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.
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.
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.
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.
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.
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.
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)
Marc Smith - Charting Collections of Connections in Social Media: Creating Ma...Saratoga
An advocate for open tools, open data and open scholarships, Marc Smith strives for access to information to be available to all. Pioneering the possibilities through charting collections and creating maps with NodeXL.
Slides from talks presented at Mammoth BI in Cape Town on 17 November 2014.
Visit www.mammothbi.co.za for details on the event. Follow @MammothBI on twitter.
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
This is a presentation for parents.
It commences with a quiz to see what they know about online profiles and goes on to give some facts and trends about the things students are up to online and why parents should support them.
This is a presentation for clients at the salon at the Mandarin in HK. It has a quiz that works with Qwizdom Actionpoint and then allows for a discussion of the need to take hold of your online identity.
Professor Hendrik Speck - Social and Virtual. - An Analysis Framework for Lar...Hendrik Speck
Professor Hendrik Speck - Social and Virtual. - An Analysis Framework for Large Scale Communities. Commercial Communities Conference. Technical University of Berlin. Institute of Sociology, October 30 - 31 2008, Berlin, Germany, User Generated Content, Interaction, Third Party Associations and Content, Access and Connectivity, API's, Beacons, and Data Feeds, Merger of Social, Mobile and Local, social network analysis, social network visualization, Audience and Participants, Relational Data, Mathematical Models, Analytical Framework, Processing, Computing Power, Computer Mediated Communication, Visualization Algorithms, Interest, Use Cases, Marketing, Commerce, Web Services, Type of Data, Attribute, Ideational, Relational, Research Method, Survey Research, Surveys and Interviews, Ethographic Research, Observations, Field Studies, Documentary Research
Logfiles, Texts and Archives, Type of Analysis, Variable, Typological, Network, User Profiles. Name, Age, Links, Interests, Hobbies, City, Country, Category, Videos Headline, Content, Descriptions, Tags, Playlists, Video Comments Author, Text, Tags, Themes, Ranking of Users and Channels Views or Subscriptions by Time and Category, Rankings of Videos Ratings or Views by Time and Category, Interaction Friends, Subscription, Comments, FollowUps
Betweenness, Centrality Closeness, Centrality Degree, Flow Betweenness Centrality, Centrality Eigenvector, Centralization, Clustering Coefficient, Cohesion, Contagion, Density, Integration, Path Length, Radiality, Reach, Structural Equivalence, Structural Hole, Islands
Professor Hendrik Speck - Information Mining in the Social Web. Empolis Execu...Hendrik Speck
Professor Hendrik Speck - Information Mining in the Social Web. Empolis Executive Forum, June 8th 9th 2009 Berlin Germany. social networks, social media, web 2.0, social network analysis, usage, audience, user, markets, revenues, google, youtube, myspace, wikipedia, attributes, search engines, marketing, lobbying, information mining, information retrieval, risk, law, security, branding, marketing, privacy, private sphere, public sphere, anonymity, surveillance, panopticon, sousveillance, hype, history, features, examples, captcha, security, cracking, data portability, decentralization
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.
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)
Marc Smith - Charting Collections of Connections in Social Media: Creating Ma...Saratoga
An advocate for open tools, open data and open scholarships, Marc Smith strives for access to information to be available to all. Pioneering the possibilities through charting collections and creating maps with NodeXL.
Slides from talks presented at Mammoth BI in Cape Town on 17 November 2014.
Visit www.mammothbi.co.za for details on the event. Follow @MammothBI on twitter.
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
This is a presentation for parents.
It commences with a quiz to see what they know about online profiles and goes on to give some facts and trends about the things students are up to online and why parents should support them.
This is a presentation for clients at the salon at the Mandarin in HK. It has a quiz that works with Qwizdom Actionpoint and then allows for a discussion of the need to take hold of your online identity.
Professor Hendrik Speck - Social and Virtual. - An Analysis Framework for Lar...Hendrik Speck
Professor Hendrik Speck - Social and Virtual. - An Analysis Framework for Large Scale Communities. Commercial Communities Conference. Technical University of Berlin. Institute of Sociology, October 30 - 31 2008, Berlin, Germany, User Generated Content, Interaction, Third Party Associations and Content, Access and Connectivity, API's, Beacons, and Data Feeds, Merger of Social, Mobile and Local, social network analysis, social network visualization, Audience and Participants, Relational Data, Mathematical Models, Analytical Framework, Processing, Computing Power, Computer Mediated Communication, Visualization Algorithms, Interest, Use Cases, Marketing, Commerce, Web Services, Type of Data, Attribute, Ideational, Relational, Research Method, Survey Research, Surveys and Interviews, Ethographic Research, Observations, Field Studies, Documentary Research
Logfiles, Texts and Archives, Type of Analysis, Variable, Typological, Network, User Profiles. Name, Age, Links, Interests, Hobbies, City, Country, Category, Videos Headline, Content, Descriptions, Tags, Playlists, Video Comments Author, Text, Tags, Themes, Ranking of Users and Channels Views or Subscriptions by Time and Category, Rankings of Videos Ratings or Views by Time and Category, Interaction Friends, Subscription, Comments, FollowUps
Betweenness, Centrality Closeness, Centrality Degree, Flow Betweenness Centrality, Centrality Eigenvector, Centralization, Clustering Coefficient, Cohesion, Contagion, Density, Integration, Path Length, Radiality, Reach, Structural Equivalence, Structural Hole, Islands
Professor Hendrik Speck - Information Mining in the Social Web. Empolis Execu...Hendrik Speck
Professor Hendrik Speck - Information Mining in the Social Web. Empolis Executive Forum, June 8th 9th 2009 Berlin Germany. social networks, social media, web 2.0, social network analysis, usage, audience, user, markets, revenues, google, youtube, myspace, wikipedia, attributes, search engines, marketing, lobbying, information mining, information retrieval, risk, law, security, branding, marketing, privacy, private sphere, public sphere, anonymity, surveillance, panopticon, sousveillance, hype, history, features, examples, captcha, security, cracking, data portability, decentralization
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.
LSS'11: Charting Collections Of Connections In Social MediaLocal Social Summit
Keynote Title: Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
Abstract: Networks are a data structure common found across all social media services that allow populations to author collections of connections. The Social Media Research Foundation‘s NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, Networks become as easy to create as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, and email networks will be presented.
Slides for talk at ConTech 2011 the International Symposium on Convergence Technology (ConTech 2011) – Smart & Humane World – on November 3rd in Seoul, South Korea.
Date: 2011 November 3 (Thurs)
Place: COEX Grand Ballroom, Seoul, Korea
Organized by Advanced Institutes of Convergence Technologies (AICT), Seoul National University (SNU)
In Cooperation with Ministry of Knowledge Economy, Ministry of Education, Science and Technology, National Research Foundation of Korea, Graduate School of Convergence Science and Technology (GSCST)
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.
New Metrics for New Media Bay Area CIO IT Executives MeetupTatyana Kanzaveli
Presentation done by Marc Smith, Chief Social Scientist, Telligent at the Bay Area CIO/IT Executives meetup http://www.meetup.com/CIO-IT-Executives/ run by Tatyana Kanzaveli.
Immersive Recommendation incorporates cross-platform and diverse personal digital traces into recommendations. Our context-aware topic modeling algorithm systematically profiles users' interests based on their traces from different contexts, and our hybrid recommendation algorithm makes high-quality recommendations by fusing users' personal profiles, item profiles, and existing ratings. The proposed model showed significant improvement over the state-of-the-art algorithms, suggesting the value of using this new user-centric recommendation model to improve recommendation quality, including in cold-start situations.
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
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.
"Friendsters @ Work" - a presentation on the Context, Content & Community Collage proactive display application at the Emerging Tech SIG of the SDForum, 12 December 2007
How to use social media network analysis for amplificationMarc Smith
How can social media network analysis help you get your message out? Use network maps of social media to identify the most influential contributors based on their location within the network. Use content analysis to identify the topics, hashtags and URLs of greatest interest to the "mayors" of the hashtags that matter to you.
A network is a collection of connections. Learn how to express an edge in NodeXL. If you can make a pie chart, you can now make a network chart in NodeXL,
Course description for Optimice class on organizational network analysis (ONA) - the application of social network analysis (SNA) to business organizations.
20110830 Introducing the Social Media Research FoundationMarc Smith
Description of the activities and goals of the Social Media Research Foundation. The foundation is dedicated to Open Tools, Open Data, and Open Scholarship. See: http://www.smrfoundation.org.
One project from the Social Media Research Foundation is NodeXL, the network overview, discovery and exploration add-in for Excel 2007/2010. See: http://www.smrfoundation.org
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
3. About Me
Marc A. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
http://www.connectedaction.net
http://www.codeplex.com/nodexl
http://www.twitter.com/marc_smith
http://delicious.com/marc_smith/Paper
http://www.flickr.com/photos/marc_smith
http://www.facebook.com/marc.smith.sociologist
http://www.linkedin.com/in/marcasmith
http://www.slideshare.net/Marc_A_Smith April 10-12, Chicago, IL
http://www.smrfoundation.org
13. A network is born whenever two GUIDs are joined.
Username Attributes Username Attributes
@UserName1 Value, value @UserName2 Value, value
A B
Vertex1 Vertex 2 “Edge” “Vertex1” “Vertex2”
Attribute Attribute Attribute
@UserName1 @UserName2 value value value
16. Social
Networks
Jacob Moreno ’ s early
social network diagram of
History:
positive and negative
relationships among
from the dawn of time!
members of a football
team.
Theory and method: Originally published in
1934 ->
Moreno, J. L. (1934). Who
shall survive? Washington,
Jacob L. Moreno
DC: Nervous and Mental
Disease Publishing
Company.
http://en.wikipedia.org/wiki/Jacob_L._Moren 16
17.
18. Social network diagram of relationships among workers in a factory illustrates the positions
different workers occupy within the workgroup.
Originally published in Roethlisberger, F., and Dickson, W. (1939). Management and
the worker. Cambridge, UK: Cambridge University Press.
41. Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith. 2007.
Visualizing the Signatures of Social Roles in Online Discussion Groups.
The Journal of Social Structure. 8(2).
Experts & Discussion people
Discussion starters
“Answer People” Topic setters
Topic setters
41
42. NodeXL: Network Overview Discovery and Exploration add-in for Excel 2007/2010
A minimal network can illustrate
the ways different locations
have different values for
centrality and degree
42
52. 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 do #SQLPass and #PASSBAC look like?
52
59. Social Network Theory
http://en.wikipedia.org/wiki/Social_network
Central tenet
Social structure emerges from the aggregate of relationships (ties)
among members of a population
Phenomena of interest
Emergence of cliques and clusters from patterns of relationships
Centrality (core), periphery (isolates), betweenness Source: Richards, W.
(1986). The NEGOPY
network analysis
Methods program. Burnaby, BC:
Department of
Surveys, interviews, observations, log file analysis, computational Communication, Simon
analysis of matrices Fraser University. pp.7-
16
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)
59
60. SNA 101 • Node
– “actor” on which relationships act; 1-mode versus 2-mode networks
• Edge
A – Relationship connecting nodes; can be directional
• Cohesive Sub-Group
– Well-connected group; clique; cluster A B D E
B C • Key Metrics
– Centrality (group or individual measure)
• Number of direct connections that individuals have with others in the group (usually look at
D incoming connections only)
• Measure at the individual node or group level
E – Cohesion (group measure)
• Ease with which a network can connect
• Aggregate measure of shortest path between each node pair at network level reflects
average distance
– Density (group measure)
• Robustness of the network
• Number of connections that exist in the group out of 100% possible
F G – Betweenness (individual measure)
• # shortest paths between each node pair that a node is on
• Measure at the individual node level
H I • Node roles
– Peripheral – below average centrality C
– Central connector – above average centrality D
– Broker – above average betweenness E
61. NodeXL: Free/Open Social Network Analysis add-in for Excel 2007/2010
makes graph theory as easy as a pie chart, with integrated analysis of social
media sources. See: http://nodexl.codeplex.com
61
63. Goal: Make SNA easier
• Existing Social Network Tools are challenging for many novice users
• Tools like Excel are widely used
• Leveraging a spreadsheet as a host for SNA lowers barriers to
network data analysis and display
63
68. The Content summary
spreadsheet displays the most
frequently used URLs, hashtags,
and user names within the
network as a whole and within
each calculated sub-group.
68
75. Social Media Research Foundation
People Disciplines Institutions
University Faculty Computer Science University of Maryland
Students HCI, CSCW Oxford Internet Institute
Industry Machine Learning Stanford University
Independent Information Visualization Microsoft Research
Researchers UI/UX Illinois Institute of Technology
Developers Social Science/Sociology Connected Action
Network Analysis Cornell
Collective Action Morningside Analytics
75
76. What we are trying to do:
Open Tools, Open Data, Open Scholarship
Build the “Firefox of GraphML” – open tools for collecting and
visualizing social media data
Connect users to network analysis – make
network charts as easy as making a pie chart
Connect researchers to social media data sources
Archive: Be the “Allen Very Large Telescope Array” for Social
Media data – coordinate and aggregate the results of many user’s
data collection and analysis
Create open access research papers & findings
Make “collections of connections” easy for users to manage
76
77. What we have done: Open Tools
NodeXL
Data providers (“spigots”)
• ThreadMill Message Board
• Exchange Enterprise Email
• Voson Hyperlink
• SharePoint
• Facebook
• Twitter
• YouTube
• Flickr
77
78. What we have done: Open Data
NodeXLGraphGallery.org
• User generated collection of network
graphs, datasets and annotations
• Collective repository for the research
community
• Published collections of data from a
range of social media data sources to
help students and researchers connect
with data of interest and relevance
78
81. 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, 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
81
82. 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
82
83. Charting Collections of Social
Media Connections with NodeXL
Maps and reports for social media networks
April 10-12, Chicago, IL
84. Win a Microsoft Surface Pro!
Complete an online SESSION EVALUATION
to be entered into the draw.
Draw closes April 12, 11:59pm CT
Winners will be announced on the PASS BA
Conference website and on Twitter.
Go to passbaconference.com/evals or follow the QR code link displayed on
session signage throughout the conference venue.
Your feedback is important and valuable. All feedback will be used to improve
and select sessions for future events.
84
A tutorial on analyzing social media networks is available from: casci.umd.edu/NodeXL_TeachingDifferent positions within a network can be measured using network metrics.
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.
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.
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.
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.
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.
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.