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A project from the Social Media Research Foundation: http://www.smrfoundation.org
Network mapping
the social media ecosystem
with NodeXL
Marc A. SmithChief Social ScientistSocial Media Research Foundationhttp://smrfoundation.org http://nodexl.codeplex.com/http://nodexlgraphgallery.org
About Me
Introductions
Marc A. SmithChief Social Scientist / DirectorSocial Media Research Foundation
[email protected] http://www.smrfoundation.orghttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smithhttp://www.flickr.com/photos/marc_smithhttp://www.facebook.com/marc.smith.sociologist
Crowds matter
http://www.flickr.com/photos/amycgx/3119640267/
Crowds in social media matter
Crowds in social media have a hidden structure
https://demo-3dg-viz.herokuapp.com/
http://www.bonkersworld.net/organizational-charts/
Kodak BrownieSnap-Shot Camera
The first easy to use
point and shoot!
https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=46679
#pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 21:18 UTC
NodeXL Ribbon in Excel
NodeXL in Excel
We envision hundreds of NodeXL data collectors around the world collectively generating a free and open archive of social media network
snapshots on a wide range of topics.
http://msnbcmedia.msn.com/i/msnbc/Components/Photos/071012/071012_telescope_hmed_3p.jpg
https://nodexlgraphgallery.org/Pages/Default.aspx?search=data+open
Top 10 Vertices:@mlsif@civichall@mitgc_cm@stone_rik@civicist@juansvas@tableteer@jcstearns@ppolitics@marc_smith
#pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 12:41 UTC
Top 10 Hashtags:#pdf15#ian1#asmsg#bzbooks#bynr#civictech#nyc#authors#t4us#aga3
#pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 12:41 UTC
Broadcast Hub(stone_rik)
Broadcast Hub(CivicHall, mlsif)
Broadcast Hub(mitgc_cm)
Brand Cluster(Isolates)
A DAY LATER
#pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 21:18 UTChttps://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=46679
Top 10 Vertices:@mitgc_cm@stone_rik@mlsif@jgilliam@dantebarry@deanna@slaughteram@jcstearns@civicist@Digiphile
Top 10 Hashtags:#pdf15#civictech#tiimr#blacklivesmatter#ian1#asmsg#bzbooks#bynr#pitmad#scfinalsvote
#pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 21:18 UTChttps://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=46679
Community Cluster
Broadcast Hub(digiphile)
Brand Cluster(Isolates)
Community Cluster
Broadcast Hub(mlsif)
Hubs
https://flic.kr/p/4Z6GHv
https://flic.kr/p/etEmeR
Bridges
Islands
http://www.flickr.com/photos/storm-crypt/3047698741
https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=46163
Top 10 Vertices:@niyiabiriblog@niyiabiri@codeforamerica@civichall@knightfdn@omidyarnetwork@betanyc@digiphile@elle_mccann@participatory
Top 10 Hashtags:#civictech#opendata#opengov#latism#tictec#govtech#newurbanpractice#womenforward#gov20#civichall
civictech Twitter NodeXL SNA Map and Report for Tuesday, 26 May 2015 at 05:25 UTC
World Wide Web
Social media must contain one or more
social networks
Crowds in social media form networks
Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections
from people
to people.
30
Patterns are left behind
31
There are many kinds of ties…. Send, Mention,
http://www.flickr.com/photos/stevendepolo/3254238329
Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in…
Internet Verbs!
“Think Link”Nodes & Edges
Is related to
A BIs related to
Is related to
“Think Link”Nodes & Edges
Is related to
A BIs related to
Is related to
Vertex1 Vertex 2 “Edge” Attribute
“Vertex1” Attribute
“Vertex2” Attribute
@UserName1 @UserName2 value value value
A network is born whenever two GUIDs are joined.
Username Attributes
@UserName1 Value, value
Username Attributes
@UserName2 Value, value
A B
NodeXL imports “edges” from social media data sources
http://techpresident.com/news/22538/crowd-photography-cyber-tahrir-square
http://foreignpolicy.com/2012/06/18/visualizing-the-war-on-women-debate/
http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
Social media network analysis
• Social media is inherently made of networks,
– which are created when people link and reply.
• Collections of connections have an emergent shape,
– Some shapes are better than others.
• Some people are located in strategic locations in these shapes,
– Centrally located people are more influential than others.
https://nodexlgraphgallery.org/Pages/Default.aspx?search=civic
http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
[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
http://www.pewresearch.org/fact-tank/2014/02/20/the-six-types-of-twitter-conversations/
[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
#My2K
Polarized
#CMgrChat
In-group / Community
Lumia
Brand / Public Topic
#FLOTUS
Bazaar
New York Times ArticlePaul Krugman
Broadcast: Audience + Communities
Dell Listens/Dellcares
Support
New Book in Progress!
Network Analysis Data Flow
PublicationVisualizationAnalysisContainerProviders
Social Network Maps Reveal
Key influencers in any topic.
Sub-groups.
Bridges.
SNA questions for social media:
1. What does my topic network look like?2. What does the topic I aspire to be look like?3. What is the difference between #1 and #2?4. How does my map change as I intervene?
What does #YourHashtag look like?
Who is the mayor of #YourHashtag?
[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
Examples of social network scholarshipMargarita M. Orozco Doctoral Student, School of Journalism & Mass CommunicationUniversity of Wisconsin- Madison
Katy Pearce (@katypearce)Assistant Prof of Communication Studies technology & inequality in Armenia & Azerbaijan.
Elena Pavan, Ph.D.Post Doctoral Research FellowDipartimento di Sociologia e Ricerca SocialeUniversità di Trentovia Verdi 26, 38122 Trento (Italy)
Examples of social network scholarshipMargrét Vilborg BjarnadóttirRobert H. Smith School of Business | University of MarylandData Scientist | Parliamentary Special Investigation Commission
Prof. Diane Harris ClineAssociate Professor of HistoryGeorge Washington University
C. Scott Dempwolf, PhDResearch Assistant Professor & DirectorUMD - Morgan State Center for Economic Development
Studying the Colombian Peace Process in Twitter
• Analyzing perceptions of the peace process in Colombian public opinion in Twitter.
• It is important to know what are citizens thinking, perceptions, and concerns.
• Q: who are the main actors in Twitter in favor and against the peace process who are leading sources of information about it?
• Colombians are the world’s 15th top Twitter users. For this reason this social media constitutes an important source of information about public opinion.
• 6/5/15 57
Margarita M. Orozco Doctoral Student, School of Journalism & Mass CommunicationUniversity of Wisconsin- Madison
Katy Pearce (@katypearce)Assistant Prof of Communication Studies technology & inequality in Armenia & Azerbaijan.
#ProtestBakuAzerbaijan
Take Back The Tech! Reclaiming ICTs against Violence Against Women
• Launched in 2006 by the Association for Progressive Communications Women Rights Program (APC WRP)
• Runs yearly during the 16 days against Violence Against Women (VAW)• Website http://www.takebackthetech.net • “16 daily actions” to reclaim ICTs against VAW and a Tweetathon• Explored in the context of the project REACtION (
http://www.reactionproject.info) in relation to the interplay between the “offline” advocacy strategy and the “online” Twitter networks over time
• Findings: shifts in the advocacy strategy shift the network structure – moving from the outside to the online of the institutions (lobbying at the Commission on the Status of Women) led to a centralized Twitter network where organizational and institutional accounts play most central roles
REACtION - Collective Action Networks between Online and Offline Interactions - http://www.reactionproject.info. Grant post-doc 2011 by the Provincia Autonoma di Trento (Italy)
Elena Pavan, Ph.D.Post Doctoral Research FellowDipartimento di Sociologia e Ricerca SocialeUniversità di Trentovia Verdi 26, 38122 Trento (Italy)
2012: Outside institutions, a grassroots conversation
REACtION - Collective Action Networks between Online and Offline Interactions - http://www.reactionproject.info. Grant post-doc 2011 by the Provincia Autonoma di Trento (Italy)
2013: Accessing institutions, a more structured conversation
REACtION - Collective Action Networks between Online and Offline Interactions - http://www.reactionproject.info. Grant post-doc 2011 by the Provincia Autonoma di Trento (Italy)
2014: Inside institutions,a centralized conversation
REACtION - Collective Action Networks between Online and Offline Interactions - http://www.reactionproject.info. Grant post-doc 2011 by the Provincia Autonoma di Trento (Italy)
Margrét Vilborg BjarnadóttirRobert H. Smith School of Business | University of Maryland
Data Scientist | Parliamentary Special Investigation Commission
Data Driven Large Exposure Estimation:A Case Study of a Failed Banking System
Co-authors: Sigríður Benediktsdóttir and Guðmundur Axel Hansen
Supporting Publications:Margrét V. Bjarnadóttir and Gudmundur A. Hanssen. 2010. Cross-Ownership and Large Exposures; Analysis and Policy Recommendations. Report of the Special Investigation Commission, Volume 9. Sigridur Benediksdottir and Margrét V. Bjarnadóttir. “Large Exposure Estimation through Automatic Business Group Identification”. Proceedings to DSMM 2014.
C. Scott
Dempwolf, PhD
Research Assistant Professor &
DirectorUMD - Morgan State Center for Economic
Development
http://www.terpconnect.umd.edu/~dempy/
Social Network Analysis for the humanities?
Social Network Analysis and Ancient History
Prof. Diane Harris ClineAssociate Professor of History; Affiliated faculty member in Classical and Near Eastern Literatures and Civilizations.George Washington University
1. New framework for analysis2. Data visualization allows new perspectives – less linear, more comprehensive
Applying the insights of social networks to social media:
Your social media audience is smaller…
…than the audiences of ten influential voices.
Build a collection of mayors
• Map multiple topics– Your brand and company names– Your competitor brands and company names– The names of the activities or locations related to your
products• Identify the top people in each topic• Follow these people
– 30-50% of the time they follow you back• Re-tweet these people (if they did not follow you)
• 30-50% of the time they follow you back
Speak the language of the mayors
• Use NodeXL content analysis to identify each users most salient:– Words– Word pairs– URLs– #Hashtags
• Mix the language of the Mayors with your brand’s messages.
Speak the language of the mayors
The “perfect” tweet:
.@Theirname #Theirhashtag News about your brand using their words http://your.site #Yourhashtag
Speak the language of the mayors
Some shapes are better than others:
• The value of Broadcast versus community network!
• From community to brand!
• Support and why community can be a signal of failure!
Three network phases of social media success
Phase 1: You get an audience Phase 2: Your audience gets an audience Phase 3: Audience becomes community
Some shapes are better than others
• Each shape reflects the kind of social activity that generates it:
– Divided: Conflict– Unified: In-group– Brand: Fragmentation– Community: Clustering– Broadcast: Hub and spoke (In)– Support: Hub and spoke (Out)
[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.
Monitor your topics with social network maps
• Identify the – Key people– Groups– Top topics
• Locate your social media accounts within the network
What we want to do: (Build the tools to) map the social web
• Move NodeXL to the web: (Node[NOT]XL)– Node for Google Doc Spreadsheets? – WebGL Canvas? D3.JS? Sigma.JS
• Connect to more data sources of interest:– RDF, MediaWikis, Gmail, NYT, Citation Networks
• Solve hard network manipulation UI problems:– Modal transform, Time series, Automated layouts
• Grow and maintain archives of social media network data sets for research use.
• Improve network science education:– Workshops on social media network analysis– Live lectures and presentations– Videos and training materials
How you can help
• Sponsor a feature• Sponsor workshops• Sponsor a student• Schedule training• Sponsor the foundation• Donate your money, code, computation, storage,
bandwidth, data or employee’s time• Help promote the work of the Social Media
Research Foundation
A project from the Social Media Research Foundation: http://www.smrfoundation.org
Network mapping
the social media ecosystem
with NodeXL
Marc A. SmithChief Social ScientistSocial Media Research Foundationhttp://smrfoundation.org http://nodexl.codeplex.com/http://nodexlgraphgallery.org
