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2014 TheNextWeb-Mapping connections with NodeXL
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Transcript of 2014 TheNextWeb-Mapping connections with NodeXL
6 slides about SMRF
Marc A. SmithChief Social ScientistConnected Action Consulting [email protected]://www.connectedaction.nethttp://nodexl.codeplex.com/A project from the Social Media Research Foundation: http://www.smrfoundation.orgMapping and Measuring Connections
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About MeIntroductions
Marc A. SmithChief Social ScientistConnected Action Consulting Group
[email protected]://www.connectedaction.nethttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://www.flickr.com/photos/marc_smithhttp://www.facebook.com/marc.smith.sociologisthttp://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smithhttp://www.smrfoundation.org
Central tenet Social structure emerges from the aggregate of relationships (ties) among members of a populationPhenomena of interestEmergence of cliques and clusters from patterns of relationshipsCentrality (core), periphery (isolates), betweennessMethodsSurveys, interviews, observations, log file analysis, computational analysis of matrices
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7-16
Social Network Theoryhttp://en.wikipedia.org/wiki/Social_network
CSCW 2004 - Analyzing Social CMC3
SNA 101Nodeactor on which relationships act; 1-mode versus 2-mode networksEdgeRelationship connecting nodes; can be directionalCohesive Sub-GroupWell-connected group; clique; clusterKey MetricsCentrality (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 levelCohesion (group measure)Ease with which a network can connectAggregate measure of shortest path between each node pair at network level reflects average distanceDensity (group measure)Robustness of the networkNumber of connections that exist in the group out of 100% possible Betweenness (individual measure)# shortest paths between each node pair that a node is onMeasure at the individual node levelNode rolesPeripheral below average centralityCentral connector above average centralityBroker above average betweenness
EDFACBHGICDEABDE
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OF
Crowds matter
Kodak BrownieSnap-Shot Camera
The first easy to use point and shoot!
http://www.browniecamera.nl/brownie_original_model.htmhttp://en.wikipedia.org/wiki/Snapshot_%28photography%29
http://www.flickr.com/photos/amycgx/3119640267/
Crowds
http://www.flickr.com/photos/amycgx/3119640267/8
Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections
from people
to people.10
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Patterns are left behind11
11I can look at the tracks people have left behind in their interactions with me.
There are many kinds of ties. Send, Mention, http://www.flickr.com/photos/stevendepolo/3254238329Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in Internet Verbs!
Think LinkNodes & Edges
Is related to
ABIs related toIs related to
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Think LinkNodes & Edges
Is related to
ABIs related toIs related to
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World Wide WebSocial media must contain one or more social networks
Vertex1Vertex 2Edge AttributeVertex1 AttributeVertex2 Attribute@UserName1@UserName2valuevaluevalue
A network is born whenever two GUIDs are joined.
UsernameAttributes@UserName1Value, value
UsernameAttributes@UserName2Value, value
AB
NodeXL imports edges from social media data sources
Location, Location, Location
Position, Position, Position
Mapping and Measuring Connections with
Like MSPaint for graphs. the Community
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Now Available
Communities in Cyberspace
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What we are trying to do:Open Tools, Open Data, Open ScholarshipBuild the Firefox of GraphML open tools for collecting and visualizing social media dataConnect users to network analysis make network charts as easy as making a pie chartConnect researchers to social media data sourcesArchive: Be the Allen Very Large Telescope Array for Social Media data coordinate and aggregate the results of many users data collection and analysisCreate open access research papers & findingsMake collections of connections easy for users to manage
Goal: Make SNA easierExisting Social Network Tools are challenging for many novice usersTools like Excel are widely usedLeveraging a spreadsheet as a host for SNA lowers barriers to network data analysis and display
What we have done: Open ToolsNodeXLData providers (spigots)ThreadMill Message BoardExchange Enterprise EmailVoson HyperlinkSharePointFacebookTwitterYouTubeFlickr
NodeXL Ribbon in Excel
What we have done: Open DataNodeXLGraphGallery.orgUser generated collection of network graphs, datasets and annotationsCollective repository for the research communityPublished collections of data from a range of social media data sources to help students and researchers connect with data of interest and relevance
What we have done: Open Scholarship
http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/30
Network Analysis Data Flow
PublicationVisualizationAnalysisContainerProviders
http://www.flickr.com/photos/badgopher/3264760070/Data Providers
http://www.flickr.com/photos/badgopher/3264760070/
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Providers
Example NodeXL data importer for Twitter
http://www.flickr.com/photos/druclimb/2212572259/in/photostream/Data Container
Container
Data Analysishttp://www.flickr.com/photos/hchalkley/47839243/
Analysis
Data Visualizationhttp://www.flickr.com/photos/rvwithtito/4236716778
Visualization
http://www.flickr.com/photos/62693815@N03/6277208708/Data Publication
Publication
Social Network Maps Reveal
Key influencers in any topic.
Sub-groups.
Bridges.
Hubs
Bridges
Islandshttp://www.flickr.com/photos/storm-crypt/3047698741
http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/Clusters
http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/47
[Divided]Polarized Crowds[Unified]Tight Crowd[Fragmented]Brand Clusters[Clustered]Community Clusters[In-Hub & Spoke]Broadcast Network[Out-Hub & Spoke]Support Network
6 kinds of Twitter social media networks
[Divided]Polarized Crowds[Unified]Tight Crowd[Fragmented]Brand Clusters[Clustered]Community Clusters[In-Hub & Spoke]Broadcast Network[Out-Hub & Spoke]Support Network
6 kinds of Twitter social media networks
#My2KPolarized
The network of connections among people who tweeted #My2K over the 1-day, 21-hour, 39-minute period from Sunday, 06 January 2013 at 03:30 UTC to Tuesday, 08 January 2013 at 01:09 UTC. 50
#CMgrChatIn-group / Community
The graph represents a network of 268 Twitter users whose recent tweets contained "#cmgrchat OR #smchat. The network was obtained on Friday, 18 January 2013 at 15:44 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 3-day, 21-hour, 15-minute period from Monday, 14 January 2013 at 18:23 UTC to Friday, 18 January 2013 at 15:38 UTC.51
LumiaBrand / Public Topic
The graph represents a network of 1,227 Twitter users whose recent tweets contained "lumia. The network was obtained on Saturday, 12 January 2013 at 19:52 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 5-hour, 1-minute period from Saturday, 12 January 2013 at 14:36 UTC to Saturday, 12 January 2013 at 19:37 UTC.52
#FLOTUSBazaar
The graph represents a network of 1,260 Twitter users whose recent tweets contained "flotus". The network was obtained on Friday, 18 January 2013 at 18:26 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 3-hour, 3-minute period from Friday, 18 January 2013 at 15:16 UTC to Friday, 18 January 2013 at 18:20 UTC.53
New York Times ArticlePaul KrugmanBroadcast: Audience + Communities
The graph represents a network of 399 Twitter users whose recent tweets contained "http://www.nytimes.com/2013/01/11/opinion/krugman-coins-against-crazies.html. The network was obtained on Friday, 11 January 2013 at 14:27 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 12-hour, 32-minute period from Friday, 11 January 2013 at 01:52 UTC to Friday, 11 January 2013 at 14:24 UTC.54
Dell Listens/DellcaresSupport
The graph represents a network of 388 Twitter users whose recent tweets contained "delllistens OR dellcares. The network was obtained on Tuesday, 19 February 2013 at 17:44 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 6-day, 21-hour, 58-minute period from Tuesday, 12 February 2013 at 19:34 UTC to Tuesday, 19 February 2013 at 17:33 UTC.55
SNA questions for social media:
What does my topic network look like?What does the topic I aspire to be look like?What is the difference between #1 and #2?How does my map change as I intervene?
What does #YourHashtag look like?
Top 10 Vertices@tnwconference@shingy@aral@patrick@jarnoduursma@sarahmarshall@boris@briansolis@technifista@qadabraplatform
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Most central:@bitpay@coindesk@tuurdemeester@bitgiveorg@allthingsbtc@ihavebitcoins@btcmarketsnews@sp0rkyd0rky@hermetec@redditbtc
strataconf Twitter NodeXL SNA Map and Report for 2014-02-11 12-53-27
Top 10 Vertices, Ranked by Betweenness Centrality:@strataconf@peteskomoroch@acroll@oreillymedia@orthonormalruss@ayirpelle@bigdata@furrier@marketpowerplus@sassoftware
https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=16540
strataconf Twitter NodeXL SNA Map and Report for 2014-02-11 12-53-27
The graph represents a network of 1,685 Twitter users whose recent tweets contained "strataconf",tweeted over the 8-day, 0-hour, 44-minute period from Monday, 03 February 2014 at 19:55 UTC to Tuesday, 11 February 2014 at 20:39 UTC.
Top Hashtags in Tweet in Entire Graph: #Strataconf, #bigdata, #hds, #BigDataSV, #hadoop, #ddbd60
datavis Twitter NodeXL SNA Map and Report for Tuesday, 11 February 2014 at 18:55 UTC
Top 10 Vertices, Ranked by Betweenness Centrality:@bigpupazzoverde@randal_olson@twitterdata@7of13@yochum@edwardtufte@twittersports@grandjeanmartin@smfrogers@albertocairo
https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=16541
datavis Twitter NodeXL SNA Map and Report for Tuesday, 11 February 2014 at 18:55 UTC
The graph represents a network of Twitter users whose tweets in the requested date range contained "dataviz OR datavis over the 41-day, 4-hour, 5-minute period from Wednesday, 01 January 2014 at 00:01 UTC to Tuesday, 11 February 2014 at 04:06 UTC
Top Hashtags in Tweet in Entire Graph: #dataviz, #bigdata, #analytics, #map, #Europe, #Datavis, #Audit, #Logs
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[Divided]Polarized Crowds[Unified]Tight Crowd[Fragmented]Brand Clusters[Clustered]Community Clusters[In-Hub & Spoke]Broadcast Network[Out-Hub & Spoke]Support Network
6 kinds of Twitter social media networks
[Divided]Polarized Crowds[Unified]Tight Crowd[Fragmented]Brand Clusters[Clustered]Communities[In-Hub & Spoke]Broadcast Network[Out-Hub & Spoke]Support Network
[Low probability]Find bridge users.Encourage shared material.[Low probability]Get message out to disconnected communities.[Possible transition]Draw in new participants.[Possible transition]Regularly create content.[Possible transition]Reply to multiple users.
[Undesirable transition]Remove bridges, highlight divisions.[Low probability]Get message out to disconnected communities.[High probability]Draw in new participants.[Possible transition]Regularly create content.[Possible transition]Reply to multiple users.
[Undesirable transition]Increase density of connections in two groups.[Low probability]Dramatically increase density of connections.[High probability]Increase retention, build connections.[Possible transition]Regularly create content.[Possible transition]Reply to multiple users.
[Undesirable transition]Increase density of connections in two groups.[Low probability]Dramatically increase density of connections.[Undesirable transition]Increase population, reduce connections.[Possible transition]Regularly create content.[Possible transition]Reply to multiple users.
[Undesirable transition]Increase density of connections in two groups.[Low probability]Dramatically increase density of connections.[Low probability]Get message out to disconnected communities.[Possible transition]Increase retention, build connections.[High probability]Increase reply rate, reply to multiple users.
[Undesirable transition]Increase density of connections in two groups.[Low probability]Dramatically increase density of connections.[Possible transition]Get message out to disconnected communities.[High probability]Increase retention, build connections.[High probability]Increase publication of new content and regularly create content.
Red=Undesireable transitionYellow=Low probability transitionLight blue= potential transition or complementary structureDark green = strong transition or complementary structure
http://www.katypearce.net/protestbaku-analysis-the-day-after/64
C. Scott Dempwolf, PhDResearch Assistant Professor & DirectorUMD - Morgan State Center for Economic Development
http://portal.sliderocket.com/ATWBE/Using-SNA-to-find-and-manage-RICs
C. Scott Dempwolf, PhDResearch Assistant Professor & DirectorUMD - Morgan State Center for Economic Development
http://www.terpconnect.umd.edu/~dempy/
Insights: many clusters are based around a county and local enterprises. E.g., the middle-left cluster is Pittsburgh metro area, with large orange Westinghouse Electric. The Philadelphia cluster in the top-right is highly connected to the bottom left, which are adjacent counties. An exception to location grouping is the top-left pharma and medical cluster, composed of several companies, universities, HHS, and an interesting arrangement of inventors in several connected fans.https://plus.google.com/photos/116499393494903612852/albums/5659635437858992593/5659734868308985794?banner=pwa&pid=5659734868308985794&oid=11649939349490361285265
What is Social Network Analysis? How is it useful for the humanities?1. New framework for analysis2. Data visualization allows new perspectives less linear, more comprehensiveSocial Network Analysis and Ancient HistoryDiane H. Cline, Ph.D.University of Cincinnati
Prof. Diane Clinehttp://www.academia.edu/2153390/The_Social_network_of_Alexander_the_Great_Social_Network_Analysis_in_Ancient_History
Its about who you know, and who those people know, and how everyone knows each other.Data visualization tool to see data differently.66
Strategies for social media engagement based on social media network analysis
Request your own network map and report
http://connectedaction.net
What we want to do: (Build the tools to) map the social webMove NodeXL to the web: (Node[NOT]XL)Node for Google Doc Spreadsheets? WebGL Canvas? D3.JS? Sigma.JSConnect to more data sources of interest:RDF, MediaWikis, Gmail, NYT, Citation NetworksSolve hard network manipulation UI problems:Modal transform, Time series, Automated layoutsGrow and maintain archives of social media network data sets for research use.Improve network science education:Workshops on social media network analysisLive lectures and presentationsVideos and training materials
How you can helpSponsor a featureSponsor workshopsSponsor a studentSchedule trainingSponsor the foundationDonate your money, code, computation, storage, bandwidth, data or employees timeHelp promote the work of the Social Media Research Foundation
Thank you!
Marc A. SmithChief Social ScientistConnected Action Consulting [email protected]://www.connectedaction.nethttp://nodexl.codeplex.com/A project from the Social Media Research Foundation: http://www.smrfoundation.orgMapping and Measuring Connections
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