Exploring opinion leadership and homophily in political discussion networks of korean twitter users
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  • This study provides a network analysis of Twitter discussions about Myung-Bak Lee, South Korea’s former president, to gain a better understanding of the dynamics of public opinion exchanges on Twitter. When tweets including the Korean character “이명박” were collected for the study, “Lee, Myung-Bak” was the incumbent President of South Korea. Earlier this year, Park, Geun-hye became the eleventh and current President of South Korea. <br />
  • Many studies have argued that SNSs can be an effective tool for facilitating the public’s political engagement. It is not difficult to find real life examples of this relationship. However, some have questioned the relationship. And literature on this field has provided mixed findings. Some have claimed that social media have beneficial effects on political engagement, whereas others have found no significant relationship between the two. <br />
  • Given the importance of “opinion leaders” in disseminating ideas and information, the concept of opinion leadership has been widely adopted by a broad range of disciplines. Sociometric techniques, one of the useful approaches, retraces communication paths in a network, as well. According to this approach, if someone receives many choices, that person can be considered as an opinion leader. Scholars have tackled the question of how best to define and measure opinion leadership. <br /> Most of the studies on opinion leadership has examined the concept in the mass media environment, those findings might be different in the new media environment. Easy access to media and more resources to reach audiences … <br />
  • “The literature in homophily is not all one-sided in support of increased political polarizations.” A growing body of research has explored the homophily thesis in the context of Twitter. Twitter is different from blogs and Facebook. In the specific context of Twitter, whether and to what extent the homophily thesis is applied still remains an open debate given the relative newness of Twitter. Conover found high levels of clustering along party lines. The theory of selective exposure suggests that “individuals often systematically prefer information that is consistent with their beliefs, attitudes, or decisions and, in contrast, neglect inconsistent information. <br />
  • We focused on two research questions. We tried to identify the opinion leaders and validate whether Korean Twitter users are likely to cluster around similar political interests. <br />
  • We used Twitter’s API to retrieve data from Twitter. The application for data collection automatically retrieved data from Twitter for those who mentioned “Lee, Myung-Bak” and others who followed them. Since Twitter has set a limitation in providing the information, we refused to retrieve data on Twitter when we had too many requests. <br />
  • indegree centrality, which is based on the number of ties received from other actors .We used UCINET 6 to calculate densities within and between cluster. For binary data, density is calculated as (the number of lines in the network) / (the number of nodes) * (the number of nodes – 1) (Wasserman & Faust, 1994) in an inner-group. And, for valued data, density is defined as the sum of the values of all present lines divided by the number of possible lines. In this study, network density indicates the proportion of ties formed through “following,” “mention,” and “retweet” by Twitter users who sent Tweets about Myung-Bak Lee. <br />
  • To report their demographic information, we used search engines and checked their Twitter profiles. <br />
  • For a more in-depth analysis of these actors, we examined those who showed the highest indegree centrality in the discussion network at least two times. The results indicate that among these 25 users, 9 appeared more than twice.     <br />
  • Here you can see a clear pattern. Blue nodes represent liberal users, and red ones, conservative ones. Although it is difficult to see black nodes in the figure, they represent those users with a neutral political stance. Definitely, during the time period of analysis, liberals were more active and occupied large portion of the discussion network. <br />
  • It should be noted that there is an inverse relationship between size and density. Usually, as network size increases, density decreases. We examined the network density for liberal and conservative clusters  and between liberal and conservative clusters. To remove potential differences derived from node size , we calculated the nodes and lines through log values. <br />
  • ANOVA was conducted to determine if significant density differences existed among liberals, conservatives, and between liberals and conservatives. These results support the argument that Twitter networks are politically polarized. Based on our observation, we can say that Korean Twitter users are likely to cluster around shared political views. This result needs to be interpreted with caution since the size of cluster matters in calculating density. <br />
  • In this study, we found that liberal users were more actively participating in the discussion of the Korean President, Myung-Bak, Lee. The findings of the study confirm those of previous studies. Chang and Ghim (2011) This study also provided support for the argument that individuals were more likely to interact with like-mined individuals in terms of political viewpoints on the Internet <br />
  • In this study, we found that liberal users were more actively participating in the discussion of the Korean President, Myung-Bak, Lee. The findings of the study confirm those of previous studies. Chang and Ghim (2011) This study also provided support for the argument that individuals were more likely to interact with like-mined individuals in terms of political viewpoints on the Internet <br />

Exploring opinion leadership and homophily in political discussion networks of korean twitter users Exploring opinion leadership and homophily in political discussion networks of korean twitter users Presentation Transcript

  • Exploring Opinion Leadership and Homophily in Political Discussion Networks of Korean Twitter Users Yoonmo Sang (UT-Austin), Myunggoon Choi (Sungkyunkwan U), Hanwoo Park (Yeungnam U) December 14, 2013
  • SNSs and Political Discourse  Platform for political engagement  The relationship between social media use and political engagement  Mixed result (see Kushin & Yamamoto, 2010) • Beneficial effects on political engagement (e.g., Kim & Geidner, 2008; Valenzuela, Park, & Kee, 2009) • Questioning SNSs’ role in facilitating political engagement (e.g., Gil de Zuniga, Puig, & Rojas, 2009; Zhang, Johonson, Seltzer, & Bichard, 2010)
  • Opinion Leadership in Political Communication  Two-step flow theory (Katz & Lazarsfeld, 1955/2006)  Diffusion studies (Rogers, 2003; Vishwanath & Barnett, 2011)  Sociometric approach to opinion leadership (Monge & Contractor, 2003; Rogers, 2003; Valente & Pumpuang, 2007; Valente, 2010)  Opinion leadership in Twitter networks (Wu, Hofman, Mason, & Watts, 2011)
  • Divergent Findings on homophily and selective exposure • Bimber (2004) • • Mutz & Mondak (2006) • Brundidge (2010) • Adamic & Glance (2005) • Garrett et. al. (2011) • Sunstein (2007) • Gilbert & Karahalios (2009) McPherson, Smith-Lovin, & Cook (2001)  Uniqueness of Twitter necessitates further research on this issue.  Conover et al. (2011); Gruzd (2012); Himelboim, McCreery, & Smith (2013); Murthy (2012); Yardi & boyd (2010)
  • Research questions  RQ1: Who are the opinion leaders on Twitter in the discussion on President Myung-Bak Lee? Specifically, are opinion leaders on Twitter polymorphic or monomorphic through political events?  RQ2: Is the Twitter-based network broken down into subgroups with similar political interests?
  • Method
  • Data collection  Data collection period: from Nov. 1, 2011 to Apr. 20, 2012 NodeXL (the open-source network analytic tool which can collect and visualize network data)  UCINET6, Gephi for Data Analysis & Visualization  53, 165 Twitter users/ 1,144,306 lines (Three types: following, retweet, mention)  Moderate correlation between mutual and interaction networks (Gruzd et al., 2011)
  • Measurement  Opinion leadership : In-degree centrality * In-degree Centrality: The number of ties received from other actors  Network density within and between clusters (calculated by UCINET6) * Density in the study: The proportion of ties formed through “following,” “mention,” and “RT(retweet)” by Twitter users who sent tweets about Myung-Bak Lee
  • Measurement (Cont.)  Systematic steps to conduct the study No 1 Step Data collection - Description NodeXL using Twitter Search API 1.0 - All available tweets (Singleton, Retweet, and Mention) Korean president’s full name, “Myung-Bak Lee,” - From November 1, 2011 to April 20, 2012.
  • Measurement (Cont.)  Systematic steps to conduct the study No 2 Step Identifying opinion leaders - Description In-degree centrality Examining political views of opinion leaders by their twitter profiles and the People Search service offered by Naver
  • Measurement (Cont.)  Systematic steps to conduct the study No 3 Step Dissecting the networks - Description Modularity algorithm via Gephi Limiting to those days that show above average network density
  • Measurement (Cont.)  Systematic steps to conduct the study No 4 Step Determining the political inclination of each cluster Description Systematically selecting 10% of Twitter users with the highest in-degree centrality within each sub-cluster - Coding their political views by two independent coders and using Krippendorff’s α (Krippendorff, 2004) to assess inter-coder reliability
  • Measurement (Cont.)  Systematic steps to conduct the study No 5 Step Correlation of words’ frequencies between core and peripheral groups - Description Semantic analysis Comparing word frequency with regard to the tweets generated by 10% core nodes with the highest in-degree centrality, and by 90% the peripheral nodes Geul-Jap-I
  • Measurement (Cont.)  Systematic steps to conduct the study No 6 Step Interactions between the liberal and conservative cluster Description Densities between and within clusters (calculated by UCINET)
  • Results
  • Opinion Leaders in the Context of Twitter  The demographic characteristics (including occupation and political view) of influential Twitter users from their Twitter profiles and through search engines  Those who showed the highest indegree centrality in the discussion network at least two times  Liberal Twitter users’ considerable influence on the Twitter network (Hsu & Park, 2011; Hsu & Park, 2012)
  • Table 1. Opinion leaders in the discussion on Myung-Bak Lee No. Twitter ID No. of Twitterians with the highest indegree centrality Followings Followers Tweets Occupation Political view 1 User1 6 (12.24%) 17,391 17,596 17,484 Journalist Liberal 2 User2 6 (12.24%) 6,005 89,696 16,269 Researcher Liberal 3 User3 4 (8.16%) 381 267,531 15,012 Novelist Liberal 4 User4 4 (8.16%) 45,256 48,586 9,859 Media outlet Liberal 5 User5 4 (8.16%) 5,048 54,821 27,188 Journalist Liberal 6 User6 2 (4.08%) 220,879 202,123 51,645 Unknown Not clear 7 User7 2 (4.08%) 68,725 139,033 65,106 Journalist Liberal 8 User8 2 (4.08%) 24,830 28,476 11,370 Politician Liberal 9 User9 2 (4.08%) 25,156 43,290 15,776 Media outlet Liberal
  • Mapping Interactions Within and Between Subgroups Table 2. Pearson correlation coefficient Liberal clusters Conservative clusters Core 10% nodes-peripheral 90% nodes Core 10% nodes-peripheral 90% nodes 11/26/11 0.874*** 0.824*** 11/30/11 0.927*** 0.773*** 12/14/11 0.881*** 0.848*** 12/18/11 0.909*** 0.679*** 12/19/11 0.499*** 0.844*** 12/24/11 0.871*** 0.522*** 12/25/11 0.912*** 0.866*** 12/26/11 0.866*** 0.675*** 01/01/12 0.899*** 0.903*** 01/14/12 0.857*** 0.836*** Date (month/day/year)
  • Mapping Interactions Within and Between Subgroups (Cont.) Table 2. Pearson correlation coefficient Date (month/day/year) 01/15/12 01/21/12 01/22/12 01/27/12 01/29/12 02/09/12 03/10/12 03/18/12 03/24/12 03/30/12 Liberal clusters Conservative clusters Core 10% nodes-peripheral 90% nodes Core 10% nodes-peripheral 90% nodes 0.786*** 0.624*** 0.823*** 0.851*** 0.879*** 0.909*** 0.581*** 0.882*** 0.913*** 0.930*** 0.747*** 0.786*** 0.502*** 0.842*** 0.872*** 0.759*** 0.701*** 0.827*** 0.840*** 0.934***
  • Table 3. Result of Modularity Analysis Date (Mon/ Day No. of 11/30/11 12/14/11 12/18/11 12/19/11 12/24/11 12/25/11 12/26/11 01/01/12 01/14/12 01/15/12 01/21/12 01/22/12 01/27/12 01/29/12 02/09/12 03/10/12 03/18/12 03/24/12 03/30/12 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7 Liberal Conservative Clusters /Year) 11/26/11 Cluster1 (%) (%) (%) (%) (%) (%) (%) (%) (%) 6 36.67 18.62 17.58 10.68 8.98 1.04 - 82.89 10.68 4 32.28 25.48 23.75 13.69 - - - 69.72 25.48 4 33.85 29.95 26.3 3.12 - - - 56.25 33.85 6 26.91 18.6 14 12.47 11.82 10.94 - 67.83 26.91 4 38.03 22.14 17.6 17.41 - - - 73.04 22.14 6 35.62 21.14 12.34 11.48 11.37 3.54 - 91.95 3.54 4 43.77 21.22 19.98 11.8 - - - 84.97 11.8 5 38.55 19.74 18.91 16.96 3.15 - - 75.25 18.91 4 31.7 24.56 22.71 13.99 - - - 78.97 13.99 5 32.97 26.49 26 5.98 5.08 - - 90.54 5.98 6 32.6 25.64 13.59 8.73 7.07 5.64 - 84.54 8.73 6 20.08 16.94 16.45 15.87 14.79 10.87 - 78.55 16.45 5 22.43 21.11 20.36 19.49 11.4 - - 73.68 21.11 4 35.1 23.62 18.94 18.51 - - - 72.55 23.62 6 28.59 21.17 15.88 12.61 11.55 1.44 - 70.07 21.17 4 34.96 30.71 22.04 5.03 - - - 52.75 34.96 6 33.66 18.14 16.88 14.08 10.56 2.35 - 82.76 10.56 7 23.73 19.96 18.57 11.92 9.93 7.35 4.27 83.81 11.92 5 48.64 19.1 18.34 6.49 5.55 - - 91.63 6.49 4 40.22 31.84 24.85 1.8 - - - 65.07 31.84 Modularity 0.172 0.223 0.222 0.179 0.209 0.156 0.184 0.216 0.153 0.170 0.145 0.170 0.200 0.222 0.254 0.264 0.169 0.139 0.189 0.229
  • Table 3. Result of Modularity Analysis Date (Mon/ Day No. of 11/30/11 12/14/11 12/18/11 12/19/11 12/24/11 12/25/11 12/26/11 01/01/12 01/14/12 01/15/12 01/21/12 01/22/12 01/27/12 01/29/12 02/09/12 03/10/12 03/18/12 03/24/12 03/30/12 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7 Liberal Conservative Clusters /Year) 11/26/11 Cluster1 (%) (%) (%) (%) (%) (%) (%) (%) (%) 6 36.67 18.62 17.58 10.68 8.98 1.04 - 82.89 10.68 4 32.28 25.48 23.75 13.69 - - - 69.72 25.48 4 33.85 29.95 26.3 3.12 - - - 56.25 33.85 6 26.91 18.6 14 12.47 11.82 10.94 - 67.83 26.91 4 38.03 22.14 17.6 17.41 - - - 73.04 22.14 6 35.62 21.14 12.34 11.48 11.37 3.54 - 91.95 3.54 4 43.77 21.22 19.98 11.8 - - - 84.97 11.8 5 38.55 19.74 18.91 16.96 3.15 - - 75.25 18.91 4 31.7 24.56 22.71 13.99 - - - 78.97 13.99 5 32.97 26.49 26 5.98 5.08 - - 90.54 5.98 Modularity 0.172 0.223 0.222 0.179 0.209 0.156 0.184 0.216 0.153 0.170 0.145 0.170 0.200 0.222 0.254 0.264 0.169 0.139 0.189 0.229 6 6 5 4 6 4 6 During the period of analysis, liberal Twitter users 32.6 25.64 13.59 8.73 7.07 5.64 - 84.54 8.73 20.08 16.94 16.45 15.87 14.79 10.87 - 78.55 16.45 (M = 76.34, SD = 10.88) were clearly more active 22.43 21.11 20.36 19.49 11.4 - - 73.68 21.11 35.1 23.62 18.94 18.51 - - - 72.55 23.62 in discussing Myung-Bak Lee than conservative 28.59 21.17 15.88 12.61 11.55 1.44 - 70.07 21.17 34.96 30.71 22.04 5.03 - - - 52.75 34.96 ones (M = 18.01, SD = 9.48). 33.66 18.14 16.88 14.08 10.56 2.35 - 82.76 10.56 23.73 19.96 18.57 11.92 9.93 7.35 4.27 83.81 11.92 5 48.64 19.1 18.34 6.49 5.55 - - 91.63 6.49 4 40.22 31.84 24.85 1.8 - - - 65.07 31.84 7
  • Figure 1. Temporal changes in network structure over time
  • Figure 2. Longitudinal density for liberal and conservative clusters and between clusters
  • Mapping Interactions Within and Between Subgroups (Cont.)  ANOVA result: Significant difference among three groups (F(2, 57) = 24.8327, p < .001). - Conservatives: The most densely interconnected (M = 0.0508, SD = 0.0329) - Liberals: Less densely interconnected (M = 0.0360, SD = 0.0065) - Conservatives and liberals: Low level of interconnectedness (M = 0.0080, SD = 0.0026)
  • Discussion and conclusion  Limitations and future research  Conclusion
  • Thank you for listening… Myunggoon Choi Department of Interaction Science Sungkyunkwan University E-mail: myunggoon.choi@gmail.com