A project from the Social Media Research Foundation: http://www.smrfoundation.org

Picturing political social

media discourse networks -

mapping the shape of the virtual crowd

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

marc@smrfoundation.org 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=54648

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)

prague Twitter NodeXL SNA Map and Report for Thursday, 01 October 2015 at 09:04 UTC

Prague: https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=54651

https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=54643

MZemanOficialni OR "Miloš Zeman" OR PrezidentZeman Twitter NodeXL SNA Map and Report

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.

37

Patterns are left behind

38

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.

• 9/30/15 64

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.

Request your own network map and report

http://connectedaction.net

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