Hello North Bay Area network analysis fans!
Join the NodeXL users meetup at the Napa Embassy Suites hotel (1075 California Boulevard, Napa, California, 94559, USA) on the 27th of February at 6pm.
Meet other network, social network, and social media network researchers and practitioners.
Hear about the latest and upcoming updates in the free and open NodeXL application.
Connect to the NodeXL network! Please RSVP using the form below:
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Connected Action enables cloud storage of your NodeXL social media network datasets.
The Connected Action NodeXL Graph Server Importer allows you to import your Twitter or Facebook data into NodeXL from the cloud based Connected Action NodeXL Graph Database.
Contact Connected Action for pricing and availability.
The Connected Action NodeXL Graph Server Database enables NodeXL users to store their social media data from Twitter and Facebook in a personal Cloud Storage locker.
Specify the search terms and queries that matter to you with your Connected Action account representative and the Connected Action NodeXL Graph Database will collect and store your social media data for you every day.
Subscribers to the Connected Action NodeXL Graph Database can then use NodeXL to import long periods of their collected social media network data in a short period of time!
The importer can be added to any recent copy of NodeXL.
Just download and unzip the add-in and copy it to the folder you have selected to hold 3rd party importers for NodeXL .
You can select the folder to use for 3rd party importers via the NodeXL menu located at:
NodeXL>Data>Import>Import Options:
This folder can be located anywhere in your file system.
When you restart NodeXL, your NodeXL>Data>Import Menu may look like this:
When you select the option “From Connected Action NodeXL Graph Server…” you will get a dialog that looks like:
Enter your account credentials and then enter the queries you have created with your Connected Action account representative.
NodeXL will then import a social media network data set that can be automatically analyzed and visualized.
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/
Fourth International AAAI Conference on Weblogs and Social Media
(ICWSM-10)
May 23-26, 2010
George Washington University, Washington, DC
Sponsored by the Association for the Advancement of Artificial Intelligence
The ICWSM 2010 conference starts Sunday. This is a very high quality conference on the study of social media. My colleague, Professor Derek Hansen, and I will lead a tutorial on using NodeXL to analyze social media networks.
Social networks are the defining data structure of social media, created as people reply, link, click, favorite, friend, re-tweet, co-edit, mention, or tag one another. In this tutorial, we review the core concepts and methods of social network analysis and apply it to the collection, analysis, and visualization of social media networks. Using the free and open NodeXL application, learn how to extract a social media network and generate metrics and visualizations that highlight key people and positions within streams of tweets, videos, photos, or emails.
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.
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
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.
Video is now available from a panel hosted by the Women’s Affinity Group of O’Melveny & Myers’ Silicon Valley Office in Menlo Park on November 19th. Along with Karla Spormann, President and CEO Tendo Communications, Martin Eberhard, Co-founder and former CEO Tesla Motors, Patrick Ewers, Founder, Mindmavin LLC. We spoke about “Using Social Media to Grow and Market Your Business”.
We discussed ways to leverage social networks networks beyond personal connections – to provide business value. We talked about ways to efficiently and effectively use social media to market and grow your business.
I spoke about tools, like NodeXL, that we have been building that create maps of the relationships among a population of people gathered by some shared attribute, like mentioning a keyword or hashtag.
Here are a few excerpts from the event created by Tendo Communications: