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EventGraphs: mapping the social structure of events with NodeXL

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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Page 1: EventGraphs Talk at HCIL2011

EventGraphs: mapping the social structure of events with NodeXL

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Mass Conversations of Events

Research Goal: Augment people’s ability to make sense of mass conversations of events

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HICSS 2011 EventGraph

https://casci.umd.edu/HICSS_2011_EventGraph

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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

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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

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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

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Creating EventGraphs in NodeXL

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HICSS

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Analyzing EventGraphs in NodeXL

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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

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Compare DC Week (left) to HICSS (right)

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Who are “Important” Event Discussants?Popular globally

and locally

Popular globally but not locally

Bridge Spanner

Popular locally but not globally

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What is the Nature of the Event Conversation?

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Theorizing The Web 2011 (@ttw2011)(Size = Total Twitter Follower)

https://casci.umd.edu/TTW2011_EventGraph

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Theorizing The Web 2011 (@ttw2011)(Size = Betweenness Centrality)

https://casci.umd.edu/TTW2011_EventGraph

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HCIL Symposium 2011 (#hcil OR hcil)(Size based on Total Twitter Follower)

https://casci.umd.edu/HCIL2011

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HCIL Symposium 2011 (#hcil OR hcil)(Size based on Betweenness Centrality)

https://casci.umd.edu/HCIL2011

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HCIL Symposium 2011 (#hcil OR hcil)(Size based on Betweenness Centrality; Discussion only)

https://casci.umd.edu/HCIL2011

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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

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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

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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

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http://nodexl.codeplex.com