the list of Twitter followers’ andfollowings’ ID, and the number of Tweets each

user published but also visualized the follower-based and following-based matrices. As a result,they found that resource-deficient politicians aremore likely to secure Twitter’s potentials thanother politicians.

Furthermore, NodeXL has providedcritical data-visualization functions, which allowresearcher to draw a range from entire networks toego-networks representations and to map dataattributes to visible properties e.g., nodes’ size,color, shape, and transparency. In this vein, Choiet al. [6] used NodeXL to detect and representinnovation processes in social network datacollected from Twitter, highlighting that “a

particularly important aspect of NodeXL is thatsocial science researchers can improve theefficiency of data visualization analysis byexamining data retrieved from a specificgeography and timeframe (p. 46)”. Through theiranalysis using NodeXL, the results showedevidence that innovation processes are mainlyemerged from overlapped members who

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participated in multitudinous innovationcommunities. Using NodeXL, Cho and Park [9]also visualized ego-networks within Twitteractivities of the Korean Ministry for Food,Agriculture, Forestry, and Fisheries todemonstrate the relationships its followers and

followings. From a result of its ego-networkanalysis, this research found that the Ministry’sefforts to directly communicate with the publicunder a specific event while Twitter has thepotential to facilitate risk communication.

With advanced network simulationtechniques, NodeXL is suitable to elaboratepoliticians’ networking behavior [2][8][9][4] and aparticular event such as the 2013 North Korea’snuclear test [7]. In order to provide a betterunderstanding of the dynamics of the discussionnetwork, one hand, Choi et al. [9] collected thelongitudinal network data derived from Tweetswith the former president’s name and three types

of the messages (i.e., followings, mentions, andretweets by using NodeXL applications. Usingsample from November 1, 2011, to April 20, 2012included 26,150 Twitter users and 892,034relationships, the results showed that thediscussion about President Myung-Bak Lee was

dominated by Twitter users who already had

considerable influence both online and offline. Onthe other hand, Yoon and Park [2] utilizedNodeXL to collect data including Korea’s nationalassemblymen and the most influential politicalfigures. Then, they tried to link the data toexponential random graph models in order to

predict Twitter-based networking patterns ofactors. Particularly, it can be considered as acritical step to fostering NodeXL by testinghypothesized structures in social media networkdata as advanced applications of NodeXL [10]. Asshown in Figure X.1, on top of that, Jung and Park[7] proposed a systemically-designed researchusing NodeXL. Following the case of the 2013North Korea’s nuclear test, they collected socialmedia network data from Twitter for four weeks.

Again, they initially collected data for respectivelytwo weeks before and after the nuclear test. Basedon the longitudinal data, they showed the changeof Twitter-based networks’ attributes such as sizeof network, density, reciprocity rate, and pagerank index. By collecting the data with key wordsin English and Korean through NodeXL, theyfound evidence that the impact of social contextsmatters in the evolution of each international andKorean networks.

Figure X.1 During the 2013 North Korea’s nuclear test, changes in the average geodesic network distance and density

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Regarding to natural disaster such astropical typhoons and floods occurred in Korea,scholars in the field of emergency managementstarted using NodeXL to investigate patterns ofrisk communication on social media. Sincecitizens use social media as the source of disaster

information for helping them evacuate fromdisaster-impacted sites, NodeXL can be used notonly to collect real-time social media network databut also to contribute to decision-makingprocedures of principal agencies such as national,provincial, and local emergency operations centers.According to the Seoul research report [8], forinstance, NodeXL seems to fill the gap of acurrent risk management system operated by theSeoul Metropolitan Government. The report

recommends NodeXL for public administrators toidentify emergent needs of victims and affectedcommunities, which facilitate stakeholders tomake a timely decision during disaster responses.As a result of the social media network analysisusing NodeXL, the report presented policy

implication that local governments within the Cityof Seoul are recommended to periodically monitorsocial media and then provide customized disasterinformation that citizens need after a disaster.

Recent frequent occurrences ofoverwhelming events raise urgent need forstudying effective risk communication on socialmedia. In this point of view, NodeXL is a key toexamine the patterns of risk communication

embedded in social media networks. Song et al.[11] utilized NodeXL to identify the differencespatterns between Korean and internationalcontexts by comparing Korean and internationalnetworks based on the social amplification of riskframework. The results in Figure X.2, fromNodeXL analysis focusing on interpersonal riskcommunication in the context of the Sewol ferrydisaster, show that the Korean risk communicationnetwork was more fragmented, and its clusteringwas more sparsely knitted based on the impact ofissues and the physical proximity of the disaster.

Figure X.2 After the Sewol ferry disaster, Korean risk communication networks on Facebook

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X.2 NodeXL in Cultural Innovation

NodeXL provides a systematic look over thefabrics of our digital culture. The tool addresses afundamental shift of power in cultural innovations.The contemporary culture is increasingly mediatedthrough participatory internet platforms in whichnetworked cultural consumers encounter, evaluateand recreate cultural symbols. What drive culturalinnovations are not single cultural institutions,

governments or policies, but a networked onlinecommunity of active cultural participants.NodeXL can be used to visualize and measure thestructure of the community, thus providingcompelling insights into two essential qualities ofdigital culture: virality and meme.

Virality ensures the wide exposure ofcultural offerings to the global audience. Strategicpromotion of national cultures becomes possiblethrough viral videos. The successful cases includeKorean artist Psy’s horse-dancing Gangnam Style,

which has become synonymous with Kpop. Meme,

referring to a host of user-generated cultureinspired by the original viral symbols, expands thelongevity of viral culture [12]. Following therelease of Gangnam Style, various genres of remixand mash-ups bubbled up to create a viablecultural ecosystem. The two qualities call for a

paradigm shift from studying established culturalenforcers to studying networked individuals incultural systems. In light of the explosive growthof viral digital culture, in which Korea has playeda dominant role, a new research strain has beendeveloped to explore the interplay in the Kpopmediated through digital platforms. Suchknowledge is critical in understanding how nation-states and corporates strategically enhance theirbrand images [13]. The research can be integrated

to build a hybrid webometric model of culturalinnovations, built on webometrics enabled byNodeXL. Figure X.3 shows a visualization of thewebometric model used in Xu, Park, and Park’s[13] work.

Figure X.3 The webometric model for examining YouTube-based cultural innovations through NodeXL

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The hybrid model uses NodeXL toexamine three layers of ties in YouTube-basednetworks of cultural innovations. The first layer isbased on reply-to-comment behavior among users,meaning that A is connected to B when A repliesto B’s comments on a video [14]. Such directional

ties reveal exchanges of ideas within the onlinecommunity formed on collective culturalevaluation. Using the case of Gangnam Style, Xu,Park and Park [13] showed that YouTubecommentators, while being the minority of theirpeer, nevertheless engaged in continuous andreciprocal exchanges of cultural critique.Interestingly, center users in such discussionnetworks—those who are most active in replyingothers’ comments and most frequently targeted by

other users, voiced critical opinions towards thecultural offering. In a way, these influential users

are not necessarily evangelists but criticalconnoisseurs. Their opinions possibly resonatewith the rest of the community. Among the activecommentators, their intensively of participationvaried by gender: male commentators, in the caseof Gangnam Style, were more active than females,

and those residing in countries culturally similar toKorea were more likely to express favorableattitudes. Figure X.4 displays the network graphdescribed in the above study. Taking alongitudinal look [15], the influential commenters,majority of them are amateur users, retained theircentral dominance over time. But the structure ofthe entire discussion network changed: thenetwork became smaller, less cohesive and morecompartmentalized, reflecting the waning of

audience interest and engagement, and possibly,the increasing divide among users over cultural

Figure X.4 YouTube network based on users’ reply-to-comment activities in the diffusion of Gangnam style

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tastes and opinions.

The second layer of ties reflects sharedvideo taste/interest among users. The ties arebased on co-subscription to the same YouTubechannel, that is, A and B are connected when bothsubscribe to the same channel [14]. Thissymmetric network tends to be denser than reply-to-comment networks because subscriptionrequires minimal cognitive and time input than

commenting. By tabulating the most sharedchannels, Xu, Park and Park [13] showed thatcommentators in the case of Gangnam Styleshared topical interest in Kpop and peculiarcontent. The shared video interest disintegratedover time [15] — as time went by, commentatorsshared less video interest, reflected in decreasingdensity in the network of co-subscription.

The third layer of ties is based on user-generated content, in comments and user-generated videos. Semantic networks feature

topical links based on semantic co-occurrenceswithin the same text [16]. Semantic analysis—akey feature in NodeXL—has been used to revealsalient and thus influential concepts in userdiscussions. The study of Gangnam Style onYouTube showed that discussions by

commentators focused on the cultural origin of thevideo and related the content to the broadernational and cultural image of a foreign country[13]. NodeXL also features connections betweencultural objects in meme. This object-objectnetwork underlies topical similarities between twovideos and the mutual attention they are able todrive. In this network, videos A and B are tiedwhen both are commented on by the same user[14]. By examining the network structure and

unique positions, the analyses can revealinfluential cultural objects in the entire memeecosystem. A study of meme inspired byGangnam Style showed that memetic creations fallin the categories of remix, reproducing

Figure X.5 A network of meme, with links indicating mutual audience attention attracted by two user-generated videos.

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background music, parody, physically imitatingthe horse-dancing, making verbal commentsthrough response videos and etc. [17]. Figure X.5visualizes a network of memes identified in Xu etal., (in press). The genre reflects varying degree ofuser participation, and accordingly, result in

different levels of audience attention: for example,a few remix and physical imitation videosattracted a large number of commentators, butreaction videos (verbal commenting) drew mostcross-commenting, likely due to controversialnature of opinion expression. Based on centralpositions in the meme network, the contentprovided by traditional mass media gave the viralcultural symbol wide publicity. But the dominantrole of traditional media was later shared byamateur users.

Taking into account the three types of ties,the networked perspective towards culturalinnovations on YouTube reveals the structure of

the social system in diffusion of innovations.NodeXL is critical in visualizing the relationshippatterns in the social system, whether it is therelationship formed on social interaction, sharedinterest or topical similarities. Discussing salientactors in the network reveals the important roles ofvarious diffusion actors such as innovators andearly adopters. Examining the changing landscapeof the social system also reveals various diffusionstages such as evaluation, trial, etc. Overall, the

insights add important pieces to the Rogers’ [18]classical diffusion framework.

X.3 Directions for Future Research usingNodeXL

While much of the current NodeXL research inKorea has focused on capturing the nature ofsocial media network, its users may overlooked aqualitative perspective that facilitate researchers tofill the gap of quantitative results derived fromNodeXL’s applications. For instance, testing

patterns of interactions among actors can explainstructural relationships such as reciprocal andtransitive ties, but it seldom clarifies potentialfactors to build the patterns. The qualitativeapproach to NodeXL’s applications helpsresearchers systemically design their data

collection procedures and analysis methods. Priorto using NodeXL, contacting and interviewing keystakeholders engaged in certain policy issuessheds light on the motivation of their interactionswith other actors on social media network. Toundoubtedly answer a research question derivedfrom theoretical considerations, NodeXL presentsthe key to data collection and analysis methods,but future research should consider its applicationsas a critical lens to analyze and evaluate social

phenomena and previous research byincorporating the qualitative perspective.

Cultural innovations, on the other hand, isan interesting case for testing classical social

science theories such as diffusion and opinionleadership. In the theoretical testing, NodeXLprovides a structural view of the entirecommunication system. The networkedperspective, in future studies, can be connectedwith individual-level analyses of important actors.For example, in the diffusion of viral culturalsymbols, patterns of individual network positionscan be compared to the user’s self-reported data.In addition, NodeXL and the network perspective

it represents provide new territories for testing oldtheories. For example, there is a recent call forcombining social network analysis with agenda-setting [20]. In addition, findings from theaforementioned YouTube studies of culturalinnovations can be interpreted in conjunction withfindings of global cultural innovations on Twitter[3][19]. NodeXL can be used to map connectionsbetween attributes and objects in culturalinnovations, as researchers previously did, andthen such connections can be compared and

contrasted with individual perception of thesalience of certain cultural objects.

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In terms of data validity and reliabilityissues, studying political theory and public policyembedded in social media network recommendsus to narrow down a case of research topics withsocial events such as the 2012 president electionand the 2013 North Korea’s nuclear test. That is,

because, the network collected and visualized byNodeXL might be part of an entire network onFacebook or Twitter. In future research, socialmedia network data tends to be more complicatedand/or overlapped with not-relevant stakeholders,and thus it should be intentionally designed fordata collection and methods such as keywords andtime points used by a researcher. Furthermore,sharing data collection and cleaning procedureswith others through NodeXL Graph Gallery can

be an opportunity to increase the validity andreliability of social media network data.

Finally, the combination of networkperspective and analyses of individual perception

requires us to integrate network data and surveydata. This represents a new frontier for futuremethodological development of NodeXL studies.Another possible methodological innovation, inthe context of cultural innovations, is to compareYouTube-based networks with networks on theirsocial media platforms. YouTube arguably can beviewed as an entertainment media platform, morefor content consumption and less for socialinteractions, whereas, on Facebook, social needs

become a more salient need. It is worthydiscussing how culture diffuses through networksunderlying different types of relationship ties.

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