2016 socialmedia.org marc smith-nodexl-social media sna
TRANSCRIPT
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A project from the Social Media Research Foundation: http://www.smrfoundation.org
CHARTING COLLECTIONS OF CONNECTIONS IN SOCIAL MEDIA: CREATING MAPS AND MEASURES
WITH NODEXL
Mapping social media networks to find
Influencers, Groups & Key Topics
Marc A. SmithChief Social ScientistSocial Media Research Foundationhttp://smrfoundation.org http://nodexl.codeplex.com/http://nodexlgraphgallery.org
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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
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Crowds matter
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http://www.flickr.com/photos/amycgx/3119640267/
Crowds in social media matter
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Crowds in social media have a hidden structure
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https://demo-3dg-viz.herokuapp.com/
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http://www.bonkersworld.net/organizational-charts/
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Kodak BrownieSnap-Shot Camera
The first easy to use
point and shoot!
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https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=61133
socialmediaorg Twitter NodeXL SNA Map and Report for Saturday, 23 January 2016 at 16:45 UTC
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NodeXL Ribbon in Excel
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NodeXL in Excel
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We envision hundreds of NodeXL data collectors around the world collectively generating an 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
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https://www.nodexlgraphgallery.org/Pages/Default.aspx
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https://www.nodexlgraphgallery.org/Pages/Default.aspx
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5 AM 11 AM 5 PM 11 PM 5 AM 11 AM 5 PM 11 PM 5 AM 11 AM 5 PM 11 PM 5 AM 11 AM 5 PM 11 PM 5 AM 11 AM 5 PM 11 PM 5 AM 11 AM 5 PM 11 PM 5 AM 11 AM 5 PM 11 PM 5 AM 11 AM 5 PM 11 PM 5 AM 11 AM 5 PM 11 PM 5 AM9-Jan 10-Jan 11-Jan 12-Jan 13-Jan 14-Jan 15-Jan 16-Jan 17-Jan 18-Jan 19-Jan
Jan2016
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USAA from Twitter Search Network
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Social Media (email, Facebook, Twitter, YouTube, & more) is all about connections
from people
to people.
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Patterns are
leftbehind
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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!
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World Wide Web
Social media must contain one or more
social networks
Crowds in social media form networks
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“Think Link”Nodes & Edges
Is related to
A BIs related to
Is related to
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“Think Link”Nodes & Edges
Is related to
A BIs related to
Is related to
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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
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NodeXL imports “edges” from social media data sources
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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
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http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
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Hubs
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https://flic.kr/p/4Z6GHv
https://flic.kr/p/etEmeR
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Bridges
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Islands
http://www.flickr.com/photos/storm-crypt/3047698741
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https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=57696
#influencermarketing Twitter NodeXL SNA Map and Report for Wednesday, 25 November 2015 at 05:09 UTC
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https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=57696#headerTopVertices
InfluencerMarketing
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Top Hashtags in Tweet in Entire Graph:[10001] influencermarketing [2381] marketing [652] socialmedia [553] brand [541] klout [501] socialmediamarketing [404] contentmarketing [377] frizemedia [332] influencers [300] charlesfriedofrize
https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=57696#headerTopHashtags
InfluencerMarketing
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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/
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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.
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http://www.pewresearch.org/fact-tank/2014/02/20/the-six-types-of-twitter-conversations/
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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
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#My2K
Polarized
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#CMgrChat
In-group / Community
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Lumia
Brand / Public Topic
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#FLOTUS
Bazaar
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New York Times ArticlePaul Krugman
Broadcast: Audience + Communities
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Dell Listens/Dellcares
Support
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New Book in Progress!
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Network Analysis Data Flow
PublicationVisualizationAnalysisContainerProviders
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Social Network Maps Reveal
Key influencers in any topic.
Sub-groups.
Bridges.
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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?
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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
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Applying the insights of social networks to social media:
Your social media audience is smaller…
…than the audiences of ten influential voices.
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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
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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.
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Speak the language of the mayors
The “perfect” tweet:
.@Theirname #Theirhashtag News about your brand using their words http://your.site #Yourhashtag
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Speak the language of the mayors
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Tools for simplifying engagement: Who to say what to?
List the top “mayors” of the
topics that matter to you.
“Smart Tweet” creates content
for best engagement.
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Network phases of social media success
Phase 1: You get an audience
Phase 2b: Your audience gets an audience
Phase 3: Audience becomes community
Phase 2a: People mention you
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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!
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[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.
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Contact Me
Marc A. SmithChief Social Scientist / DirectorSocial Media Research [email protected]
http://www.smrfoundation.orghttp://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
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Examples of social network scholarship using NodeXLMargarita 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)
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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
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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.
• 1/23/2016 73
Margarita M. Orozco Doctoral Student, School of Journalism & Mass CommunicationUniversity of Wisconsin- Madison
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Katy Pearce (@katypearce)Assistant Prof of Communication Studies technology & inequality in Armenia & Azerbaijan.
#ProtestBakuAzerbaijan
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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)
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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)
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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)
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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)
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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.
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C. Scott
Dempwolf, PhD
Research Assistant Professor &
DirectorUMD - Morgan State Center for Economic
Development
http://www.terpconnect.umd.edu/~dempy/
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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
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Many papers of interest created using NodeXL can be found at http://www.pinterest.com/nodexl/pins/