EventGraphs Talk at HCIL2011


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This talk discusses and illustrates EventGraphs, a genre of social network diagram that illustrate the social structure of mass conversations around events.

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  • Thanks to Jimmy Lin for the CHI2011 image.
  • Size based on total Twitter FollowersEdge Colors: Orange=Follow, Blue dotted=Mentions, Red=Reply To, Purple = Mentions and Reply ToSee https://casci.umd.edu/HICSS_2011_EventGraph for details
  • Identify search terms (e.g., “hicss”, “oil spill” OR “oilspill”)User “importer” to download sample of messages and connections between authors, along with other data of interestChoose appropriate layout algorithmMap visual attributes to variables of interest (e.g., size = Twitter Followers)
  • Colors indicate automatic detection of groups based on network position, not trait.
  • Size, number of isolates, distribution of degree, clusters
  • Bill Murray = Cliff Lampe
  • Sized by Total Twitter Followers
  • Sized by Betweenness Centrality
  • Sized by Total Twitter Followers
  • Sized by Total Twitter Followers
  • Sized by Total Twitter Followers
  • Here are some ways that EventGraphs can be used to achieve “actionable insights” for conference attendees and organizers.
  • Take-Home message. Ways of using EventGraphs as user & conference organizer
  • EventGraphs Talk at HCIL2011

    1. 1. EventGraphs: mapping the social structure of events with NodeXL<br />
    2. 2. Mass Conversations of Events<br />Research Goal: Augment people’s ability to make sense of mass conversations of events<br />
    3. 3. HICSS 2011 EventGraph<br />https://casci.umd.edu/HICSS_2011_EventGraph <br />
    4. 4. EventGraph: n. A specific genre of network graph that illustrates the structure of connections among people discussing an event via social media services like Twitter.1<br />1Derek Hansen, Marc A. Smith, Ben Shneiderman, "EventGraphs: Charting Collections of Conference Connections," HICSS, pp.1-10, 2011 44th Hawaii International Conference on System Sciences, 2011<br />
    5. 5. Types of EventGraph Connections<br />Conversational Connections: E.g., Mentions, Replies to, Forwards to, Re-Tweets<br />Structural Connections: E.g., Follows, is Friends with, is a Fan of<br />
    6. 6. Taxonomy of EventGraphs<br />Duration of event (point events, hours long, days long, weeks long…)<br />Frequency of event (one-time, repeated)<br />Spontaneity of event (planned, unplanned)<br />Geographic dispersion of event discussants<br />
    7. 7. Creating EventGraphs in NodeXL<br />
    8. 8. HICSS<br />
    9. 9. Analyzing EventGraphs in NodeXL<br />
    10. 10. What is the Social Structure of an Event Related Discussion?<br />EventGraph of “oil spill” Twitter data from May 4, 2010 with clusters colored differently and size based on Twitter followers<br />
    11. 11. Compare DC Week (left) to HICSS (right)<br />
    12. 12. Who are “Important” Event Discussants?<br />Popular globally and locally<br />Bridge Spanner<br />Popular locally but not globally<br />Popular globally but not locally<br />
    13. 13. What is the Nature of the Event Conversation?<br />
    14. 14. Theorizing The Web 2011 (@ttw2011)(Size = Total Twitter Follower)<br />https://casci.umd.edu/TTW2011_EventGraph <br />
    15. 15. Theorizing The Web 2011 (@ttw2011)(Size = Betweenness Centrality)<br />https://casci.umd.edu/TTW2011_EventGraph <br />
    16. 16. HCIL Symposium 2011 (#hcil OR hcil)(Size based on Total Twitter Follower)<br />https://casci.umd.edu/HCIL2011 <br />
    17. 17. HCIL Symposium 2011 (#hcil OR hcil)(Size based on Betweenness Centrality)<br />https://casci.umd.edu/HCIL2011 <br />
    18. 18. HCIL Symposium 2011 (#hcil OR hcil)(Size based on Betweenness Centrality; Discussion only)<br />https://casci.umd.edu/HCIL2011 <br />
    19. 19. Caveats<br />EventGraphs are only as good as their data<br />Keywords with low recall (#ashcloud, #ashtag) or precision (Jaguar)<br />Not everyone Tweets (HICSS vs. South by Southwest)<br />Twitter usage patterns confounded with underlying social network relationships (not a problem for conversational analysis)<br />Size limitations for visualizations to be meaningful<br />
    20. 20. EventGraph Uses<br />Conference Attendees<br />Find people you want to meet (and who can introduce you)<br />Assess reputation of speakers<br />Find subgroups you fit in, and those you’re not connected to<br />Conference Organizers<br />Provide an appealing visual representation of conference<br />Demonstrate role of bridging different communities<br />Demonstrate value of creating new connections (by comparing before/after EventGraphs)<br />Look for subgroups that could form SIGs<br />
    21. 21. Future Work<br />Automated query expansion/refinement (particularly for unplanned events)<br />Event detection algorithms and hashtag recommendations<br />Overlaying text-based attributes (e.g., sentiment analysis)<br />Integrating EventGraphs and events<br />Developing metrics that identify individuals that benefit most from events<br />
    22. 22. http://nodexl.codeplex.com<br />