NodeXL Research


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This is a presentation that describes at a high level some of the work we've been performing related to NodeXL and it's use to understand social media networks.

Published in: Technology
  • Derek, I thank you for your prompt clarification. I have also different interests in applying SNA such as in studying intangibles and including emotions. Some of my slideshare presentations dwell on these issues.
    I liked your Slide 6 as it summarizes in simplicity SNA relationships
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  • I've focused on the specific area of social media marketing and online community managers, so I haven't delved into creditors. However, as you state the techniques are similar and there is an active area of research in that domain - I'm just not particularly up on it.
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  • Great stuff. Are you teaching students how to use SNA in specific business applications such as credit faulters and their SNA as compared to non-faulters? Slide 11 shows that your systematic approach is valid for such applications.
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  • Slide 11: We have been using NodeXL in courses for non-technical students unfamiliar with social network analysis to better understand the process they go through, the barriers they encounter at various steps in that process, and the ways that SNA tools can be customized to meet their particular needs. For example, we found that the tight integration of the visual network browser and spreadsheet data was key for students’ understanding. We also found the need to support the data collection and structuring phase, and improve layouts. These and other detailed findings have led to improvements in NodeXL.
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  • Slide 9: What you see is a screenshot from the NodeXL tool.
    NodeXL is a plugin (technically a template) for Microsoft Excel 2007 and above, which allows you to analyze and visualize relational data.
    It includes features that make it easy for analysts to grab data from social media tools like email, Twitter, YouTube, and Flickr; and then make sense of that data by calculating metrics and visualizing networks.
    It is constantly being updated to improve scalability, usability, and functionality, and serves as a platform on which to try novel approaches to SNA.
    Attribute data or network metric calculations describing individuals and connections in the network can be mapped onto different visual attributes such as size, color, and opacity. Subgraph images like those seen on the left characterize person-specific networks and Excel’s formulas can be used to calculate additional metrics. For example, this network shows the most active contributors to a website design Q&A community, with greener nodes filling the social role of “question answerer” and redder nodes representing discussion starters.
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  • One key characteristic of technology-mediated communication is that it can (and typically does) capture detailed data on social interactions.Just like footprints left on the sand tell a story about walking a dog on the beach, our digital footprints tell stories about our online behaviors and interactions.The mass of data created by social media has the potential to usher in a golden age of social science and data-driven decision making.However, to put this social data to good use by researchers, as well as non-technical community managers and decision makers, we need usable and powerful tools that support social media data analysis.
  • NodeXL Research

    1. 1. Analyzing Social Media Networks with NodeXL<br />Derek L. Hansen<br />Maryland’s iSchool<br />
    2. 2.
    3. 3. Patterns are left behind<br />
    4. 4. Research Goal<br />Develop powerful tools, processes, and methods that dramatically lower the barriers for community managers and researchers to make sense of social media interactions.<br />
    5. 5. Online Community Analysis<br />
    6. 6. Social Network Analysis<br />A systematic method for understanding relationships between entities.<br />Vertex-Specific Metrics<br /><ul><li>Betweenness Centrality
    7. 7. Degree Centrality
    8. 8. Eigenvector Centrality
    9. 9. Closeness Centrality</li></ul>Network-Specific Metrics<br /><ul><li> Components
    10. 10. Density</li></ul>Clusters & Subgroups<br /><ul><li> Grouped based on shared connections</li></li></ul><li>Research Question (1)<br />How can the complex, sophisticated set of SNA techniques be supported in an intuitive manner for community analysts? <br />
    11. 11.
    12. 12. NodeXL (<br />
    13. 13. Lesson’s Learned<br />+ Tight integration of spreadsheet & visualization<br />+ Attribute data and SNA metrics can be mapped onto rich set of visual properties<br />+ Data importers<br />+ Dynamic filters<br />- Not platform independent or web-based<br /><ul><li>Lack of “undo”
    14. 14. Poor at dealing with multiple networks</li></li></ul><li>Research Questions (2)<br />How do non-technical users learn SNA and apply it to understand community interaction?<br />What barriers do they encounter?<br />How should SNA tools be customized for novices?<br />
    15. 15. Novice SNA Study<br /><ul><li>Participants: 16 grad students in course on Online Communities
    16. 16. Task: create insightful network visualization & explanation of online community they’d been studying (3 weeks with feedback from peers & instructor)
    17. 17. Data collection: diaries, observations, interviews, questionnaire, content analysis of assignments, in-class process recap
    18. 18. Data analysis: grounded theory approach
    19. 19. Output: process model; challenges; supports</li></li></ul><li>Mapping Sub-Groups of Ravelry<br />
    20. 20. Finding an Alternate Admin<br />
    21. 21. Inferring Relationships<br />
    22. 22. Challenges & Supports<br />
    23. 23. What Novices Need…<br />Social media “network” data importers<br />Better defined & recognized network visualization “genres”<br />Improved layout algorithms<br />The ability to share visualizations & analysis<br />Basic network literacy<br />
    24. 24. Research Questions (3)<br />How can SNA be applied to different social media platforms to gain actionable insights?<br />What network “genres” lead to actionable insights?<br />
    25. 25.
    26. 26. Personal Email Collection<br />
    27. 27. Mapping Corporate Email Communication Between Research Groups<br />
    28. 28. Surgery Videos on YouTube<br />
    29. 29. Finding Friendship Clusters in Facebook<br />By Bernie Hogan<br />
    30. 30. Finding Theorists in Lostpedia<br />
    31. 31. Mapping Events with Twitter EventGraphs<br />
    32. 32. Conclusion<br />There is a pressing need to support community managers and researchers trying to make sense of social media data – especially relational data.<br />Novices benefit from tight data/visualization coupling, example visualizations, data importers, good network layouts, and collaboration even from other novices.<br />Researchers and practitioners could benefit from a pallet of network visualization “genres” for specific social media networks, which can be used to gain actionable insights. <br />
    33. 33. The Future of Social Media Networks<br />Networks and place<br />Networks over time<br />Comparing multiple networks<br />Bi-modal, multiplex, affiliation and other non-standard networks<br />Improved relational data spigots<br />
    34. 34. Reflections for Social Media Researchers<br />Don’t get bogged down in endless data exploration<br />Network analysis is ideally coupled with qualitative methods<br />Remove unnecessary elements of visualizations so it clearly tells your story<br />Work with real-world clients, not just academically “interesting” work<br />
    35. 35. Analyzing Social Media Networks with NodeXL<br />Derek L. Hansen<br />Thanks to Microsoft Research<br /><ul><li>NatasaMilic-Frayling
    36. 36. Dan Fay</li></ul>Collabortors:<br /><ul><li> Ben Shneiderman
    37. 37. Marc Smith
    38. 38. Dana Rotman
    39. 39. Elizabeth Bonsignore</li></ul>The NodeXL Team<br />
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