Analyzing social media networks with NodeXL - Chapter- 09 Images

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Analyzing social media networks with NodeXL - Chapter- 09 Images

  1. 1. 1Copyright © 2011, Elsevier Inc. All rights Reserved Chapter 9 Thread Networks Mapping Message Boards and Email Lists Analyzing Social Media Networks with NodeXL Insights from a Connected World
  2. 2. 2Copyright © 2011, Elsevier Inc. All rights Reserved FIGURE 9.1 Chapter9 Threaded conversation diagram showing five threads that are part of two different topics. Each post includes a subject (e.g., Thread A), a single author (e.g., Adam), and a time stamp (e.g., 12/10/2010 2:30 pm). Indenting indicates placement in the reply structure. Green posts initiate new threads (i.e., they are top-level threads), yellow posts reply directly to green posts, orange posts reply to yellow posts, and the pink post replies to the orange post.
  3. 3. 3Copyright © 2011, Elsevier Inc. All rights Reserved FIGURE 9.2 Chapter9 A n example discussion Reply network graph displayed in NodeXL, based on the data found in Figure 9.1. The network is constructed by creating an edge pointing from each replier to the person he or she replied to and then merging duplicate edges. Notice that Beth has replied directly to Dave twice, so the edge connecting them is thicker. Fiona replied to her own message, so there is a self-loop. Greg started a thread but was not replied to. He would normally not show up on the graph because he is not in the edge list; however, he was manually added to the Vertices tab and his visibility was set to “Show,” so he would appear.
  4. 4. 4Copyright © 2011, Elsevier Inc. All rights Reserved FIGURE 9.3 Chapter9 A Top-Level Reply network graph displayed in NodeXL based on the data found in Figure 9.1 . The network is constructed by creating an edge pointing from each replier to the person who started the thread (i.e., posted the top-level message) and then merging duplicate edges. Notice how Cathy plays a more prominent role (i.e., has a higher in-degree) than in the standard Reply network graph (Fig. 9.2) because she started the longest thread and all subsequent repliers link to her. Self-loops are more frequent in this type of network because people like Cathy may respond to those who replied to her initially, leading to a self-loop.
  5. 5. 5Copyright © 2011, Elsevier Inc. All rights Reserved FIGURE 9.4 Chapter9 NodeXL’s Import from Email Network dialog showing two methods to restrict the network to messages sent to the email list. One method is to include messages with “[css-d]” in the Subject line. The other method is to include messages sent to the list address (css-d@lists.css-discuss.org). Messages are also filtered to only include 2 months of data: January and February of 2007.
  6. 6. 6Copyright © 2011, Elsevier Inc. All rights Reserved FIGURE 9.5 Chapter9 NodeXL Subgraph images (1.5 degree; vertex and incident edges highlighted red) for six CSS-D contributors that fill three different social roles within the CSS-D community. Answer people predominantly reply to questions from isolates (i.e., those who are not connected to others). Question people typically have a low degree themselves, but they receive messages from those with high degree (i.e., answer people). Discussion starters initiate long threads and receive many replies, often from people who know each other.
  7. 7. 7Copyright © 2011, Elsevier Inc. All rights Reserved FIGURE 9.6 Chapter9 NodeXL map of the CSS-D email list network for January through February of 2007 after removing the vertex for the email list address itself. Answer people (greener) and discussion starters (redder) are identified by the calculated answer person score (see Advanced Topic: Social Role Measures). Blue vertices have a total degree of fewer than 15. Subgraph images (1.5) of the top four discussion starters are shown. Vertex size is mapped to eigenvector centrality. Edge weight is mapped to both edge size (1.5 – 4) and opacity (20 – 80), applying the logarithmic scale and ignoring outliers. Like many help-based communities, CSS-D consists of mostly question askers with a handful of answer people and discussion starters.
  8. 8. 8Copyright © 2011, Elsevier Inc. All rights Reserved FIGURE 9.7 Chapter9 NodeXL map of the filtered version of the CSS-D email list seen in Figure 9.6 showing only the most central members. The maximum size of vertices and edges has been increased to more clearly draw comparisons.
  9. 9. 9Copyright © 2011, Elsevier Inc. All rights Reserved FIGURE 9.8 Chapter9 NodeXL map of ABC-D’s email list reply network. The current administrator (Admin) and other members that are most central to the network are labeled. Larger vertices have a higher eigenvector centrality and darker purple vertices have a higher betweenness centrality. Subgraphs are shown in the worksheet to indicate each member’s local 2-degree network neighborhood.
  10. 10. 10Copyright © 2011, Elsevier Inc. All rights Reserved FIGURE 9.9 Chapter9 NodeXL map of ABC-D’s email list reply network with the current administrator removed from the network. Graph metrics are recalculated without the administrator. Size and color mean the same thing as the prior graph (Fig. 9.5), and vertex locations have been fixed to facilitate comparison.
  11. 11. 11Copyright © 2011, Elsevier Inc. All rights Reserved FIGURE 9.10 Chapter9 Bimodal network connecting three Ravelry groups (i.e., forums) represented as blue text boxes to contributors represented as circles. Edge width is based on number of posts (with logarithmic mapping). Vertex size is based on number of completed Ravelry projects. Maroon vertices have a blog and solid circles are either community moderators or volunteer editors. The network helps identify important boundary spanners (e.g., those connected to multiple groups) as well as compare groups.

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