Social Media Analytics Meetup
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Transcript of Social Media Analytics Meetup
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Leveraging NodeXLfor Visualizationof Social Network Data
Visualizing Social Data with Twitter, MapBox, and NodeXL
C. Scott Dempwolf, PhDResearch Assistant Professor& Director
Social Data and Analytics MeetupThe Washington Post
August 19, , 2014
UMD – Morgan StateCenter for Economic Development
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Who are you ?(and why are you staring at me?)
Who you were yesterday… Who you are today…
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Who am I?(and how did I get here?)
• At UMD since 2007– 2007 – 1012 PhD student– 2012 - Research Asst.
Professor
• Using NodeXL since 2011• Uses of Social Network
Analysis in Planning• Focus on innovation &
economic development
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Social Network TheoryIn one slide
• Central tenet – Social structure emerges from – the aggregate of relationships (ties) – among members of a population
• Phenomena of interest– Emergence of cliques and clusters – from patterns of relationships– Centrality (core), periphery (isolates), – betweenness
• Methods– Surveys, interviews, observations,
log file analysis, computational analysis of matrices
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)http://en.wikipedia.org/wiki/Social_network
Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7-16
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OK… two slides
• Node– “actor” on which relationships act; 1-mode versus 2-mode networks
• Edge– Relationship connecting nodes; can be directional
• Cohesive Sub-Group– Well-connected group; clique; cluster
• Key Metrics– Centrality (group or individual measure)
• Number of direct connections that individuals have with others in the group (usually look at incoming connections only)
• Measure at the individual node or group level– Cohesion (group measure)
• Ease with which a network can connect• Aggregate measure of shortest path between each node pair at network level reflects
average distance– Density (group measure)
• Robustness of the network• Number of connections that exist in the group out of 100% possible
– Betweenness (individual measure)• # shortest paths between each node pair that a node is on• Measure at the individual node level
• Node roles– Peripheral – below average centrality– Central connector – above average centrality– Broker – above average betweenness
CD
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Why I Use NodeXL
Built on ExcelEasy to learnUser friendlyFlexibleFREE*Community of users
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Using NodeXL to AnalyzeInnovation Networks
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Using NodeXL to AnalyzeInnovation Networks
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Visualizing CrunchBase Data
Global Capital FlowsAccelerators, Investors & Startups
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Why networks & technology matter
• Startups need to be seeded into strong clusters
• Clusters have strong connections to markets, supply chains and talent pools
• Clusters form around technologies
• Capital flows around technologies
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What about social media?
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6 Kinds of Twitter Networks
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Cool pictures but so what?
Th
e n
etw
ork
you h
ave
The network you want
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What Now?
Contemplate your hashtags Get started with NodeXL
Thanks to Marc Smith for letting me cannibalize his slides