This document provides an introduction to social network analysis using the NodeXL tool. It discusses how NodeXL can be used to map social media networks from platforms like Twitter. Different types of social media networks are identified, including polarized crowds, tight crowds, brand clusters, community clusters, broadcast networks, and support networks. Examples of social network scholarship are presented. Strategies for using social network analysis to improve engagement on social media are also discussed.
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...Marc Smith
Networks are a powerful way to understand social media.
This talk reviews the ways the NodeXL application can be used to reveal the social media networks structures around topics.
2014 TheNextWeb-Mapping connections with NodeXLMarc Smith
Slides from a talk at the 2014 TheNextWeb in Amsterdam.
NodeXL social media network analysis of Twitter reveals six common structures in Twitter networks.
Think Link: Network Insights with No Programming SkillsMarc Smith
Networks are everywhere, but the tools for end users to access, analyze, visualize and share insights into connected structures have been absent. NodeXL, the network overview discovery and exploration add-in for Excel makes network analysis as easy as making a pie chart.
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Networks are a powerful way to understand social media.
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Slides from a talk at the 2014 TheNextWeb in Amsterdam.
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An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.
2010 june - personal democracy forum - marc smith - mapping political socia...Marc Smith
Marc Smith's presentation to the Personal Democracy Forum 2010 in New York City on June 4th, 2010 about the use of NodeXL, a social media network analysis tool, to map political topics in services like Twitter.
NodeXL is available from http://nodexl.codeplex.com
An overview of the Network Overview Discovery and Exploration add-in for Excel 2007 (NodeXL), a social network analysis add-in for the familiar spreadsheet application. Visualize twitter, flickr, facebook, and email networks with just a few mouse clicks.
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
20110128 connected action-node xl-sea of connectionsMarc Smith
Slides for the 28 January 2011 Presentation of "Finding direction in a sea of connection" at Hartnell College in Salinas, California, sponsored by the Community Foundation for Monterey County (CFMCO.org)
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
More than ever, we need to learn how to harness the power of networks to tackle the complex issues we're facing as a society. Here's a quick guide to the basics of social network analysis.
Interested? Sign up at http://kumu.io
A presentation describing application of Node XL into analyzing social networks.
Made as part of project work for ITB course at VGSOM IIT Kharagpur.
By : Mayank Mohan
Anuradha Chakraborty
( Batch of 2012)
2013 NodeXL Social Media Network AnalysisMarc Smith
Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.
Social Network Analysis (SNA) and its implications for knowledge discovery in...ACMBangalore
Social Network Analysis (SNA) and its implications for knowledge discovery in Informal Networks- Talk by Dr Jai Ganesh, SETLabs, Infosys at Search and Social Platforms tutorial, as part of Compute 2009, ACM Bangalore
How to use social media network analysis for amplificationMarc Smith
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An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.
2010 june - personal democracy forum - marc smith - mapping political socia...Marc Smith
Marc Smith's presentation to the Personal Democracy Forum 2010 in New York City on June 4th, 2010 about the use of NodeXL, a social media network analysis tool, to map political topics in services like Twitter.
NodeXL is available from http://nodexl.codeplex.com
An overview of the Network Overview Discovery and Exploration add-in for Excel 2007 (NodeXL), a social network analysis add-in for the familiar spreadsheet application. Visualize twitter, flickr, facebook, and email networks with just a few mouse clicks.
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
20110128 connected action-node xl-sea of connectionsMarc Smith
Slides for the 28 January 2011 Presentation of "Finding direction in a sea of connection" at Hartnell College in Salinas, California, sponsored by the Community Foundation for Monterey County (CFMCO.org)
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
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More than ever, we need to learn how to harness the power of networks to tackle the complex issues we're facing as a society. Here's a quick guide to the basics of social network analysis.
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This talk will focus on the scientific analysis of social networks, how to derive useful insights from social graphs using Python, and discuss some lessons learned from taking this analysis out of the lab and into to a useful product in a production setting. I will cover acquiring data, predicting influence or activity, recommending possible friends from the graph and detecting communities within large networks. Experience with tools used to do this, including sklearn, networkx, Spark and Kafka will be covered.
The science of networks is becoming an increasingly important and intriguing area of study that reveals many a patterns and relationships often hidden. This presentation is about the use of SNA to study the network of the Digital Library Community
Social Network Analysis for Competitive IntelligenceAugust Jackson
How can CI teams apply the concepts of social network analysis to gain insight into the capabilities and plans of their competitors? Presented by Jim Richardson and August Jackson in April 2007 at the Society of Competitive Intelligence Professionals annual conference in New York City.
Tools for SNA
Page Rank Algorithm
Hierarchical Clustering
Recommendation System based on SNA (Collaborative Filtering)
How Facebook/Amazon uses SNA for recommendations?
Two Hop degree
Dynamism in Friendship Network of CSE-B
Online Social Networks and Clusters
Influential Nodes and Their Importance
Bibliography
Question - Answer Session
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Presented at 2016 POD Network conference #POD16
Many Faculty Development centers engage in far-reaching organizational development initiatives within their institutions. These initiatives are incredibly valuable but difficult to assess using traditional methods. Social network analysis (SNA) is a powerful visualization and statistical technique that has multiple applications in researching and assessing organizational development. In this session, learn how SNA was used at one institution to investigate the formation of community regarding online course quality standards as well as to analyze organizational structure for strategic planning. While this session focuses on organizational uses, examples will also be shared of applications for teaching and research.
Text analytics in Python and R with examples from Tobacco ControlBen Healey
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This presentation focuses on analysing text, with Tobacco Control as the context. Examples include monitoring mentions of NZ's smokefree goal by politicians and examining media uptake of BATNZ's Agree/Disagree PR campaign. It covers common obstacles during data extraction, cleaning and analysis, along with the key Python and R packages you can use to help clear them.
TN3270 Access to Mainframe SNA ApplicationszOSCommserver
This presentation presents the basics of the TN3270 protocol, and discusses how to configure the TN3270 server provided with z/OS Communications Server.
The basics of Python are rather straightforward. In a few minutes you can learn most of the syntax. There are some gotchas along the way that might appear tricky. This talk is meant to bring programmers up to speed with Python. They should be able to read and write Python.
Appreciating Contradications: The Cyberpsychology of Information SecurityCiarán Mc Mahon
Information security is at a critical juncture. How do we solve the weakest link - human psychology? Insight from cyberpsychology into leadership, power and persuasion are essential. These slides are from Dr Ciarán Mc Mahon's keynote at (ISC)² Security Congress EMEA, Sofitel Munich, October 2015
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IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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1. A project from the Social Media Research Foundation: http://www.smrfoundation.org
Network
mapping
the
social media
ecosystem
with
NodeXL
2. About Me
Introductions
Marc A. Smith
Chief Social Scientist / Director
Social Media Research Foundation
marc@smrfoundation.org
http://www.smrfoundation.org
http://www.codeplex.com/nodexl
http://www.twitter.com/marc_smith
http://www.linkedin.com/in/marcasmith
http://www.slideshare.net/Marc_A_Smith
http://www.flickr.com/photos/marc_smith
http://www.facebook.com/marc.smith.sociologist
16. 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
20. #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)
22. #pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 21:18 UTC
https://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
23. #pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 21:18 UTC
https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=46679
Community Cluster
Broadcast Hub
(digiphile)
Brand Cluster
(Isolates)
Community Cluster
Broadcast Hub
(mlsif)
35. 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
38. 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.
52. Social Network Maps Reveal
Key influencers in any topic.
Sub-groups.
Bridges.
53. 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?
55. Examples of social network scholarship
Margarita M. Orozco
Doctoral Student, School of Journalism &
Mass Communication
University of Wisconsin- Madison
Katy Pearce (@katypearce)
Assistant Prof of Communication
Studies technology & inequality in
Armenia & Azerbaijan.
Elena Pavan, Ph.D.
Post Doctoral Research Fellow
Dipartimento di Sociologia e Ricerca Sociale
Università di Trento
via Verdi 26, 38122 Trento (Italy)
56. Examples of social network scholarship
Margrét Vilborg Bjarnadóttir
Robert H. Smith School of Business |
University of Maryland
Data Scientist | Parliamentary
Special Investigation Commission
Prof. Diane Harris Cline
Associate Professor of History
George Washington University
C. Scott Dempwolf, PhD
Research Assistant Professor &
Director
UMD - Morgan State Center for
Economic Development
57. 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.
6/5/2015 57
UNIVERSITY OF WISC ONSIN–MADISONMargarita M. Orozco
Doctoral Student, School of Journalism & Mass Communication
University of Wisconsin- Madison
59. 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 Fellow
Dipartimento di Sociologia e Ricerca Sociale
Università di Trento
via Verdi 26, 38122 Trento (Italy)
60. 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)
61. 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)
62. 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)
63. Margrét Vilborg Bjarnadóttir
Robert 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.
65. Social Network Analysis for the humanities?
Social Network Analysis and Ancient History
Prof. Diane Harris Cline
Associate Professor of History; Affiliated faculty
member in Classical and Near Eastern
Literatures and Civilizations.
George Washington University
1. New framework for
analysis
2. Data visualization allows
new perspectives –
less linear, more
comprehensive
66. Applying the insights of
social networks to social media:
Your social media audience is smaller…
…than the audiences of
ten influential voices.
67. 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
68. 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.
69. Speak the language of the mayors
The “perfect” tweet:
.@Theirname #Theirhashtag News about your brand
using their words http://your.site #Yourhashtag
71. 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!
72. 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
73. 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)
74. [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.
75. Request your own network map and report
http://connectedaction.net
76. Monitor your topics with social network maps
• Identify the
– Key people
– Groups
– Top topics
• Locate your social media accounts within the
network
77. 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
78. 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
79. A project from the Social Media Research Foundation: http://www.smrfoundation.org
Network
mapping
the
social media
ecosystem
with
NodeXL