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Performance scale parallel and cloud computing

October 2-3, 2014: Digital Strategies for Development Summit, Asian Institute of Management, Makati City, Philippines – Mapping social media networks

22SepMay 7, 2015 By Marc Smith

20141002-AIM-DSDS-Banner

 

I attended and participated in the October 2-3, 2014 Digital Strategies for Development Summit hosted by the Asian Institute of Management and held in Makati City, Philippines.

The event gathered 50 speakers from around the world and more than 300 participants to focus on the role of digital and social technologies for civic needs.  The summit focused on bringing people from many communities into a discussion of how technology can be used for:

“…enabling a better society and an empowered community? How can various stakeholders, including Government, Private Sector and Civil Society gain more momentum for their core mandates by leveraging the use of digital technology enabled solutions? Can Digital Technology create a platform for better collaboration and cooperation amongst various stakeholders?”

I spoke about the role social network analysis can play in understanding the emerging world of social media and computer mediated collective action.

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20141002-Digital Strategies for Development Summit-Sheet

Posted in 2014, All posts, Collective Action, Common Goods, Conference, Foundation, Measuring social media, Metrics, Network clusters and communities, Network Data Archives, Network data providers (spigots), Network metrics and measures, Network visualization layouts, NodeXL, Performance scale parallel and cloud computing, Presentation, Research, SMRF, SNA, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Theories and concepts, Sociology, Talk, Talks, User interface, Visualization, Workshop

NodeXL Office Hours: Thursday 10-12 Pacific Time in Google Hangout

13JunMay 7, 2015 By Marc Smith

2013-NodeXL Office Hours in Google Hangout

Hello!  Each Thursday at 10AM to noon (Pacific Time), I will be taking questions and providing support to NodeXL users in a Google Hangout.  Join me for a Q&A about NodeXL, SNA, Social Media, Networks, Mapping, Visualization and Analytics.

Posted in 2013, All posts, Measuring social media, Network Data Archives, Network data providers (spigots), Network metrics and measures, Network visualization layouts, NodeXL, Performance scale parallel and cloud computing, Presentation, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Talks, User interface, Visualization, Workshop Tagged Google, Hangout, NodeXL, Office Hours, Support, Technical 5 Comments

9 August 2011 – Social Media SNA Workshop – Association for Education in Journalism and Mass Communication (http://www.aejmc.com/)

31JulMay 7, 2015 By Marc Smith

Here is a map of the connections among the people who tweeted the term “AEJMC” on August 7, 2011:

The top most between people in this network are:@aejmc, @jlab, @karenrussell, @terryflynn, @natcomm, @tmccorkindale, @derigansilver, @tkell, @aejmconlineads, and @jeremyhl:

I will present a Workshop on Social Media Network Analysis and NodeXL at the 9 August 2011 – Association for Education in Journalism and Mass Communication (http://www.aejmc.com) in St. Louis, Missouri along with my colleague Professor Hernando Rojas, from the School of Journalism & Mass Communication, University of Wisconsin – Madison.

See: http://www.aejmc.com/home/events/annual-convention/

The Association for Education in Journalism and Mass Communication (AEJMC) is a nonprofit, educational association of journalism and mass communication educators, students and media professionals. The Association’s mission is to promote the highest possible standards for journalism and mass communication education, to cultivate the widest possible range of communication research, to encourage the implementation of a multi-cultural society in the classroom and curriculum, and to defend and maintain freedom of communication in an effort to achieve better professional practice and a better informed public.

Our session is:

Using NodeXL for Social Network Analysis
Tuesday — 
2 pm to 5 pm
Presented by Communication Theory and Methodology Division
This pre-conference workshop examines social network analysis. Social network analysis can be used to examine message boards, blogs, and friend networks (amongmany other phenomena). Participants will learn to use the NodeXL program to conduct a network analysis. For information, contact Michel M. Haigh, Pennsylvania State University at mmh25@psu.edu.

 

Posted in All posts, Collective Action, Common Goods, Community, Conference, Foundation, Measuring social media, Metrics, Network clusters and communities, Network data providers (spigots), Network metrics and measures, Network visualization layouts, NodeXL, Performance scale parallel and cloud computing, SMRF, Social Media, Social network, Social Network Analysis, Social Roles, Social Theories and concepts, Sociology, Technology, User interface, Visualization Tagged 2011, AEJMC, Analysis, August, Chart, Conference, Diagram, Education, Journalism, Map, Marc, Marc Smith, Marc_Smith, Mass Communication, network, NodeXL, Smith, SNA, Social Media, Social network, Visualization, workshop

July 17 – July 23, 2011 – NodeXL Session at Computational Social Science Workshop, Lipari Island, Italy

25AprMay 7, 2015 By Marc Smith


Logo
Lipari

I will be speaking at the Jacob T. Schwartz International School for Scientific Research week long Lipari School on Computational Social Science , July 17 – July 23, 2011, Lipari Island, Italy.

This year’s program is dedicated to Computational Social Science: Text and Decisions

Speakers:

  • Claudio Cioffi-Revilla: Director of the Center for Social Complexity, Krasnow Institute for Advanced Study, George Mason University, Washington DC.
  • Huan Liu: Community Detection and Mining in Social Media [abstract]
    School of Computing, Informatics, and Decision Systems Engineering, Arizona State University
  • Roel Popping: Computer-assisted text analysis, and the relevance of decision making and text mining [abstract]
    Department of Sociology, University of Groningen

Tutorials

  • Marc A. Smith: Charting Collections of Connections in Social Media: Maps and Measures with NodeXL [abstract]
    Chief Social Scientist, Connected Action Consulting Group
  • Calogero Zarba: Introduction to matrix algebra [abstract]
    Neodata Intelligence s.r.l., Italy
  • Alessandro Pluchino: Netlogo: An agent based simulation programmable environment [abstract], University of Catania, Italy
Posted in All posts, Collective Action, Common Goods, Community, Conference, Measuring social media, Metrics, Mobile Devices, Mobile Social Software, Network clusters and communities, Network data providers (spigots), Network metrics and measures, Network visualization layouts, NodeXL, Performance scale parallel and cloud computing, Research, Social Interaction, Social Media, Social network, Social Network Analysis, Social Roles, Sociology, Talks, Technology, University, User interface, Visualization Tagged 2011, Analysis, Italy, July, Lecture, Lipari, Marc Smith, network, NodeXL, Presentation, SNA, social, Talk, Tutorial, workshop

Aggregate Overall Metrics Feature: Finding patterns in collections of many networks using NodeXL

28DecMay 7, 2015 By Marc Smith

Once you start creating and collecting network graphs you may find you can build a significant collection: hundreds, thousands or tens of thousands of graphs may result from a study or on-going monitoring project. In a series of features in the NodeXL project we have enabled a workflow for constructing many social media  network graphs using the Network Server component (see: How to schedule the creation of a network with NodeXL and Windows Task Scheduler and: New NodeXL Network Server (v1.0.1.126) – Frequently Asked Questions).  This can result in a collection of *many* NodeXL (and GraphML) network files.  Then we implemented features that enabled “Automation”, the application of many operations in NodeXL (metrics calculation, autofill columns, layout and more) to many files without direct human engagement (see: Automatic for the people (who use the latest NodeXL!). Release v.1.0.1.128 and: Fully automatic: NodeXL can build your network graphs hands free).

A single workbook may contain data from a single NodeXL data collection, run on a particular day and collecting data from a few hours or days back from that moment  (depending on factors like the volume of activity around the selected keyword and the depth of the twitter search catalog, which is often not more than a week or two long and much shorter for active topics).  An example of a single network slice is this recent map of the connections among people who mentioned “microsoft research” in Twitter on a single day (December 18th, 2010):

:

This is a single slice of the network, a day out of months of activity.  A still frame can tell a rich story: this is a picture of a crowd that has gathered to discuss a topic of common interest: “microsoft research“.  It illustrates a structure common to many large discussions of popular topics — a large set of isolates (the rows at the bottom) who were not observed to have a followed, mentions, or replies relationship to anyone else who tweeted the same term.  These are casual mentioners of the topic.  At the end of these rows are a small number of dyads, triads, and small components of a handful of people who link to one another but not to the largest connected component. These are pairs or small groups discussing the topic among themselves, but none are connected to a larger component.  Above these rows is the “giant component” — the blob of people who do have a connection to someone else who also tweeted a message containing the same term who in turn have a connection that leads to a large number of others.  The giant component is itself composed of several sub-components of densely connected groups.  At the center of each component are the core users, the people who often hold their cluster together. Between these clusters are the bridges, the people who link otherwise disconnected sub-groups.  At the edges are the peripheral people who have just taken the first step up from being an isolate and have formed a single reply, mention, or follows relationship to someone else who also tweeted the search keyword and can bridge them back to the core of the giant component.  This is a large and active network with hybrid qualities.  There is a “brand” or broadcast element in it: the yellow cluster is a hub and spoke structure centered on the Microsoft Research Twitter account.  These people re-tweet what this account publishes but do not connect to one another.  Just a few of these people set off second and third waves of retweets.  Elsewhere in the graph there are other network structures present, for example the green and blue clusters feature people are centered around their own discussions of the term “microsoft research“.

If you collect many still frames of slices of network activity there is great value in exploring the way the network graph changes over time.  In the most recent release NodeXL provides the first step in a series of features related to time and graph comparison.  You can now create a workbook that aggregates the overall metrics (edge counts, vertex counts, connected component counts, etc.) for a folder full of NodeXL workbooks. In NodeXL follow the menu path: NodeXL>Analysis>Graph Metrics>Aggregate Overall Metrics to get this:

The result of this feature is a workbook with a row containing the summary data from each of the workbooks in the target folder.  Any arbitrary collection of network workbooks can be aggregated but this is particularly useful when the workbooks are sequential time slices.

An example is the time series plot below tracking the rise and fall of several Twitter volume and network measures for the “microsoft research” search term over several months:

This chart tracks the number of vertices (each vertex in this case is a person  our data collector saw tweet about the search term “microsoft research“) in each (almost) daily network snapshot.  In addition the unique edges or connections between these Twitter users are plotted along with the number of people with no connections (“Single-Vertex Connected Components”).  The size of the largest component in the network (“Maximum vertices in a connected component “) is a measure of the changing size of the core community of discussion participants.  Measures like the maximum and average “geodesic” distance provide a rough measure of how long and thin (high values) or generally spherical (low values) a particular network is shaped. A “geodesic” is the longest path that can be walked through the network.  Long skinny networks may indicate the presence of loosely connected smaller groups that have a few people who act as bridges.  Low geodesic values suggest dense networks with people connected to many others with few isolates and sub-groups.

The peaks are closely associated with major events on the Microsoft Research calendar, like the 2010 Microsoft Research Faculty Summit event I attended in early July.

I find the ratios between measures of the size of the large network component and the population of isolates to be interesting.  As events go on over a period of days more people connect with others who are talking about the same topic, growing the size of the large connected component.  But often the isolate population also grows during this time as people at the periphery of the topic network catch sight of mentions of the event and tweet about it.  I could imagine one goal of social media management to be the conversion of isolates to connected component members.  Those who follow, reply or mention even a single other person also talking about a topic are more likely to return and engage than those who have zero connections.  It is not clear if more connections provide a linear increase in continued engagement, I suspect that the main effect is at the zero/one divide and drops off in effect after the first dozen or so connections.  Encouraging cohesion and network density by replying to isolates and encouraging others to do so may help keep a social media population focused and growing.

This feature follows the work done in the ManyNets project (http://www.cs.umd.edu/hcil/manynets/) at the University of Maryland by Manuel Freire, Catherine Plaisant, Ben Shneiderman, Awalin Sopan, and Miguel Rios.  ManyNets also created a framework for managing the metadata about collections of networks. ManyNets provides for  much richer interactions and linkages to the underlying networks than NodeXL can do so far.

Posted in All posts, Network metrics and measures, NodeXL, Performance scale parallel and cloud computing, Social Media, Social network, Visualization Tagged 2010, Analysis, Chart, Data management, Feature, File management, graph, Map, Multiple files, network, NodeXL, Series, SMRF, SMRFoundation, SNA, social, Social Media Research Foundation, social network analysis, Time, Visualization

How to run NodeXL on a connected Mac (or other platform) using Amazon EC2

16NovMay 7, 2015 By Marc Smith

I have a MacBookPro.  I can run Windows very nicely using various Virtual Machine products like VMWare’s Fusion or the free and open VirtualBox.  In these virtual machines I can run Windows and Office and then NodeXL.  It’s pretty neat to see Windows inside the Mac OS window.  I am always amazed that it works at all.

But I try to avoid doing this as the main way I run NodeXL at almost any cost.  My Mac session slows to a crawl, my Windows session is slow, and the overall usability of the system degrades too much.  If you focus on JUST NodeXL in the VM it works well, but context switching is too demanding.  So I mostly focus on NodeXL on a Windows machine.  When I travel I usually just have my Mac laptop and miss having a zippy version of NodeXL at hand.

Recently, I discovered that Amazon EC2 offers a remarkable way to have my cake and eat it too: I created a modestly powered .micro instance of Windows XP and Office 2010 and installed NodeXL.  I then use the Microsoft Remote Desktop Client for Mac OS X to get a window into the remote virtual instance.  This system can be a bit slow, it responds a bit like a NetBook, but it does not slow down my Mac OS X instance and can be reached from any machine with the Remote Desktop client and internet access.  For those who need a more speedy response or to handle larger data sets, Amazon is happy to sell more powerful instances of Windows for modestly more pennies per hour.  For example, the .small instance is merely $0.13/hour or $3.12/day and offers much faster responses. Of course, if you turn the instance off when you are not using it, Amazon does not charge anything.

Recently Adam Fields, my colleague at Morningside-Analytics did me the great favor of recording the process of creating a new .micro instance of an Amazon EC2 Windows system.

aws_instance_creation
Following each step will lead you to the creation of your own virtual machine into which you can install Office and NodeXL.  You can then access this image, which runs continuously until you Terminate it – even when you close computer you use to remote desktop.  This is both a bug and a feature – the system runs no matter the state of the computer you use to access it.  Just remember to really turn off the instance if you do not want to pay a recurring fee! You have to actually _Terminate_ the instance to avoid paying fees shutting it down isn’t enough.

Also note that the AMI Adam used in the video was a 64-bit one, which doesn’t have a “small” option, it jumps straight from micro to large. If you want the small option, you have to use the 32-bit AMI, which is right above the one he picked.

Posted in All posts, Connected Action, NodeXL, Performance scale parallel and cloud computing, Social network, Web Application Tagged 2010, Access, Amazon, AWS, cross-platform, EC2, Linux, Mac, NodeXL, Remote, Remote Desktop, Service, SMRF, SMRFoundation, SNA, Social Media Research Foundation, Virtual Machine, VirtualBox, VM, VMWare, Web 6 Comments

Book: Flier and Cover Art – Analyzing social media networks with NodeXL: Insights from a connected world

19JunMay 7, 2015 By Marc Smith

The production team at Morgan-Kaufmann have created a cover and a flier for the forthcoming book:

2010 – June – NodeXL Book Flyer.

Written and edited by Derek Hansen, Ben Shneiderman and Marc Smith, the book contains contributed chapters on sample social media systems:

[Chapter 10]: Twitter: Conversation, Entertainment and Information, All in One Network!

By Vladimir Barash and Scott Golder

[Chapter 11]: Visualizing and Interpreting Facebook Networks

By Bernie Hogan

[Chapter 12]: WWW Hyperlink Networks

By Robert Ackland

[Chapter 13]: Flickr: Linking People, Photos, and Tags

By Eduarda Mendes Rodrigues and Natasa Milic-Frayling

[Chapter 14]: YouTube: Contrasting Patterns of Interaction and Prominence

By Dana Rotman and Jennifer Golbeck

[Chapter 15]: Wiki Networks: Networks of Creativity and Collaboration

By Howard T Welser, Patrick Underwood, Dan Cosley, Derek Hansen, and Laura Black

This handy poster contains many details about the book contributors, chapters, and the book cover (which you can also see below):

2010 - Book - Analyzing Social Media Networks with NodeXL Cover

Analyzing Social Media Networks with NodeXL: Insights from a Connected World

Posted in All posts, Book, Collective Action, Common Goods, Community, Connected Action, Maryland, Measuring social media, Metrics, Network clusters and communities, Network data providers (spigots), Network metrics and measures, Network visualization layouts, NodeXL, Performance scale parallel and cloud computing, Research, Social Media, Social network, Social Network Analysis, Social Roles, Sociology, University, User interface, Visualization Tagged 2010, Art, Ben Shneiderman, Book, Chart, class, Cover, Derek Hansen, Flier, graph, Hansen, learn, Map, Marc Smith, Maryland, Morgan Kaufmann, NodeXL, Promotional materials, Shneiderman, SNA, Social Media, social network analysis, teach, textbook

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Transparency in Social Media

2015-07-30-Transparency in Social Media-Structures of Twitter Crowds and COnversations
Transparency in Social Media
Sorin Adam Matei, Martha G. Russell, Elisa Bertino

CÓMO ENCONTRAR LOS HASHTAGS MÁS POTENTES: Para convertir LEADS a VENTAS (SEOHashtag nº 1) (Spanish Edition)

Apply NodeXL in espanol!

CÓMO ENCONTRAR LOS HASHTAGS MÁS POTENTES - Para convertir LEADS a VENTAS (SEOHashtag nº 1) (Spanish Edition)
By: Vivian Francos from #SEOHashtag Comparto algunas de las mejores formas de elegir los hashtags más poderosos y
que puedan generar tráfico a tus redes sociales para aprovechar el poder del
hashtag.
Si quieres aumentar tus interacciones, debes aprender a utilizar los hashtags como herramienta.

https://amzn.to/305Hpsv

Networked


Networked By Lee Rainie and Barry Wellman

Social Media in the Public Sector

2015-07-31Social Media in the Public Sector-Cover
Ines Mergel

Ways of Knowing in HCI

2014-Ways of Knowing in HCI - Olson and Kellogg

The Virtual Community


Virtual Community

The Evolution of Cooperation


The Evolution of Cooperation

Governing the Commons


Governing the Commons

SmartMobs


SmartMobs

Networks, Crowds, and Markets


Networks, Crowds, and Markets

Development of Social Network Analysis


Development of Social Network Analysis: A Study in the Sociology of Science

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