analyzing social media networks with nodexl - chapter-15 images

1Copyright © 2011, Elsevier Inc. All rights Reserved

Chapter 15

Wiki NetworksConnections of Creativity and Collaboration

Analyzing Social Media Networks with NodeXLInsights from a Connected World

2

Howard T. Welser is an Assistant Professor of Sociology at Ohio University, where he explores issues of social change and technology in courses on group processes, introduction to sociology, and research methods. His research investigates how micro-level processes generate collective outcomes, with application to status achievement in avocations, development of institutions and social roles, the emergence of cooperation, and network structure in computer mediated interaction. He has a Ph.D. in sociology from the University of Washington.

Patrick Underwood a PhD student in sociology at the University of Washington. His master's thesis investigates how online communities maintain cohesion and group boundaries and how online social interaction makes the transition to offline group action. He is primarily interested in how individuals form and maintain social interactions in online spaces. He is also interested in the growing impact of internet communications technologies upon "offline" life and the growing prominence of video games within popular culture.

Dan Cosley is an assistant professor of information science at Cornell University. His primary interest is helping groups make sense, use, and reuse of information, from motivating people to contribute more to communities like Wikipedia by mining their prior behavior in the group to supporting reminiscence by re-using content created in social media systems. He is also interested in the general problem of how to use theory, principles, and models to build and evaluate real systems. He has a Ph.D. in computer science from the University of Minnesota.

Derek L. Hansen is an Assistant Professor at Maryland’s iSchool and Director for the Center for the Advanced Study of Communities and Information (http://casci.umd.edu). He is also an active member of the Human Computer Interaction Lab (http://www.cs.umd.edu/hcil/). His research focuses on mass collaboration, consumer health informatics, alternate reality games (ARGs), and social network analysis and visualization of online interactions. Dr. Hansen has a PhD from the University of Michigan’s School of Information.

Laura W. Black is an Assistant Professor in the School of Communication Studies at Ohio University. She studies public deliberation, dialogue, and conflict in small groups and is specifically interested in how people tell and respond to personal stories during small group discussions. Her research on social media includes studies of decision making in Wikipedia, conflict management in an online public forum, and social support in the online weight loss community FatSecret. She has a Ph.D. in communication from the University of Washington.


FIGURE 15.1C

hapter 15

This article page from the English-language Wikipedia displays content and illustrates discussion, edit, and history tabs. These tabs are standard to most wiki systems and they provide access to edit records from which edge relationships and attributes can be measured.


FIGURE 15.2C

hapter 15

Wiki pages have a related history page that depicts the timing of every edit, indicates the editor or IP address responsible for the edit, provides space for a brief description of the edit, and displays links to the state of the page before and after the edit. History pages are important sources of network and attribute data in wiki systems.


FIGURE 15.3C

hapter 15

This article talk page is used to coordinate decisions about the best contents for the article page. The edits to this page are made by people who have an interest in the content page and are often made by people who actively edit the article page. This page shows evidence both of content-based discussion and the implementation of templates to encourage compliance with community editing norms.


FIGURE 15.4C

hapter 15

This page reports a partial history of edits made by a wiki user. These contribution pages are an important source of information about editors. This image also shows a drop-down menu with a range of page types or “namespaces” in Wikipedia and typical to many wikis. The tendency of editors to edit pages in certain namespaces and not others provides important clues about the roles they play in the wiki community.


FIGURE 15.5C

hapter 15

This study of wiki social networks used the full revision history of the Project Castle page in the Empire Wiki as both a definition of the community of interest and as a source of user IDs. We were interested in the roles played within the community of contributors to these pages. Therefore, when we scraped all of these history pages, we were sure to get all active contributors to this project. Starting from a list of URLs for Project history pages, the web scraping software returns an Excel sheet populated with all text that occurs after the edit date and prior to the (talk & Contribs) link.


FIGURE 15.6C

hapter 15

NodeXL uses spreadsheet columns to store attributes of each vertex and can be transformed using standard Excel formulas. In this case, we see a sample of some Empire Wiki editors’ overall activity and the proportion of pages that they edited that were related to Project Castle.


FIGURE 15.7C

hapter 15

NodeXL allows you to assign gradients of vertex colors that correspond with data attributes in the spreadsheet. This helps make the resulting graph easier to read and analyze and highlights key features of interest.


FIGURE 15.8C

hapter 15

This NodeXL wiki network graph shows a well defined outer ring of users and a strong inner core. Only a handful of vertices connect the outer ring to the inner core. Without these nodes, the population would be highly fragmented.


FIGURE 15.9C

hapter 15

The NodeXL wiki network on the left displays the relative proportions of Project Castle edits among users sampled. Dark green indicates the lowest proportion of edits, and light green is the highest. The figure on the right displays the volume of edits to the users’ respective user pages. Dark blue indicates the lowest edit volume, and light blue represents the highest edit volume. Users who connect the outer ring to the inner core in the previous visualization have few Project Castle edits, and those users who display a high volume of edits are relatively isolated in the previous visualization. This indicates that Project Castle is not strongly connected to the larger Empire Wiki community.


FIGURE 15.10C

hapter 15

This figure compares the degree 1.5 ego network graphs of four different exemplary types of Project Castle contributors. Ego network graphs with automated layouts are good ways to identify potential structural signatures of online roles. In this instance, we see evidence that system administrators tend to have more connection to others involved in the project than do the actual substantive experts. Interestingly, for both sysops and substantive contributors, the higher-level contributors tend to have fewer connections.


FIGURE 15.11C

hapter 15

NodeXL can make use of the full range of Excel 2007 features, for example, using an “if-statement” to assign vertex color according to a categorical defi nition of low, medium, and high. A categorical assignment like this one is used to highlight large differences in the measured attribute. In this case, we can concentrate on the difference between contributors who are actively improving the quality of the discussion (green) from those who are actively undermining it (red).


FIGURE 15.12C

hapter 15

This NodeXL network graph depicts user-to-user talk page connections from a Wikipedia policy article. The graph illustrates one way that styles of contribution are tied to structural attributes. Note that the red nodes (most confrontational) are involved in the strongest dyadic ties, and they tend to have the highest outdegree. In contrast, the most deliberative contributors tend to have fewer partners and do not necessarily involve themselves in intense dyadic interactions. Observations like these can provide direction for further research that statistically tests the strength of these observer relations. Ultimately, if those measures are robust predictors, they could be used in automated systems for identifying more or less collaborative contributors, assessing community health, and deciding where interventions or support might be most helpful.


FIGURE 15.13C

hapter 15

Lostpedia’s article about the Statue of Taweret with links to its associated Discussion and Theory pages. Similar to other wiki systems, Lostpedia include links to History pages and an Edit page. The Theory page is an additional type of page for contributor interpretations of what is happening and why, whereas the articles are more descriptive of what occurred in the show.


FIGURE 15.14C

hapter 15

NodeXL Lostpedia wiki page-to-page co-edit network visualization and Vertex worksheet showing only those pages with more than 50 co-editors. All types of pages were considered, but only Article pages (maroon), Discussion pages (orange), Theory pages (green), and User Talk pages (deep pink) were co-edited enough to show up. The Harel-Koren Fast Multiscale Layout identifies natural groupings such as the main cluster of articles and the cluster of interrelated Theory pages. Size is based on total user edits of a page, and opacity is based on degree. Subgraph images show small dense clusters for the displayed vertices.


FIGURE 15.15C

hapter 15

NodeXL visualization of Lostpedia wiki user-to-user affiliation network connecting users (vertices) based on the number of unique pages they have both edited (weighted edges). Two types of edges are included: those connecting users based on co-edits of 20 or more Theory pages (green) and those connecting users based on co-edits of 150 or more articles (maroon). Vertex size is based on total wiki edits, and color is based on the percentage of pages that are Theory pages (green vertices edit mostly Theory pages and maroon vertices edit mostly Article pages). Boundary spanners and important individuals are easily identified.


FIGURE 15.16C

hapter 15

NodeXL Edges worksheet and visualization of a Lostpedia wiki user-to-user affiliation network graph with edges filtered based on the number of pages that users share as a percentage of the total number of edited pages. The number of edges for frequent editors like Santa (highlighted in red) are significantly reduced in the graph, but size indicates that they exist with those filtered out of the graph.

analyzing social media networks with nodexl - chapter-15 images

Technology

content page

article page

wiki pages

related history page

social roles

wiki community

rights reserved chapter

wiki systems