EventGraphs are network graphs that illustrate the social structure of discussions around events on social media. This document discusses EventGraphs, including how they are created in NodeXL and analyzed to understand the social structure and important discussants in event conversations. It provides examples of EventGraphs for conferences and discusses future work such as automated query expansion and integrating sentiment analysis.

1. EventGraphs: mapping the social structure of events with NodeXL

2. Mass Conversations of Events Research Goal: Augment people’s ability to make sense of mass conversations of events

3. HICSS 2011 EventGraph https://casci.umd.edu/HICSS_2011_EventGraph

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 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

5. Types of EventGraph Connections Conversational Connections: E.g., Mentions, Replies to, Forwards to, Re-Tweets Structural Connections: E.g., Follows, is Friends with, is a Fan of

6. Taxonomy of EventGraphs Duration of event (point events, hours long, days long, weeks long…) Frequency of event (one-time, repeated) Spontaneity of event (planned, unplanned) Geographic dispersion of event discussants

7. Creating EventGraphs in NodeXL

8. HICSS

9. Analyzing EventGraphs in NodeXL

10. What is the Social Structure of an Event Related Discussion? EventGraph of “oil spill” Twitter data from May 4, 2010 with clusters colored differently and size based on Twitter followers

11. Compare DC Week (left) to HICSS (right)

12. Who are “Important” Event Discussants? Popular globally and locally Bridge Spanner Popular locally but not globally Popular globally but not locally

13. What is the Nature of the Event Conversation?

14. Theorizing The Web 2011 (@ttw2011)(Size = Total Twitter Follower) https://casci.umd.edu/TTW2011_EventGraph

15. Theorizing The Web 2011 (@ttw2011)(Size = Betweenness Centrality) https://casci.umd.edu/TTW2011_EventGraph

16. HCIL Symposium 2011 (#hcil OR hcil)(Size based on Total Twitter Follower) https://casci.umd.edu/HCIL2011

17. HCIL Symposium 2011 (#hcil OR hcil)(Size based on Betweenness Centrality) https://casci.umd.edu/HCIL2011

18. HCIL Symposium 2011 (#hcil OR hcil)(Size based on Betweenness Centrality; Discussion only) https://casci.umd.edu/HCIL2011

19. Caveats EventGraphs are only as good as their data Keywords with low recall (#ashcloud, #ashtag) or precision (Jaguar) Not everyone Tweets (HICSS vs. South by Southwest) Twitter usage patterns confounded with underlying social network relationships (not a problem for conversational analysis) Size limitations for visualizations to be meaningful

20. EventGraph Uses Conference Attendees Find people you want to meet (and who can introduce you) Assess reputation of speakers Find subgroups you fit in, and those you’re not connected to Conference Organizers Provide an appealing visual representation of conference Demonstrate role of bridging different communities Demonstrate value of creating new connections (by comparing before/after EventGraphs) Look for subgroups that could form SIGs

21. Future Work Automated query expansion/refinement (particularly for unplanned events) Event detection algorithms and hashtag recommendations Overlaying text-based attributes (e.g., sentiment analysis) Integrating EventGraphs and events Developing metrics that identify individuals that benefit most from events

22. http://nodexl.codeplex.com