EventGraphs: mapping the social structure of events with NodeXL.

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Presentation transcript:

EventGraphs: mapping the social structure of events with NodeXL

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

HICSS 2011 EventGraph

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 1 Derek Hansen, Marc A. Smith, Ben Shneiderman, "EventGraphs: Charting Collections of Conference Connections," HICSS, pp.1-10, th Hawaii International Conference on System Sciences, 2011

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

Creating EventGraphs in NodeXL

HICSS

Analyzing EventGraphs in NodeXL

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

Compare DC Week (left) to HICSS (right)

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

What is the Nature of the Event Conversation?

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

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

Theorizing The Web 2011

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

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