NodeXL users in the Washington DC area are welcome to join a free users group meetup on October 2, 2013 at 2pm.
There will be a morning session at the University of Maryland (9-11) and an afternoon session downtown.
If you’d like to attend, please complete this form.
I hope to see you there!
This October 18, 2013 an NSF funded workshop called Kredible.Net to be held at Stanford University will bring together researchers studying reputation and social roles in social media.
The workshop will help researchers investigate how social media, especially Wikipedia articles and editors, shape public knowledge. The project aims to build a research community and to propose a research agenda for the study of reputation and authority in informal knowledge markets, such as Wikipedia.
The team is lead by Sorin Adam Matei (PI) of Brian Lamb School of Communication, Elisa Bertino (Cyber Center), Michael Zhu and Chuanhai Liu (Department of Statistics) , and Luo Si (Department of Computer Science).
The KredibleNet project is supported by an international team of researchers including Marc Smith, director of the Social Media Research Foundation and the NodeXL project and by Dr. Luca De Alfaro, from University of California, Santa Cruz, the head of the WikiTrust project. The team also includes researchers Jure Leskovec (Stanford), Bernie Hogan (Oxford Internet Institute), and Ben Shneiderman (University of Maryland).
The KredibleNet grant will fund research and workshops that will reveal how social media leaders gain authority to shape public knowledge.
 |
I spoke at the 13th Annual World Knowledge Forum in Seoul, Korea on October 9th, 2012.
Here are photos from the trip which also included talks at Sogang University and the Samsung Economic Research Institute and Hallym University and a visit with the social media analysis company Treum.
[flickrset id=”72157631755708276″ thumbnail=”thumbnail” photos=”” overlay=”false” size=”small”]
I will keynote at the Predictive Analytics World event in Boston which will be held September 30-October 4th, 2012.
The event has grown along with the importance of big data, analytics, BI, and data visualization.
I will speak about the ways social media networks can be collected and analyzer to reveal the key people, groups and topics relevant to a topical population.
Title: Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
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.
We now live in a sea of tweets, posts, blogs, and updates coming from a significant fraction of the people in the connected world. Our personal and professional relationships are now made up as much of texts, emails, phone calls, photos, videos, documents, slides, and game play as by face-to-face interactions. Social media can be a bewildering stream of comments, a daunting fire hose of content. With better tools and a few key concepts from the social sciences, the social media swarm of favorites, comments, tags, likes, ratings, and links can be brought into clearer focus to reveal key people, topics and sub-communities. As more social interactions move through machine-readable data sets new insights and illustrations of human relationships and organizations become possible. But new forms of data require new tools to collect, analyze, and communicate insights.
I presented on social media network analysis on October 20, 2011 in New York City at Predictive Analytics World.
A map of the connections among the people tweeting about the #Pawcon hashtag is below.
Abstract: Networks are everywhere except the end user desktop. NodeXL, the free and open network overview, discovery and exploration add-in for the popular and familiar Excel (2007/2010) spreadsheet allows users who are comfortable making pie charts to now make useful network visualizations. Developed and released by the Social Media Research Foundation, NodeXL uses Excel as a framework, providing a GUI network browser (a “web browser”?) that novices can use quickly and experts can use to generate sophisticated results. Data importers provide access to a range of social media network data sources like Twitter, flickr, YouTube, Facebook, email, the WWW, and more through standard file formats (CSV, GraphML, Matrix). Simple to use tools can automatically analyze, visualize and highlight insights in complex network graphs. Using NodeXL, researchers have been collecting a wide range of network data sets from various social media services. These images reveal a range of common social formations in social media and point to people who occupy strategic locations in these graphs.
This is a map of the connections among the people who tweeted the term “PAWCON” on the first day of the event:
[flickr id=”6261006732″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]
These are the connections among the Twitter users who recently tweeted the word #pawcon when queried on October 19, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
A larger version of the image is here: www.flickr.com/photos/marc_smith/6261006732/sizes/l/in/ph…
Top most between users:
@tapan_patel
@pawcon
@sasanalytics
@deloitteba
@kristinevick
@jamet123
@zementis
@kdnuggets
@tibcospotfire
@saspublishing
Graph Metric: Value
Graph Type: Directed
Vertices: 41
Unique Edges: 233
Edges With Duplicates: 120
Total Edges: 353
Self-Loops: 44
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 40
Maximum Edges in a Connected Component: 352
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 1.87133
Graph Density: 0.15304878
NodeXL Version: 1.0.1.179
Here is an example map of the connections among the people who tweeted the term “pawcon” in Twitter on September 14th, a week prior to the event.
[flickr id=”6274836259″ thumbnail=”small” overlay=”true” size=”large” group=”” align=”none”] [flickr id=”6274836151″ thumbnail=”small” overlay=”true” size=”large” group=”” align=”none”]
Manu Sharma, Principle Research Scientist at LinkedIn gave a great presentation on the patterns found in their data. Big data, for example, showed that most of the people who previously worked at recently failed banks and financial institutions have updated their profiles to show that they mostly have new jobs at some of the remaining companies in the industry.
The event was held at the New York Hilton: Maps & Directions
 |
 The Social Media Research Foundation is pleased to announce the immediate availability of ThreadMill 0.1. ThreadMill is a free and open application that consumes message thread data and produces reports about each author, thread, forum, and board along with visualizations of the patterns of connection and activity. ThreadMill is written in Ruby, and depends on MongoDB, SinatraRB, HAML, and Flash to collect, analyze, and report data about collections of conversation threads. |
Threaded conversations are a major form of social media. Message boards, email and email lists, twitter, blog comments, text messages, and discussion forums are all social media systems built around the message thread data structure. As messages are exchanged through these systems, some messages are sent as a reply to a particular previous message. As messages are sent in reply to prior messages, chains of messages form. Message chains come in two major forms: branching and non-branching. Branching threads are those that allow more than one message to reply to a prior message. Non-branching threads are single chains, like a string of pearls, that allow only one message to reply to a prior message. Many web based message boards are non-branching. Many email systems and discussion forums are branching.
ThreadMill requires a minimal set of data elements to generate its reports. A data table must minimally have a column of information for each message that includes the name of the message board, the forum, the thread, and the author, along with a unique identifier for each message and the date and time it was posted. Optional data elements include the unique identifier of the message being replied to, the URL of the message, and the URL for a profile photo.
All forms of threaded message exchange can be measured. Simple measures like the count of the number of messages or the number of authors are obvious and useful. Other measures can be created from more sophisticated analysis. For example, the network of connections that forms as different authors reply to one another can be extracted and analyzed using network analysis methods. It is possible to calculate metrics from these networks of reply that describe the location of each person in the graph.
ThreadMill generates several data sets that can be used to create visualizations of the activity and structure of a message board collection.
A Treemap data set can illustrate the hierarchy of encapsulated authors within threads, threads within fora, fora within boards, and boards within collections. Treemap visualizations of collections of threaded conversations can quickly highlight the most active or populous discussions.
An AuthorLine visualization takes the form of a double histogram, with bubbles representing each thread active in each time period sized by the volume of messages the author contributed, sorted by size. Threads that have been initiated by the author are represented as bubbles above the center line. Messages that the author contributes to threads started by other authors are represented as bubbles stacked below the center line. AuthorLines quickly reveal the pattern of activity an author displays and identifies which of several types of contributors the author is.
A scatter plot visualization represents each author as a bubble in an X-Y space defined by the number of different days the author was active against the average number of messages the author contributes to the threads in which they participate.
A time series line chart reveals the days of maximum and minimum activity along with trends.
A network diagram reveals the overall structure of the discussion space and the people who occupy strategic locations within the network graph.
ThreadMill has received generous assistance from Morningside Analytics. Bruce Woodson implemented ThreadMill.
The Jive World 11 conference took place October 4-6, 2011.
[flickr id=”6234653454″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]
These are the connections among the Twitter users who recently tweeted the word #JW11 when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
See: www.jivesoftware.com/jiveworld
A larger version of the image is here: www.flickr.com/photos/marc_smith/6234653454/sizes/l/in/ph…
Top most between users:
@jivesoftware
@gialyons
@mikefraietta
@cflanagan
@cosmopolitan_lv
@ginorossi
@kristinhersant
@thebrandbuilder
@alanlepo
@mor_trisha
Graph Metric: Value
Graph Type: Directed
Vertices: 345
Unique Edges: 3606
Edges With Duplicates: 2072
Total Edges: 5678
Self-Loops: 632
Connected Components: 11
Single-Vertex Connected Components: 9
Maximum Vertices in a Connected Component: 334
Maximum Edges in a Connected Component: 5659
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.353001
Graph Density: 0.034184361
NodeXL Version: 1.0.1.179
More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
#occupywallstreet continues to be an active topic of social media traffic.
Here is the updated map of connections among people who recently tweeted #occupywallstreet:
[flickr id=”6232030871″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]
[flickr id=”6232550608″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]
These are the connections among the Twitter users who recently tweeted the word OccupyWallStreet when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
See: occupywallst.org/
A larger version of the image is here: www.flickr.com/photos/marc_smith/6232030871/sizes/l/in/ph…
Top most between users:
@ajenglish
@anonymousirc
@cnbc
@rawstory
@ryannewyork
@mediabistro
@blogdiva
@brentnhunter
@krenner2
@rebuilddream
Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 3014
Edges With Duplicates: 720
Total Edges: 3734
Self-Loops: 994
Connected Components: 318
Single-Vertex Connected Components: 300
Maximum Vertices in a Connected Component: 664
Maximum Edges in a Connected Component: 3326
Maximum Geodesic Distance (Diameter): 9
Average Geodesic Distance: 3.508133
Graph Density: 0.002524525
NodeXL Version1.0.1.179
More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
The #Occupywallstreet movement is growing and lots of activity is taking place in social media. Here is a map of the connections among the people who recently tweeted the term “#occupywallstreet” on 8 October 2011.
[flickr id=”6225446144″ thumbnail=”medium” overlay=”true” size=”large” group=”” align=”none”]
Link: www.flickr.com/photos/marc_smith/6225446144/
Each user is scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
See: occupywallst.org/
A larger version of the image is here: www.flickr.com/photos/marc_smith/6225446144/sizes/l/in/ph…
Top most between users:
@maddow
@motherjones
@aclu
@zerohedge
@mmfa
@spread4freedom
@diggrbiii
@angelsavant
@marychastain
@katiepavlich
Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 3835
Edges With Duplicates: 916
Total Edges: 4751
Self-Loops: 1109
Connected Components: 311
Single-Vertex Connected Components: 301
Maximum Vertices in a Connected Component: 678
Maximum Edges in a Connected Component: 4327
Maximum Geodesic Distance (Diameter): 9
Average Geodesic Distance: 3.283708
Graph Density: 0.003408408
NodeXL Version: 1.0.1.179
More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
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/
