Expanding the presidential debate by tweeting: The 2012 presidential election debate in South Korea

Telematics and Informatics 33 (2016) 557–569

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Telematics and Informatics journal homepage: www.elsevier.com/locate/tele

Expanding the presidential debate by tweeting: The 2012 presidential election debate in South Korea Se Jung Park a, Ji Young Park b, Yon Soo Lim c, Han Woo Park d,⇑ a

Department of Communication, Georgia State University, P.O. Box 5060, Atlanta, GA 30302-5060, United States Department of East Asian Cultural Studies, YeungNam University, 280 Daehak-Ro, Gyeongsan-si, Gyeongsangbuk-do 712-749, South Korea c School of Advertising & Public Relations, HongIk University, 2639 Sejong-ro, Jochiwon-eup, Sejong 339-701, South Korea d Department of Media and Communication, Interdisciplinary Program of East Asian Cultural Studies, Interdisciplinary Program of Digital Convergence Business, YeungNam University, 214-1 Dae-dong, Gyeongsan-si, Gyeongsangbuk-do 712-749, South Korea b

a r t i c l e

i n f o

Article history: Received 15 August 2014 Received in revised form 2 June 2015 Accepted 5 August 2015 Available online 13 August 2015 Keywords: Twitter Issue network Social Network Analysis Presidential debate Gatekeeper

a b s t r a c t This study investigates Twitter issue networks formed in response to the 2012 presidential debates on TV in South Korea. The overall network structure and the communication pattern of users were mapped over time, and then the gatekeepers playing roles as hubs in each candidate’s issue network and their framing practices were identified. Network analyses (a keyword analysis and a CONCOR analysis) were conducted and gatekeepers’ political ideologies were identified. The results indicate that issue networks evolved in concert with the viewership of televised debates. According to the profile analysis of gatekeepers and the keyword analysis, gatekeepers on Twitter formed homogenous groups in terms of their political ideologies and geographic locations. In addition, Twitter users tended to form networks critical of the major conservative candidate, whereas they constructed networks of support for the major opposite candidate. This study contributes to understanding the role of Twitter in reflecting the political landscape in the offline world by revealing the structure, contents, and gatekeeping practices of issue networks. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Social media represent a new type of interactive communication platform that connects individuals’ ideas, information, and stories to one another and makes them public. In political contexts, electorates exchange their opinions, comments, and feelings toward politicians or social issues and form issue networks with others through social media (Himelboim et al., 2013; Otterbacher et al., 2013; Tumasjan et al., 2010). Social scientists have explored the political use of social media and its implications in the context of information diffusion (Ausserhofer and Maireder, 2013; Lotan et al., 2011; Theocharis, 2013; Tremayne, 2014; Romero et al., 2011). However, few studies have focused on communication patterns of users (Hsu et al., 2013; Park, 2013), gatekeeping (Bastos et al., 2013; Kwon et al., 2012) and characteristics of issue networks (Cho and Park, 2012; Kim and Park, 2012a,b), which can provide a better understanding of information diffusion on social media. Another remarkable gap in the literature on the role of social media in political arena is that previous studies have mainly focused on Western and the Middle East contexts (Park and

⇑ Corresponding author. E-mail addresses: [email protected] (S.J. Park), [email protected] (J.Y. Park), [email protected] (Y.S. Lim), [email protected] (H.W. Park). http://dx.doi.org/10.1016/j.tele.2015.08.004 0736-5853/Ó 2015 Elsevier Ltd. All rights reserved.

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Thelwall, 2008; Park and Lee, 2008; Bruns and Eltham, 2009; Cha et al., 2010; Lotan et al., 2011). That is, there is little data for understanding political communication through social networking in Asian countries where political use of social media is prevalent. South Korea (hereafter ‘‘Korea”) serves as a useful example for illustrating the collective power of constituents networked by social media as they actively intervene in political events (Chang and Park, 2012; Lee and Park, 2013; Sams and Park, 2014). In recent days, researchers and political pundits have highlighted Twitter’s considerable influence on stimulating younger generations’ political interest and encouraging voting during elections in Korea. Ahead of the election day and during the election, a large number of users urged their followers to vote by posting their ‘‘photo evidence of voting,” showing their on-site experiences, which is considered a new election culture based on social media (Chang and Bae, 2012; Chang, 2010; Nam et al., 2013; Park, 2013). It is noteworthy that with a growing population of cross-media users, Twitter functions as a communication backchannel to traditional mainstream media when news-worthy events are broadcasted. Recent studies have pointed out that networked viewers vigorously produce personal notes and commentaries on TV programs aired via social media and they are likely to have conversations with strangers for political debates (Lotan et al., 2011; Park, 2013). Though this information-sharing practices and user-generated contents in the relation of traditional media have had a long history after the introduction of World Wide Web (e.g., news room bulletin board, blog posts), a distinctive feature of real-time communication of Twitter magnifies the virtual TV viewership (Ampofo et al., 2011; Park, 2013). Twitter extends and supplements TV audience experiences based on distributed communication networks in which interwoven audiences rebroadcast, interpret, and shape political events to the public and this practice may influence how other viewers perceive the events on a real-time basis (Lotan et al., 2011; Bruns and Eltham, 2009). Therefore, it is important to examine a symbiotic and dynamic relationship between the mainstream media and social media to understanding the role of social media as an emergent backchannel and who shape critical events in which ways. The present study examines the structure and content of Twitter issue networks composed of interwoven users in the context of TV debates during the 2012 presidential election in Korea. These issue networks were created and shaped by active users engaging in political discussions on Twitter. The study provides an empirical and analytical account of communication patterns of Twitter users; the role of gatekeepers in disseminating and shaping political events; and the function of Twitter issue networks to reflect the unfolding of an election. 2. A new mode of audiencing through Twitter Social media complicate the role of TV in political engagement by offering a real-time platform for public discussions on contents, transforming the way audiences gather and share their opinions on TV programs beyond their living rooms (Park, 2013; Bruns and Eltham, 2009; Anstead and O’Loughlin, 2011; Ampofo et al., 2011). Anstead and O’Loughlin (2011) coined the term ‘‘viewertariat,” which refers to audiences who publish their thoughts and responses on TV content and interact with others on social media. This concept highlights a shift in the role of TV viewers from passive recipients to active agents. If TV mediates newsworthy events, this new mode of audiencing has important implications for political discussions. Users construct an issue network on social media to produce commentaries on sociopolitical issues broadcasted on TV and exchange them with others. Social media can not only serve as a real-time backchannel for public discussions by mobilizing peers in providing feedback, checking facts, and editing and co-producing meanings on what is being watched but also sometimes play a role as a quantitative indicator of future outcomes of socio-political events (Ciulla et al., 2012; Doughty et al., 2011). For example, Anstead and O’Loughlin (2011) verified the active role of the viewertariat in expanding TV debates by sharing opinions about programs on Twitter while watching a weekly political debate show in the U.K. (BBC Question Time) aired on October 22, 2009. Wohn and Na (2011) examined the viewertariat during President Barack Obama’s live speech at the White House on his acceptance of the Nobel Peace Prize on October 9, 2009 and found that Twitter users selectively interacted with others with similar interests and actively exchanged their opinions on a real-time basis while watching televised events. Similarly, Ampofo et al. (2011) examined the viewertariat during the general election in the U. K. in 2010 and concluded that TV and social media complement each other and coevolve. These findings suggest that social media facilitate viewers’ interaction with others based on TV content and influence the way they understand sociopolitical events and participate in politics. This suggests that social scientists should consider social media in exploring this new mode of TV consumption and public engagement in politics. 3. Gatekeeping on social media From the perspective of two-step flow theory (Lazarsfeld et al., 1944), opinion leaders who play critical roles in spreading certain information to mass audiences have considerable influence on shaping public opinion and triggering behavioral change within a community (Valente and Pumpuang, 2007). To refer to an opinion leader on social media, scholars have used different terms such as a ‘‘leader”, ‘‘influencer”, ‘‘power user”, ‘‘gatewatcher”, and ‘‘gatekeeper” (Bastos et al., 2013; Bruns, 2005; Cha et al., 2010; Hsu et al., 2013). This paper uses the term of ‘‘gatekeeper” in which the concept has long been applied to mass media and communication research, although other terms are more arbitrary in terms of the meanings. Bastos et al. (2013) applied this concept to digital networks and they noted that unlike centralized wired networks, examining

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gatekeeping in virtual communities in the sense of ‘‘information control” is obsolete. Rather, they suggested that gatekeeper should be determined in terms of capability to address and manage information. The disagreement in the terminology between scholars has caused some confusion of its definition, thereby making it difficult to identify gatekeepers on social media. Previous studies have identified gatekeepers in terms of users’ social capital, connectivity in social networks, and to extent of message creation activities (Leavitt et al., 2009; Cha et al., 2010; Bastos et al., 2013; Park and Thelwall, 2006). Scholars have attempted to identify gatekeepers in terms of the connectivity and networking power of Twitter users based on the number of their followers and followings (Chin and Chignell, 2006). This approach gauges their influence by considering the size of their social networks. For instance, the more friends the user, the higher his or her rank on social media is. However, the connectivity approach may be problematic in that such a definition of gatekeepers account only for the number of connections, ignoring the quality of relationships between users (Leavitt et al., 2009). Given this tendency, considering the size of social networks as an indicator for classifying gatekeepers may be too simplistic for measuring a user’s ability to disseminate information on Twitter and his or her leverage in opinion formation. An alternative approach to identifying gatekeepers on social media is the ‘‘information-forwarding” approach. Romero et al. (2011) claimed that user passivity is an obstacle to the delivery of messages to wider audiences. Their argument suggests that the extent of message production should be considered as indicators of gatekeepers. However, this approach is also limited in that it cannot account for structural properties of social networks, which represent a major determinant of message diffusion. Existing studies reviewed above imply that both information production activities and structural properties of users are impetrative in determining gatekeepers who play decisive role in information diffusion of social networks. Therefore, this study combines both approaches in identifying gatekeepers. That is, we take account of the extent of information production activities and central network properties of users in identifying gatekeepers of issue networks. In recent days, scholars have explored the role of gatekeepers in information diffusion (Ampofo et al., 2011; Bruns, 2005; Hsu et al., 2013; Kwon et al., 2012). They found that ordinary individuals and user-generated contents play an important role in public agenda-setting and issue framing besides mainstream news media and journalists (Ampofo et al., 2011; Hsu et al., 2013; Kwon et al., 2012). These studies suggest that gatekeepers have potential capability in shaping socio-political issues and forming public opinion. Such studies, however, have failed to provide a clear understanding of the communicative characteristics and ideological orientation towards politics of gatekeepers and their roles in issue networks. This suggests that if gatekeepers are disinterested and distribute relevant information from diverse perspectives, there can be a useful political discourse. By contrast, if they are partisan and employ social media to strengthen their positions in framing political issues, social media can be used as an echo chamber. Given the importance of opinion leaders in political discussions, how gatekeepers report televised debates and frame main points may shape others’ perspectives (Valente and Pumpuang, 2007). This suggests the necessity of investigating the mechanism of gatekeeping practices in the context of political debates on social media.

4. The role of social media in politics There is an ongoing debate about the role of social media in politics. Some studies and political analysts have argued that social media platforms reflect the political landscape of the offline world, whereas others have suggested that a large portion of messages on social media are mundane and ‘‘pointless babble” (Pearanalytics, 2009). On the one hand, previous studies have noted that political discourse and information diffusion on social media can provide useful data for detecting the political climate of the public. In the context of political campaigns, social media may be useful for understanding how elections unfold based on voters’ expression of their views and debates with one another to evaluate candidates (Nam et al., 2013; Park, 2014). Recent studies have found that traditional media outlets and surveys often fail to accurately estimate election outcomes and that the Internet and social media can be used as an alternative tool for measuring political opinions and sentiments (Kobayashi and Boase, 2012; Okumura et al., 2007; Salganik and Levy, 2012; Skoric, 2012; Zhu et al., 2011). For example, Nam et al. (2013) suggested that the network structure and information behavior of the Internet and Twitter during 2010 local elections in Korea reflected the popularity of certain candidates, issue salience, and voters’ interest in a given election. Williams and Gulati (2008) showed that the number of Facebook supporters is a valid predictor of electoral success. Similarly, Tumasjan et al. (2010) found that Twitter messages mirrored voter preferences and political sentiments expressed in tweets corresponding to positions of parties and politicians prior to the 2010 Germen Federal election. On the other hand, other scholars have argued the limited predictive power of social media in illustrating offline politics. For example, Gladwell (2010) pointed out that social media use promotes ‘‘slacktivism,” in which people tend to perceive that they do something politically meaningful through online activism requiring little effort and risk. This suggests that online political participation may have little effect on offline politics. In a similar vein, some studies have shown that the exchange and sharing of information on social media are biased toward young liberals with progressive ideologies (Kwak et al., 2011; Park, 2014). This suggests that social media may be used as an echo chamber among like-minded people and thus may not meaningfully reflect the climate of political discourse. Another criticism is that, because social media use

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has become an integral part of everyday life, a majority of updates are mundane and useless in political contexts (Pearanalytics, 2009). These mixed results from previous studies suggest that how and by whom social media are used may determine the function of social media as a political discussion platform and the platform’s predictive power to illustrate offline politics. Therefore, this study examines the diffusion and sharing of political information on Twitter in the context of 2012 presidential election debates in Korea and the role of gatekeepers as core agents disseminating and shaping issues. 5. Political landscape in South Korea 5.1. Political culture and Internet control in South Korea The main political ideologies in Korea can be broadly classified into two categories: conservatism and progressivism. Conservatism focuses on modernization and social stability whereas progressives are interested in social welfare based on humanism and egalitarianism. The main political parties in South Korea are the ruling Saenury Party, which is a major conservative party, the Democratic United Party which is a major progressive party, and the Liberty Forward Party that is a minor progressive party. Since the Korean War (1950–1953), conservatism has dominated the modern politics in Korea, but conflict between conservatism and progressivism often occurs especially during critical political events or election campaigns. In the relations with North Korea, the two distinct ideologies are contradictory. That is, conservatives oppose to communism and concerned on North Korea’s threat. Progressives have supported a national reconciliation with North Korea to alleviate tension on the Korean peninsula. Korea is the most networked country where high-speed Internet penetration rate suppressed 100% firstly among OECD countries in 2012 (The Korea Herald, 2012). Ironically, the state has strongly controlled Internet use for political purposes (Park et al., 2011). Online censorship during election periods is even tighter. The Korean National Election Commission (NEC) has regulated Internet use for political campaigning for 180 days prior to voting. The election law bans politicallysensitive posts on the web, including supporting or criticizing politicians or parties during pre-election periods. While this stringent but vague restriction may prevent campaigners’ explicit activities in shaping online discourse, constituents have still generated a significant number of postings to express their opinions and ideas about politicians through Twitter during election periods in Korea (Park, 2013). 5.1.1. South Korean presidential election debates on TV Three TV debates were held during the 2012 presidential election in Korea (December 4, 10, and 16 2012). The first debate covered ‘‘politics, diplomacy, security, and reunification with North Korea”; the second debate, ‘‘welfare, the economy, labor, and the environment”; and the last one, ‘‘policies on education reform, science and technology, public safety, and social welfare.” Three were three major candidates: Guen-hye Park (hereafter ‘‘Park”), a conservative candidate of the ruling Saenury Party, Jung-hee Lee (hereafter ‘‘Lee”), a progressive candidate of the Unified Progressive Party (a minor opposition party), and Jae-in Moon (hereafter ‘‘Moon”), a progressive candidate of the Democratic United Party, a major opposition party (Kim, 2012). These presidential debates received considerable public attention and were considered the most entertaining political show. The three debates garnered 34.9%, 34.7%, and 26.6% of total viewers, respectively. The highlight of these debates was the strident performance of Lee, who was regarded as an extremely progressive politician (KoreaBANG, 2012). She was depicted as the winner of the presidential debate. Noteworthy is that her performance was highlighted in domestic and international news outlets and triggered burning reactions from members of online communities (Kim and Park, 2012a,b). She explicitly offered offensive remarks about Park, the candidate of the ruling party. For example, she described Park as a ‘‘queen” and mocked her father, Chung-Hee Park (a former president of Korea), by calling him ‘‘Takaki Masao,” his Japanese name. This was related to the criticism that he ruled the nation for 18 years until his assassination in 1979 and referred to his association with Japanese colonialism in Korea. She even stated that one of her motives to be a presidential candidate was to attack Park (Kim and Park, 2012a,b; KoreaBANG, 2012). Park, who was elected as the first female president of Korea, was considered a less prepared speaker in the debates and failed to sufficiently address her opponents’ attacks, whereas Moon was more successful in showing a sense of dignity (Lee and Shin, 2012). The result of the presidential election gave Park a victory over Moon. An exit poll revealed that Park was successful in receiving support from older voters in their fifties and sixties, who accounted for 39% of the population, and that Moon garnered 66% of all votes from younger voters in their twenties and thirties (Wall Street Journal, 2012). 6. Research objectives This study addresses how voters use Twitter as a backchannel for political discussions in the context of TV debates during the 2012 presidential election in Korea and investigates whether the Twitter issue network reflects the way in which the election unfolds. The study defines a Twitter issue network as a social network of interested users producing messages on a given sociopolitical issue (Hsu et al., 2013). First, by mapping structural characteristics of issue networks and

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relationships between users, the study analyzes the pattern of their communication. The structure of a social network can be a useful quantitative indicator of users’ perceived usefulness and importance of given information (Nam et al., 2013). In an issue network, users’ messages and communication can increase if they perceive some information to be interesting or valuable (Nam et al., 2013). Second, the study identifies gatekeepers and investigates their role in diffusing information and leading political discussions in issue networks. Given that some active users produce a majority of information and lead the discussion on a specific political event on social media (Bruns and Burgess, 2012), an analysis of gatekeepers’ tweets may provide a better understanding of information trends. In this regard, the present study is guided by the following four research questions:  RQ1: How do Twitter users construct issue networks over time in the context of TV debates during the 2012 presidential election in Korea?  RQ2: How do Twitter users interact with one another in response to political candidates?  RQ3: Who are the gatekeepers and what are their political ideologies in Twitter issue networks?  RQ4: How do gatekeepers frame TV debates across presidential candidates with different political ideologies on Twitter? 7. Methodology 7.1. Data collection To collect overall Twitter responses to the TV debates during the 2012 presidential election in Korea, a Search API-based network analysis tool NodeXL embedded in Excel 2007 was used. This tool allows gathering tweets based on search terms. We have selected the five representative key words in describing the presidential TV debates: The full name of the three candidates (e.g., ‘‘Geun-hye Park,” ‘‘Jae-in Moon,” and ‘‘Jung-hee Lee,”), ‘‘presidential election,” and ‘‘discussion” in Korean language. This obtained all the tweets, including the five key words during an each 120-min TV debates. This keywordbased data collection also enables to capture the relevant hash tags (#) that include the five search terms. For instance, it crawled #election, or #candidates’ names. The API-tool we used crawled all tweets, including the five search terms. This means that even though we did not select hash tags (#) as search terms, it crawled #election, or #candidates’ names since they included the five keywords. The reason we did not take any other hash tags is that in South Korea, using hash tag is not popular compared to other countries. Therefore, we believe that the five key words are enough to capture the relative discussion on the debates in Korean context. The Search Twitter API software provides snapshots of real-time data with a maximum of 1500 results per search query and it only traces tweets about a week. To take account of this limitation, all data sets were retrieved every 10 min during each 120-min debate to capture more updated tweets. We first separately collected each keyword data and integrated the five-keyword data set into one network for analysis. In this process, duplicated tweets gathered from different keyword data set were deleted. Considering the possibility of the term ‘‘discussion” being used to refer to other discussions rather than the presidential TV debates, we carefully read all of the tweets under the key word and filtered the irrelevant tweets that do not address the presidential debates. The automatically gathered tweets included original tweets, replies, mentions, and retweets. To map the communication networks of users, we have deleted tweets that were neither retweets (RT), replies, nor mentions and the final data set analyzed represents the interactions among users. Finally a total of 11,025 tweets generated by 7181 users were collected for the first debate (RT: 96.78%, replies: 2.70%, and mentions: 0.52%) on December 4, 2012, and a total of 11,822 tweets (RT: 97.40%, replies: 2.22%, and mentions: 0.37%) produced by 7149 users were harvested for the second debate on December 10, 2012. Because of Lee’s withdrawal after the second debate, her name was deleted from the data set for the final debate and we gathered a total of 5138 tweets (RT: 97.57%, replies: 2.12%, and mentions: 0.31%) generated by 2347 users on December 16, 2012. Descriptive network metrics were computed to measure the structure and properties of Twitter networks, using NodeXL. 7.2. Semantic network analysis The gatekeepers in each candidate’s issue network were identified based on their active participation and high centrality. In other words, users who continuously engaged in issue debates were classified during the first, second, and third debates. In the case of Lee, users who appeared during the first and second debates were identified due to her withdraw immediately after the second debate. Among the users, the top 10 gatekeepers were determined based on their indegree centrality such that they were most likely to receive mentions and replies from other users and be retweeted in the issue network. For Moon’s issue network, there were only 8 users who participated across the three debates, and therefore only these 8 were selected. A semantic network analysis (including a keyword analysis and a cluster analysis) was conducted to specify the gatekeepers’ key themes in framing the TV discussion. We obtained a total of 1517 tweets related to the debates generated by the top 28 gatekeepers (1112 tweets from Park’s gatekeepers, 241 tweets from Lee’s gatekeepers, and 164 tweets from Moon’s gatekeepers).

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The semantic network analysis is a meaning-centered network approach for examining the relationship between textual components of communication content (Cho et al., 2012; Park and Lee, 2009). The semantic analysis employed Krkwic (Korean Keyword in Context), a content analysis software package based on network algorithms for classifying frequently used keywords from large blocks of text (Park and Leydesdorff, 2004). In addition, a CONCOR (CONvergence of iterated CORrelations) analysis was conducted to reveal concurrently appearing words based on a ‘‘word  word” co-occurrence matrix because this technique can reveal hidden subgroups and examine the semantic structure of text (Cho et al., 2012; Park and Lee, 2009). This procedure was employed to obtain lists of words used frequently (more than three times) by gatekeepers in issue networks of Lee (a total of 133 words) and Moon (113). Park had a large list of frequently used words (more than seven times; 245 words) because of the large number of tweets related to Park. To visualize the network, UciNet was used (Borgatti et al., 2002). 7.3. User profile analysis A total number of 1517 tweets generated by the gatekeepers during the debates were analyzed to determine the gatekeepers’ political ideologies. In most cases, their tweets reflected the user’s political stance and attitude towards the candidates. We used this as proxy of their political ideologies and classified political ideologies into three categories: conservative, progressive, or neutral. In addition, gatekeepers’ demographic information (e.g., occupation and location) and Twitter activities (e.g., the number of followers, the number of followings, and the number of tweets) indicated in their profiles were gathered. 8. Results 8.1. Issue networks over time RQ1 asks how users construct the issue networks in the context of TV debates during the 2012 presidential election in Korea over time. To answer this research question, the structural characteristics of the issue networks were mapped. Figs. 1–3 represent the issue networks in response to the first, second, and third TV debates, respectively. It illustrates the interaction among users in terms of being retweeted, replied, or mentioned. In the figures, each node indicates the user’s Twitter ID, and the line between two nodes represents their interactions. Node size refers to indegree centrality, and the color of the node indicates outdegree centrality (red: above 51; green: 31–50; orange: 11–30; blue: below 10). The higher the indegree centrality, the more likely the user was to be mentioned, replied to, and retweeted by other users, whereas the higher the outdegree centrality, the more likely the user was to mention, reply to, and retweet others’ tweets. As shown in Figs. 1–3, users generally constructed messy networks across the debates, but they formed more interwoven networks for the second debate. A majority of blue nodes and a few orange nodes across the debates suggest no unexpected differences between users in terms of their outdegree centrality. However, there were substantial differences in their indegree centrality based on various node sizes.

Fig. 1. The issue network for the first debate.

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Fig. 2. The issue network for the second debate.

Fig. 3. The issue network for the third debate.

Table 1 Network metrics for issue networks over time.

1st debate 2nd debate 3rd debate

Maximum distance

Average distance

Centrality

13 15 17

5.06 4.99 6.11

2.23 2.46 1.90

In the first debate, 7181 users generated 11,025 relationships (RT: 96.78%, replies: 2.70%, and mentions: 0.52%) and in the second debate, 7149 users produced 11,822 tweets related to other users (RT: 97.40%, replies: 2.22%, and mentions: 0.37%). Finally, 2347 users generated the 5138 relationships (RT: 97.57%, replies: 2.12%, and mentions: 0.31%). This result suggests that the number of participants and user interactions were similar in the first debate and the second debate. However, they

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were decreased sharply in the last debate. Noteworthy is that this tendency is consistent with the sharp drop in the viewership in the final debate, the one after Lee’s withdrawal. The network metrics represented Table 1 also supports this representation. The issue network in response to the final debate was relatively less centralized, and the distance among users was larger than the other two networks. This suggests the relatively little audience interacted with each other on the final TV discussion because of Lee’s withdrawn after the first debate. These results reflect viewers’ attention to and interest in Lee, who was considered an extremely progressive politician. Noteworthy is that her strident performance in the debate triggered burning responses from Twitter users. The large number of retweets and relatively small number of replies and mentions suggest that a majority of interactions between users occurred by citing other users’ content, not by engaging in discussions with one another. The data set suggested that the most of the RT used in gatekeepers’ tweets were unedited RT rather than commented RT. This finding suggests that while a significant number of users produced tweets on political events, a few users’ voices were likely to be circulated. This supports the idea that Twitter functions as eco-chamber in which dominant opinions are reinforced (Kim and Park, 2012). This results also replicate the popularity of second debate in TV, illuminating that social network on Twitter reflects the viewership trend of TV. Furthermore, it implicates that the structural characteristics of issue networks provide a hint of issue salience of the public. 8.2. Candidates’ issue networks RQ2 addresses how users interact with each other in the response of political candidates. Figs. 4–6 map the issue networks of Park, Lee, and Moo, respectively. In the figures, each node refers to the user’s Twitter ID, and the connection between two nodes represents their interactions in terms of mentions, replies, and retweets. Node size refers to indegree centrality, and the color of the node indicates word frequency in the issue network. For instance, orange nodes refer to users who participated in one debate; green nodes, those participating in two debates; and red ones, who engaged in all three debates. The network was visualized in accordance with the Harel–Koren fast multiscale algorithm in NodeXL. The results represented in Figs. 4–6 and Table 2 suggest the structural characteristics of the issue networks and the interaction pattern among the users were differed in terms of the candidates’ political ideologies. In other words, users formed distinctive issue networks in the response of political candidates who have different political ideologies. In Park’s issue network, 7103 users generated 8010 retweets (98. 23%), 122 replies (1.50%), and 22 mentions (0.27%). The largest number of communicators engaged in the conservative Park’s network compared to other candidates. As shown in Fig. 4, conservative Park’s network suggests that users constructed an organized and hierarchical issue network. This suggests that there were a few gatekeepers who can control the issue network. In addition, productive users who continuously

Fig. 4. Park’s issue network.

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Fig. 5. Lee’s issue network.

Fig. 6. Moon’s issue network.

participated in the issue network played a role as hubs in terms of user interactions. Red nodes indicate those users who were consistently engaged in Park’s network. These users had more communication power and were more centrally positioned in the network than other users who temporarily participated in the issue network. In other words, their tweets were more likely to be retweeted and induce responses of others. In Lee’s network, a total of 6292 users produced 7561 retweets (96.73%), 208 replies (2.66%), and 48 mentions (0.61%). Noteworthy is that, as shown in Fig. 5, Lee’s issue network was unique in terms of its topology. As shown in Fig. 5, users

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S.J. Park et al. / Telematics and Informatics 33 (2016) 557–569 Table 2 Network metrics for the candidate’ network.

Park Lee Moon

Maximum distance

Average distance

Centrality

22 3 10

8.20 0.91 3.72

0.65 0.62 0.97

who tweeted about Lee formed an extremely cohesive network and were more connected to one another than those in Park’s and Moon’s networks (see Figs. 4 and 5). The cohesive network indicates that users attempted to actively share their opinions for the progressive candidate who was salient in the TV debate. As shown in Fig. 6, Moon’s issue network indicates that those users with more communication power were not necessarily productive, which differs from the case of Park’s network. In Moon’s network, a total of 5328 users generated 5707 retweets (98.24%), 78 replies (1.34%), and 24 mentions (0.41%). Given that he was the major opposition candidate against Park and that he had strong public support comparable to that for Park, there were substantial differences in the number of users and user interactions between Moon and Park. 8.3. Identification of gatekeepers and their key frames 8.3.1. Gatekeepers in Park’s network To investigate the role of gatekeepers in diffusing and shaping the issue networks, RQ3 and 4 address who the gatekeepers are and how they frame the debates. A majority of the gatekeepers who tweeted about a conservative candidate Park had progressive ideologies and sent tweets to criticize her debate performance or qualifications as a president. Table 3 shows their demographic characteristics and political ideologies. Automatically scrapped user locations suggest that 8 of the 10 gatekeepers were from Seoul, the capital of Korea. This is consistent with the findings of Takhteyev et al. (2012), who found that Twitter networks tend to be homogenous in terms of their geography, and provides support for the reinforcement tendency between in-groups and out-groups in which likeminded people with similar political ideologies interact with one another within issue networks (Yardi and Boyd, 2010). Another noteworthy is that two gatekeepers in Park’s network were publicly famous people. For example, one account was a news organization and one account was a popular novelist. This result confirmed that individuals who have higher publicity and reputation in the reality are more likely to play roles as gatekeepers on social media. The results of the CONCOR analysis classify eight themes from words frequently used by the gatekeepers. The eight themes were listed in order of the volume of key words: ‘‘policies on labor and employment issues”, ‘‘scandalous stories about political corruption and policies on economic progress”, ‘‘Park’s facial expression and defense against Lee’s attack”, ‘‘political scandals surrounding political corruption and financial problem”, ‘‘criticisms of dictator Park”, ‘‘Satire on Park’s political attitudes and stance”, ‘‘doubts about leadership and qualifications as a president”, and ‘‘Park’s slip of the tongue”. As demonstrated by these themes, a majority of tweets focused on scandalous stories suggested by Lee and Park’s inappropriate defense with embarrassed facial expressions. This result suggests that progressive gatekeepers generate tweets to devaluate Park’s quality as the President. 8.3.2. Gatekeepers in Lee’s network The gatekeepers who sent tweets about Lee were mixed in terms of their political ideologies (extremely conservative vs. progressive). Table 4 summarizes their political ideologies and demographic features. Noteworthy is that, in the case of Lee’s network, a majority of the gatekeepers lived in Seoul, and some were from Hawaii. The listed gatekeepers include one progressive news organization, resonating the result of existing study that a news organization still exercise issue dissemination power on social media (Hsu et al., 2013). Not surprisingly, the gatekeepers with progressive ideologies were gratified with her attacks against Park, whereas conservative gatekeepers criticized her inappropriate attitudes as a debate participant. The results of the CONCOR analysis Table 3 Profiles of gatekeepers in Park’s network. User ID

Political ideology

#Followers

#Followings

#Tweets

Location

sisain_editor congjee tak0518 glimcooa shanti_u leeyhlove sunboy14 sssswwwwhh yoji0802 ccmlove69

Neutral Progressive Progressive Progressive Progressive Progressive Progressive Progressive Progressive Progressive

210,872 514,096 150,664 122,618 14,732 7064 6673 10,678 34,241 11,820

83,014 866 1133 74,544 16,158 6951 5902 8251 34,474 15,190

7316 38,715 32,142 5828 23,927 31,871 21,098 44,057 39,627 15,190

Seoul Seoul Seoul Seoul Seoul Seoul Seoul N/A Seoul Hawaii

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S.J. Park et al. / Telematics and Informatics 33 (2016) 557–569 Table 4 Profiles of gatekeepers in Lee’s network. User ID

Political ideology

#Followers

#Followings

#Tweets

Location

congjee woon_hyang moa4703 kyunghyang ggabeto miso_smile_ hueah_ amen061 _hosi Madam999

Progressive N/A Conservative Neutral Conservative N/A N/A Progressive N/A Progressive

514,201 2043 5357 137,134 60,183 320 121 5773 465 10,947

866 1430 4015 102,911 64,728 393 121 5564 682 10,304

38,759 53,585 64,670 20,543 10,160 53,181 62,471 32,167 27,550 17,740

Seoul Seoul N/A Seoul Seoul Hawaii Seoul N/A Hawaii Seoul

Table 5 Profiles of gatekeepers in Moon’s network. User ID

Political ideology

#Followers

#Followings

#Tweets

Location

du0280 kaelshin bluenlive kittysister stockgzon ricej4u nofta1122 OLYMPIAD60

Progressive N/A Progressive Progressive Progressive Progressive Progressive Progressive

69,632 424 6386 3439 2520 383 2748 800

52,441 597 3345 931 2575 274 1832 465

54,095 72,136 73,778 104,017 1735 4914 30,664 13,625

Seoul Seoul Seoul Hawaii Hawaii N/A Hawaii Seoul

provide eight themes based on words frequently used by the gatekeepers. The themes were listed in order of the volume of key words: ‘‘the satire in her utterances”, ‘‘inappropriate attitudes in the political debate,” ‘‘her criticism of Park,” ‘‘humanism,” ‘‘her attacks against Park,” and ‘‘confrontations between Lee and Park”, ‘‘her acrid attitudes in the debate,” ‘‘her personal background as a lawyer.” In general, the tone of their frames was polarized with respect to her discussion ability and qualification as a presidential candidate. 8.3.3. Gatekeepers in Moon’s network As shown in Table 5, a majority of the gatekeepers in Moon’s issue network had progressive ideologies and were from Seoul and Hawaii. One of the gatekeepers was online independent news organization, suggesting that a non-mainstream can take an advantage to be a gatekeeper in issue diffusion by actively communicating with others on social media. Unlike in the cases of Park and Lee, tweets about Moon were generally messages of support and reflected favorable sentiments toward the candidate. The results of the CONCOR analysis provided eight themes based on words frequently used by the gatekeepers. The eight themes were listed in order of the volume of key words: ‘‘supportive messages and hope for a victory”, ‘‘a praise for his verbal capability”, ‘‘his humanism”, ‘‘the level of support and the status of competition”, ‘‘democracy and participatory government”, ‘‘a parody of Lee’s utterances”, ‘‘facial expressions during the debate”, ‘‘little visibility in debates”. In the key themes, encouraging messages toward him was dominant and there was little criticism. This implicates that his mild attitude and liberal political ideologies were likely to be favored and praised among Twitter users. These results suggest that, although the election was very close between Park and Moon in terms of the level of support and expected voting outcomes, the entertaining debates and conflicts between Park and Lee attracted public attention mainly to their confrontations and resulted in parodies and negative sentiments toward them instead of motivating deliberate public discourse. 9. Discussion and conclusions This study investigates the role of Twitter issue networks in reflecting how an election unfolds by considering the context of TV debates during the 2012 presidential election in Korea based on structural characteristics of issue networks and gatekeeping practices. According to the results, the issue networks tended to evolve in accordance with trends in the TV debate viewership, suggesting that the consumption of the mass media for political purposes is correlated with political participation on social media. This data confirm ‘‘spill over” effects of traditional mainstream media into social media (Mathes and Pfetsch, 1991) and cross-media use of TV and Twitter (Doughty et al., 2011; Harrington et al., 2012). In other words, it shows that issue salience mediated by television was translated to the activeness of public discussion and interaction among users. It also implicates the necessity of examining real-time response data to trace audience attention and interests on critical political issues. This suggests that a Twitter issue network may serve as a quantitative indicator of the public’s interest and issue salience.

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In addition, there were differences in how users formed issue networks in response to candidates’ ideologies. Park, the candidate of the ruling party, had a hierarchical network consisting of a number of communicators, whereas Moon, the main opposition candidate, had a relatively loose network with a limited number of connectors. Noteworthy is that Lee, a radical opposition candidate, had a dense and cohesive network. This implies that viewers were more responsive to sensational information and controversial candidates. According to the profile analysis of gatekeepers and semantic patterns of tweets, a majority of gatekeepers were homogenous and politically biased, that is, either conservative or progressive individuals with substantial online and offline social capital. These users tended to make the political debate more polarized and emotional (supportive or critical). This suggests that a Twitter issue network may reflect like-minded users’ connections and sharing of opinions. In addition, Park’s network was dominated by negative comments from out-groups with progressive ideologies, whereas Moon had a supportive network from in-groups with similar ideologies. Noteworthy is that Lee’s network was polarized in terms of gatekeepers’ attitude and sentiments. Further, the results suggest that gatekeepers tended to criticize conservatives and favored progressives. This is consistent with the findings of previous research on Twitter-based political communication in Korea. That is, Twitter may be an echo chamber that reinforces polarization in opinion formation (Kim and Park, 2012a,b; Yardi and Boyd, 2010). This raises a skeptical question about the democratic function of social media. Another interpretation of this finding is that tweets expressing some clear political stance and attitudes are more likely to be circulated among users than neutral ones. Although the structural characteristics and networking patterns of users illuminate the potential role of Twitter as a qualitative indicator of the public’s interest and issue salience, the inconsistency between the nuance embedded in gatekeepers’ tweets about candidates and election outcomes casts some doubt on the predictive power of Twitter to reflect the offline political landscape. Previous studies of Twitter have generally employed a single method (e.g., a survey, an interview, or a network analysis), but this study takes a mixed approach combining network analyses and an audience profile analysis. In addition, the study contributes to the literature by providing empirical data for a better understanding of political communication on social media in a non-Western context. However, the study has several limitations. The analyses were conducted on a real-time basis and thus did not capture Twitter activities before and after the debates that might be still useful to explore the gatekeeping process. Another limitation of the data collection is that the Search Twitter API software employed only provided snapshots of the data that possibly excluded other relevant tweets. The keyword-based data collection approach may also result in the same problem. In addition, analysis of the relatively small number of gatekeepers’ profiles and tweets may result in biased information of gatekeepers, the results may not provide a general picture of opinion leadership in social media (Lee and Nai, 2010). To address these limitations, future research should consider longer analysis periods and larger samples. In addition, though this study shows a pattern of media use pattern between TV and social media, it cannot guarantee the Twitter users who participated in the issue networks watched TV debates. It might be vise versa. Future studies need to take account of this possibility and explore the cross-media use of TV and Twitter. References Ampofo, L., Anstead, N., O’Loughlin, B., 2011. Trust, confidence, and credibility. Inf. Commun. Soc. 14 (6), 850–871. Anstead, N., O’Loughlin, B., 2011. The emerging viewertariat and BBC question time: television debate and real-time commenting online. Int. J. Press/Politics 16 (4), 440–462. Ausserhofer, J., Maireder, A., 2013. National politics on twitter: structures and topics of a networked public sphere. Inf. Commun. Soc. 16 (3), 291–314. Bastos, M.T., Raimundo, R.L.G., Travitzki, R., 2013. Gatekeeping Twitter: message diffusion in political hashtags. Media Cult. Soc. 35 (2), 260–270. Borgatti, S.P., Everett, M.G., Freeman, L.C., 2002. Ucinet for Windows: Software for Social Network Analysis. Analytic Technologies, Harvard, MA. Bruns, A., 2005. Gatewatching: Collaborative Online News Production. Peter Lang, New York. Bruns, A., Burgess, J., 2012. Researching news discussion on Twitter: new methodologies. J. Stud. 13 (5–6), 801–814. Bruns, A., and Eltham, B., 2009. Twitter free Iran: an evaluation of Twitter’s role in public diplomacy and information operations in Iran’s 2009 election crisis. In: Communications Policy & Research Forum 2009, 19th–20th November 2009, University of Technology, Sydney (November). Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, K.P., 2010. Measuring user influence in Twitter: the million follower fallacy. In: Paper presented at the 4th International AAAI Conference on Weblogs and Social Media (ICWSM), Washington DC, USA (May). Chang, D.R., 2010. Twitter’s Surprising Impact on the South Korean Election. Harvard Business Review. Retrieved from: (June 11). Chang, D., and Bae, Y., 2012. The birth of social election in South Korea, 2010–2012. Friedrich Ebert Stiftung. Retrieved from: . Chang, W.Y., Park, H.W., 2012. The network structure of the Korean blogosphere. J. Comput.-Mediated Commun. 17, 216–230. Chin, A., and Chignell, M., 2006. A social hypertext model for finding community in blogs. In: Proceedings of the Seventeenth Conference on Hypertext and Hypermedia, ACM, New York, NY, pp. 11–22. Cho, S.E., Park, H.W., 2012. Government organizations’ innovative use of the Internet: the case of the Twitter activity of South Korea’s Ministry for Food, Agriculture, Forestry and Fisheries. Scientometrics 90 (1), 9–23. Cho, S.E., Choi, M.G., Park, H.W., 2012. Government–civic group conflicts and communication strategies: a text analysis of TV debates on Korea’s import of U. S. beef. J. Contemp. East. Asia 11 (1), 1–20. Ciulla, F., Mocanu, D., Baronchelli, A., Goncalves, B., Perra, N., Vespignani, A., 2012. Beating the news using Social Media: the case study of American Idol. EPJ Data Sci. 1, 8. http://dx.doi.org/10.1140/epjds8. Doughty, Mark, Rowland, Duncan, Lawson, Shaun, 2011. Co-viewing live TV with digital backchannel streams. In: 9th European Conference on Interactive TV and Video (EuroITV’11), June 29–July 1, 2011, Lisbon, Portugal. Gladwell, M., 2010. Small change. New Yorker 4, 108–111. Harrington, S., Highfield, T., Bruns, A., 2012. More than a backchannel: Twitter and television. In: Noguera, José Manuel (Ed.), Audience Interactivity and Participation. COST Action ISO906 Transforming Audiences, Transforming Societies, Brussels, Belgium, pp. 13–17.

S.J. Park et al. / Telematics and Informatics 33 (2016) 557–569

569

Himelboim, I., Hansen, D., Bowser, A., 2013. Playing in the same Twitter network: political information seeking in the 2010 US gubernatorial elections. Inform. Commun. Soc. 16 (9), 1373–1396. Hsu, C.L., Park, S.J., Park, H.W., 2013. Political discourse among key Twitter users: the case of Sejong city in South Korea. J. Contemp. East. Asia 12 (1), 65–79. Kim, J.S., 2012. South Korean presidential candidates hold final TV debate. Press TV. Retrieved from: (December 17). Kim, J., and Park, J.M., 2012. Far left outsider ‘‘winner” in Korean presidential debate. Reuters. Retrieved from: (December 5). Kim, M., Park, H.W., 2012b. 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. Kobayashi, T., Boase, J., 2012. No such effect? The implications of measurement error in self-report measures of mobile communication use. Commun. Methods Meas. 6, 1–18. http://dx.doi.org/10.1080/19312458.2012.679243. KoreaBANG, 2012. South Korean presidential election debates, netizen reactions. KoreaBang. Retrieved from: (December 19). Kwak, N., Campbell, S.W., Choi, J., Bae, S.Y., 2011. Mobile communication and public affairs engagement in Korea: an examination of non-linear relationships between mobile phone use and engagement across age groups. Asian J. Commun. 21 (5), 485–503. Kwon, K.H., Oh, O., Agrawal, M., Rao, H.R., 2012. Audience gatekeeping in the Twitter service: an investigation of tweets about the 2009 Gaza conflict. AIS Trans. Hum.–Comput. Interact. 4 (4), 212–229. Lazarsfeld, P.F., Berelson, B., Gaudet, H., 1944. The People’s Choice: How the Voter Makes Up His Mind in a Presidential Campaign. Columbia University Press, New York. Leavitt, A., Burchard, E., Fisher, D., and Gilbert, S., 2009. The influentials: new approaches for analyzing influence on Twitter. A publication of the Web Ecology Project. Retrieved from: . Lee, T.H., and Shin, S.K., 2012. Progressive candidate steals debate show by blasting Park Geun-hye. The Hankyoreh. Retrieved from: (December 5). Lee, S., Nai, G., 2010. Audience activity in micro-blog: exploring production activity in Twitter and Me2day. Korean Assoc. Broadcast. Telecommun. Stud. 73, 171–200. Lee, Y.O., Park, H.W., 2013. E-campaigning versus the public official election act in South Korea: causes, consequences and implications of cyber-exile. Aslib Proc. 65 (4), 388–405. Lotan, G., Graeff, E., Ananny, M., Gaffney, D., Pearce, I., 2011. The Arab Spring| the revolutions were tweeted: information flows during the 2011 Tunisian and Egyptian revolutions. Int. J. Commun. 5, 31. Mathes, R., Pfetsch, B., 1991. The role of the alternative press in the agenda-building process: spill-over effects and media opinion leadership. Eur. J. Commun. 6 (1), 33–62. Nam, Y., Lee, Y.O., Park, H.W., 2013. Can web ecology provide a clearer understanding of people’s information behavior during election campaigns? Soc. Sci. Inf. 52 (1), 91–109. Otterbacher, J., Shapiro, M.A., Hemphill, L., 2013. Interacting or just acting? A case study of European, Korean, and American politicians’ interactions with the public on Twitter. J. Contemp. East. Asia. 12 (1), 5–20. Okumura, M., Motegi, T., Kobayashi, T., Oyama, K., and Suzuki, T., 2007. Can we predict political poll results by using blog entries? In: IEEE 2012 45th Hawaii International Conference on System Sciences. doi: http://dx.doi.org/10.1109/HICSS.2012.145. Park, H.W., 2013. Mapping election campaigns through negative entropy: Triple and Quadruple Helix approach to South Korea’s 2012 presidential election. Scientometrics, 1–11. Park, H.W., 2014. Mapping election campaigns through negative entropy: Triple and Quadruple Helix approach to South Korea’s 2012 presidential election. Scientometrics 99, 187–197. http://dx.doi.org/10.1007/s11192-013-1122-5. Park, H.W., Lee, Y., 2008. The Korean presidential election of 2007: five years on from the ‘‘Internet Election”. J. Contemp. East. Asia 7 (1), 1–4. Park, H.W., Lee, Y., 2009. A mixed text analysis of user comments in a portal site: ‘BBKgate’ in the 2007 presidential election of South Korea. J. Korean Data Anal. Soc. 11 (2), 731–744. Park, H.W., Leydesdorff, L., 2004. Understanding the KrKwic: a computer program for the analysis of Korean text. J. Korean Data Anal. Soc. 6 (5), 1377–1387. Park, H.W., Thelwall, M., 2006. Web-science communication in the age of globalization. New Media Soc. 8 (4), 629–650. Park, H.W., Thelwall, M., 2008. Developing network indicators for ideological landscapes from the political blogosphere in South Korea. J. Comput.-Mediated Commun. 13 (4), 856–879. Park, S.J., Lim, Y.S., Sams, S., Nam, S.M., Park, H.W., 2011. Networked politics on Cyworld: the text and sentiment of Korean political profiles. Soc. Sci. Comput. Rev. 29 (3), 288–299. Pearanalytics, 2009. Twitter Study. Retrieved from: http://www.pearanalytics.com/ wp- content/uploads/2009/08/Twitter-Study-August- 2009.pdf. Romero, D., Galuba, W., Asur, S., Huberman, B., 2011. Influence and passivity in social media. Mach. Learn. Knowl. Discov. Databases, 18–33. Salganik, M.J., and Levy, K.E.C., 2012. Wiki surveys: open and quantifiable social data collection. . Sams, S., Park, H.W., 2014. The presence of hyperlinks on social network sites: A case study of Cyworld in Korea. J. Comput.-Mediated Commun. 19 (2), 294– 307. Skoric, M.M., 2012. Social media messages as indicators of public opinion: opportunities and challenges. In: A Keynote Talk at New Media and Citizenship in Asia: Social Media, Politics, and Community-building, International Communication Association 2012 Preconference. Pheniox, AZ, USA. Takhteyev, Y., Gruzd, A., Wellman, B., 2012. Geography of Twitter networks. Soc. Networks 34 (1), 73–81. The Korea Herald, 2012. Korea’s high-speed wireless Internet penetration tops 100 percent. The Korea Herald. Retrieved from: (July 22). Theocharis, Y., 2013. The wealth of (Occupation) networks? Communication patterns and information distribution in a Twitter protest network. J. Inf. Technol. Polit. 10 (1), 35–56. Tremayne, M., 2014. Anatomy of protest in the digital era: a network analysis of Twitter and Occupy Wall Street. Soc. Mov. Stud. 13 (1), 110–126. Tumasjan, A., Sprenger, T.O., Sandner, P.G., and Welpe, I.M., 2010. Predicting elections with Twitter: what 140 characters reveal about political sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 178–185 (May). Valente, T.W., Pumpuang, P., 2007. Identifying opinion leaders to promote behavior change. Health Educ. Behav. 34 (6), 881–896. Wall Street Journal, 2012. How did Park win? A breakdown. (December 20). Williams, C., and Gulati, G., 2008. What is a social network worth? Facebook and Vote Share in the 2008 presidential primaries. In: Annual Meeting of the American Political Science Association, 1–17. Boston, MA. Wohn, D.Y., Na, E.K., 2011. Tweeting about TV: sharing television viewing experiences via social media message streams. First Monday 16 (3), 1–29. Yardi, S., Boyd, D., 2010. Dynamic debates: an analysis of group polarization over time on Twitter. Bull. Sci. Technol. Soc. 30 (5), 316–327. Zhu, J.J., Mo, Q., Wang, F., Lu, H., 2011. A random digit search (RDS) method for sampling of blogs and other user-generated content. Soc. Sci. Comput. Rev. 29 (3), 327–339.