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social network analysis

DC on Nov 13 at 8:30AM: IREX Social Network Analysis: Influence and Impact Beyond Likes and Retweets

05NovMay 7, 2015 By Marc Smith

2013-IREX-45th logo

ICT4D NodeXL SNA Map

I will participate in a workshop at IREX in Washington D.C. on November 13, 2013.

The workshop is titled Social Network Analysis: Influence and Impact Beyond Likes and Retweets.  We will focus on the applications of social network analysis for development efforts, exploring how SNA can:

  • Create viral and influential advocacy and political campaigns
  • Find business and employment connections for entrepreneurs and youth
  • Identify hidden disease vectors and stop new infection pathways
  • Break circles of government corruption and graft
  • Target existing informal support resources for disaster response

The workshop will be facilitated by Wayan Vota along with three social network analysis researchers:

  1. Marc Smith, Social Media Research Foundation and NodeXL
  2. Rohan Grover, Upworthy  and  People For the American Way
  3. Behar Xharra, Kosovo Diaspora

This Deep Dive will be an active event. We will mix thoughtful discussions with experiential activities, building social capital while we learn about social networks. Participants are encouraged to submit social media topics in advance so maps and reports can be generated for the event.

RSVP-Now-Button
Note that this event is in-person only, so please RSVP now to attend.

How Social Network Analysis Can Improve Impact
IREX Tech Deep Dive
8:30am-12:30pm,
November 13th, 2013
IREX Headquarters
Washington, DC

 

 

Posted in 2013, All posts, Connected Action, Foundation, Measuring social media, Metrics, NodeXL, Presentation, Research, SMRF, SNA, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Theories and concepts, Sociology, Talk, Talks, Technology, Training, Visualization, Workshop Tagged applications, Development, Hands-on, ICT, ICT4D, IREX, Marc Smith, NodeXL, Research, SMRF, SNA, Social Media Research Foundation, Social network, social network analysis, Talk, Tutorial, workshop

Video: Webinar on Data Visualization and NodeXL, hosted by SoftArtisans

12SepMay 7, 2015 By Marc Smith

I spoke in a webinar on Data Visualization and NodeXL  hosted by SoftArtisans and now available on Vimeo.

Thanks to Claire and Elise!

Posted in 2013, All posts, Collective Action, Connected Action, Foundation, Measuring social media, Metrics, NodeXL, Presentation, Research, SMRF, SNA, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Social Theories and concepts, Sociology, Talk, Talks, Training, Video, Visualization, Workshop Tagged Marc Smith, network, NodeXL, Slides, SMRF, SNA, Social Media, Social Media Research Foundation, social network analysis, Training, Twitter, Video, Vimeo, Webinar 7 Comments

December 15, 2011 – @IFTF NodeXL & Gephi – Social Media Mapping Open House

07DecMay 7, 2015 By Marc Smith

Online Ticketing powered by Eventbrite
NodeXL Event at IFTF, Thursday, December 15, 2011
Along with the Social Media Research Foundation, the Institute for the Future is co-hosting a meetup for those interested in mapping social media networks. Users of tools like NodeXL and Gephi (among others) are welcome to join us for an evening devoted to collecting, analyzing, and visualizing social media networks. Thursday, December 15th at 6pm at the Institute for the Future‘s offices in Palo Alto at 124 University Avenue, 2nd floor.

Online Ticketing for NodeXL/Social Media Network Mapping powered by Eventbrite
Posted in All posts, Conference, Foundation, Gephi, IFTF, Measuring social media, Metrics, NodeXL, Research, SMRF, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Sociology, Talks, Visualization Tagged 2011, Analysis, Chart, graph, IFTF, Institute for the Future, Map, MeetUp, network, NodeXL, Palo Alto, SMRF, SNA, Social Media, Social Media Research Foundation, social network analysis

November 8, 2011: University of Manchester, NodeXL SNA / Social Media Workshop

05NovMay 7, 2015 By Marc Smith

Methodologies for Web and Social Media Data Analysis in Social Science and Policy Research

CCSR Short Course
Social Media Network Analysis using NodeXL

November 9th  9.00 am – 5.30 pm.

Marc Smith
Social Media Research Foundation

http://www.smrfoundation.org

Course Summary: Networks are everywhere in the natural and social world.  New tools are making the task of getting, processing, measuring, visualizing and gaining insights from network data sets easier than ever before.  The rise of social media offers a new and abundant source of network data.  The NodeXL project (http://www.codeplex.com/nodexl) from the Social Media Research Foundation (http://www.smrfoundation.org) offers a free and open path to network overview, discovery and exploration within the context of the familiar Excel spreadsheet.  In this short course we will introduce the NodeXL application and review the landscape of networks, social networks, and social media networks. Using the tool, non-programmers can quickly select a network of interest from various social media and other data sources.  Twitter, flickr, YouTube, email, the World Wide Web, and Facebook data can be quickly imported into NodeXL.  Networks can then be analyzed and visualized using tools similar to those used to create a pie chart or line graph [1].  As the challenge and cost of network acquisition and analysis drops, abundant data sets are being generated that document the range of variation of diverse sources of social media.  How many different kinds of Twitter hashtags exist?  Using snapshots of hundreds of hashtags collected over a year, it is now possible to build rough taxonomies of this kind of social media.  NodeXL provides access to a web gallery of data [2], allowing users to browse existing data sets and upload their own as well. Borrowing the vision of telescope arrays that create composite images far better than any individual instrument could, the Social Media Research Foundation envisions an user generated archive that provides a research asset that supports the collective effort to understand the structures and dynamics of network data.

[1] NodeXL Image Gallery: http://www.flickr.com/photos/marc_smith/sets/72157622437066929/
[2] NodeXL Graph Gallery: http://nodexlgraphgallery.org

Course Objectives
After this course, participants will:

(1) Be familiar with the basic concepts of networks, social networks and social media networks
(2) Understand the core features of the NodeXL network analysis and visualization tool
(3) Review images and data sets for dozens of different social media networks
(4) Learn to identify general types of social media networks along with the key people and groups within them

Target Audience
This course is suitable for people with some experience or interest in social media, social science, or social network analysis.  It is particularly appropriate for those who are involved in studying social structures and their change over time.

Laboratory and IT requirements:
Participants will need access to a computer connected to the Internet  and will be supplied with the free NodeXL software.

Suggested Reading
Analyzing social media networks with NodeXL: Insights from a connected world
http://www.amazon.com/gp/product/0123822297?ie=UTF8&tag=conneactio-20&linkCode=as2&camp=1789&creative=390957&creativeASIN=0123822297

EventGraphs:
http://www.cs.umd.edu/localphp/hcil/tech-reports-search.php?number=2010-13

Visualizing the Signatures of Social Roles in Online Discussion Groups:
http://www.cmu.edu/joss/content/articles/volume8/Welser/

Discussion catalysts in online political discussions: Content importers and conversation starters
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-JCMC-Discussion-Catalysts-Himelboim-and-Smith.pdf

Analyzing (Social Media) Networks with NodeXL
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-CT-NodeXL-and-Social-Queries-a-social-media-network-analysis-toolkit.pdf

Whiter the experts: Social affordances and the cultivation of experts in community Q&A systems
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-Social-Computing-Whither-the-Experts.pdf

First steps to NetViz Nirvana: evaluating social network analysis with NodeXL
http://www.cs.umd.edu/~cdunne/pubs/Bonsignore09Firststepsto.pdf

Posted in All posts, Collective Action, Common Goods, Conference, Connected Action, Foundation, Measuring social media, Metrics, Network clusters and communities, NodeXL, Research, SMRF, Social Interaction, Social Media, Social Media Research Foundation, Social network, Social Network Analysis, Sociology, Talks, Visualization Tagged 2011, Analysis, Analytics, England, Manchester, Marc Smith, network, NodeXL, School, SNA, social network analysis, Training, Tutorial, UK, University, workshop

Orange in Twitter – NodeXL Social Media SNA Maps

03OctMay 7, 2015 By Marc Smith

 

Orange is a major European telecommunications provider that has been focused on what they call the “porous enterprise” – organizations in which many of the previous boundaries and barriers between businesses and customers are gone.  Social media flows in and out of companies and mixes with public collections of discussions about them. Locked down corporate laptops now are joined by employee owned mobile devices.  Consumer social media products now mingle with enterprise social media.  Corporations now engage directly with customers through public consumer social media services.

For example, Orange has several Twitter accounts.  @Orange provides general product information while @Oranger_Conseil and @OrangeHelpers provide customer support for French and English speaking customers, and @orangeapi offers technical information for developers building applications against services offered by Orange.  There are several other related accounts.

The @Orange account has 2,039 followers and is following 621.  It has 673 tweets.  The pattern of connections among these people emerges into a network that can be clustered into sub-groups:
[flickr id=”6209515710″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]

These are the connections among the Twitter users who follow or are followed by Orange when queried on September 30, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users follow one another.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6209515710/sizes/l/in/ph…

Top most between users:
@orange
@orangebusiness
@twitter
@ygourven
@pressecitron
@frenchweb
@lionelfumado
@sosh_fr
@lemondefr
@mashable

Graph Metric: Value
Graph Type: Directed
Vertices: 1587
Unique Edges: 18724
Edges With Duplicates: 26647
Total Edges: 45371
Self-Loops: 0
Connected Components: 229
Single-Vertex Connected Components: 227
Maximum Vertices in a Connected Component: 1358
Maximum Edges in a Connected Component: 45367
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.625415
Graph Density: 0.012709666
NodeXL Version: 1.0.1.179

The dense follows/follower map is a network that represents potential communication, the links indicate that there is a “follows” relationship between any two people.  The network of activated connections created when people reply or mention one another is more sparse.  For example, the @Oranger_Conseil and @OrangeHelpers accounts get mentioned by a number of other users who interact with them.  The connections among these people creates a network pattern:

[flickr id=”6208860159″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]

This is a map of the connections among the Twitter users who recently tweeted the word Orange conseil OR OrangeHelpers when queried on September 30, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

It is characterized by a hub and spoke pattern created as customers who have few connections to one another are linked to one of the hub customer service accounts for English and French speakers.

Top most between users:
@orangehelpers

@orange_conseil
@orange
@thomaslegac
@conorfromorange
@orangeripoff
@presseorange
@orangecomplaint
@lisepressac
@poupimali

Graph Metric: Value
Graph Type: Directed
Vertices: 372
Unique Edges: 474
Edges With Duplicates: 3308
Total Edges: 3782
Self-Loops: 73
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 371
Maximum Edges in a Connected Component: 3781
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 2.356011
Graph Density: 0.008361592
NodeXL Version: 1.0.1.179

Mentions of these accounts are good indications that the topic is the Orange Telecom company and not the many other Orange entities (like Orange County, the fruit, and the color, among others).  Searching for the term “Orange” or even “#Orange will likely bring back a large amount of these “name-space collisions” – overlapping uses of the term “Orange”.

[flickr id=”6210089698″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]

These are the connections among the Twitter users who recently tweeted the word #Orange when queried on October 3, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

This network of connections among the population of people who tweeted the term “#Orange has many “isolates” – users who do not follow, reply or mention any other person in the network.  These people all tweeted “#Orange” but they lack any connection to anyone else who did so.

The large cluster of connected users is a group of people discussing the Orange Telecom company, while the other clusters involve people discussing the colors of Autumn (Pumpkins!).

Top most between users:
@bluetouff
@laouffir
@fbrahimi
@presseorange
@damiendouani
@gregfromparis
@eogez
@thomaslegac
@isabellespanu
@challenges

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 2282
Edges With Duplicates: 960
Total Edges: 3242
Self-Loops: 1170
Connected Components: 584
Single-Vertex Connected Components: 515
Maximum Vertices in a Connected Component: 155
Maximum Edges in a Connected Component: 1123
Maximum Geodesic Distance (Diameter): 8
Average Geodesic Distance: 2.787863
Graph Density: 0.001827828
NodeXL Version: 1.0.1.179

[flickr id=”6209375268″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

Posted in All posts, Industry, Measuring social media, Network visualization layouts, NodeXL, Social Interaction, Social Media, Social network, Social Network Analysis, Social Roles, Social Theories and concepts, Visualization Tagged 2011, Analysis, Connected Action, Europe, Map, network, NodeXL, October, Orange, SNA, Social Media, social network analysis, Telecom, Twitter

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

Bernie Hogan’s Facebook Network Map featured in Journal of Social Structure (JOSS) (Made with NodeXL)

08JulMay 7, 2015 By Marc Smith

The Journal of Social Structure has released its First Annual JoSS Visualization Symposium results and two of the images were generated with NodeXL.  One of the two is Bernie Hogan’s radial layout applied to representing Facebook Friend networks.

http://jossviz.wordpress.com/2010/06/23/friendwheel-layout-of-a-facebook-network/

The Journal of Social Structure (JoSS) is an electronic journal of the International Network for Social Network Analysis (INSNA).  Here is Bernie’s description of the graph.

This is a “pinwheel” diagram using the author’s Facebook personal network (captured July 15, 2009).

Nodes represent the author’s friends and links represent friendships among them. The author is not shown. Each ‘wing’ radiating outwards is a partition using a greedy community detection algorithm (Wakita and Tsurumi, 2007). Wings are manually labelled. Node ordering within each wing is based on degree. Node color and size is also based on degree. Nodes position is based on a polar coordinate system: each node is on an equal angle of n/360º with a radius being a log-scaled measure of betweenness. Higher values are closer to the center indicating a sort of cross-partition ‘gravity’.

This layout has several notable features:

– The angle of each wing is proportionate to its share of the network. Thus 25 percent of nodes go from 0 to 90º.

– Partitions are distinguished by their position rather than a node’s color or shape.

– The tail indicates the periphery of each partition. A wing with many tail nodes indicates many people who are only tied to other group members.

– Edges crossing the center show between-partition connections. Since nodes are sorted by degree it is easy to see if edges originate from the most highly connected nodes or the entire partition.



Bernie’s chapter on analyzing Facebook networks with NodeXL appears in the book: Analyzing Social Media Networks with NodeXL: Insights from a connected world.

Posted in All posts, Facebook, Industry, JCMC, JoSS, Journal, Network clusters and communities, NodeXL, Oxford, Papers, Research, Social Media, Social network, Social Network Analysis, Sociology, University, Visualization Tagged 2010, Industry, JOSS, June, network, Network clusters and communities, NodeXL, Papers, Politics, SMRF, SMRFoundation, SNA, Social Media Research Foundation, social network analysis, Technology, Visualization

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

SNAP new network metrics into NodeXL – performance, speed, and scale updates and additions in NodeXL v.122

28AprMay 7, 2015 By Marc Smith

+  

The Stanford Network Analysis Platform (SNAP) (http://snap.stanford.edu) is a high performance library for calculating network metrics of potentially very large graphs. Working with SNAP author and Stanford Computer Science Professor Jure Leskovec, the NodeXL team is releasing a new update with expanded support for network metrics. With SNAP integrated into NodeXL we have improved the scale and speed performance significantly (*very* significantly!).  As of this release (v.1.0.1.122) the Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality measures are calculated using the SNAP library. In addition we have added the Page Rank metric calculated by SNAP to the list of supported network measures.  Two additional clustering algorithms automatically group nodes together into collections. With SNAP integrated into NodeXL we have added two clustering algorithms: Girvan-Newman and Clauset-Newman-Moore.

What network metrics matter most to you?

Posted in All posts, Network metrics and measures, NodeXL, Social Network Analysis Tagged 2010, measures, Metrics, NodeXL, Release, SMRF, SMRFoundation, SNA, Social Media Research Foundation, social network analysis, update 2 Comments

30 April 2010, Friday – Workshop: Social Media Network Analysis: Next Practices in Social Network Analysis, Tools and Media

26AprMay 7, 2015 By Marc Smith

the future of networks

I will present a workshop on social media network analysis at the next Network Singularity event at Fort Mason on the Bay in San Francisco on April 30, 2010.

future of networksfuture of networks
Bay Area and Silicon Valley Network: Special Action/Research Event

Social Media Network Analysis: Next Practices in Social Network Analysis, Tools and Media

Friday 30 April 2010
8:00am -5:00pm
http://www.regonline.com/BAN10

The Future of Networks.Workshop Abstract
Why do some social media and online groups succeed when others fail? How do different collections of online media and populations of authors and users differ from one another? How do patterns of contribution vary? How do these differences illustrate the roles people play within their communities?

Patterns of contribution and connection determines social media success. Visualizing these network patterns aids implementation, adoption, security and effectiveness of social media. A range of Internet social media including discussion groups, Twitter, enterprise social media, communities-of-practice, blogs and email are presented, analyzed and visualized. Network patterns are explored to illustrate the scope of variation among social media repositories and between types of contributors.

Maps and patterns of interaction deliver a far more comprehensive view of social media. These views generate actionable findings to improve social media effectiveness, syndication and collaborative outcomes. Network analysis and intelligence can guide community cultivation, coordination and development tasks. New network capabilities provision features that improve search, ranking and consumption of user generated content.

A freely available, open source tool will be demonstrated to perform basic social media network analysis for contemporary social media that should be relevant to business models and organizations of all types. Delegates will be equipped to use social network techniques to improve overall effectiveness and impact of social media, communities-of-practice and social software applications. Participants may expect to achieve immediate improvements in key business activities such as marketing, sales, engineering, support, service, innovation and overall organizational effectiveness.

Directions

Posted in All posts, Connected Action, Measuring social media, NodeXL, Social Media, Social Network Analysis, Social Roles, Sociology, Visualization Tagged 2010, April, California, Connected Action, Internet, Marc Smith, network, Network Visualization, NodeXL, San Francisco, SNA, Social Media, social network analysis, Visualization, workshop

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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

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