Connected Action enables cloud storage of your social media networks with the NodeXL Graph Server Database

10FebMay 7, 2015 By Marc Smith

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.

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

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.

SLIDES: Boston DataSwap 2013 – Network Visualization in NodeXL by @codydunne

25OctMay 7, 2015 By Marc Smith

NodeXL team member Dr. Cody Dunne recenjtly presented these slides at the 2013 Boston DataSwap on Network Visualization in NodeXL
http://slidesha.re/1a5tbCY

Boston DataSwap 2013 — Network Visualization in NodeXL from codydunne

Journal Paper: JCMC – Birds of a feather tweet together: Integrating Network and Content Analyses to Examine Cross-Ideology Exposure on Twitter

03MarMay 7, 2015 By Marc Smith

Itai Himelboim, Stephen McCreery and I have recently published a paper in the Journal of Computer Mediated Communication.

“Birds of a feather tweet together: Integrating Network and Content Analyses to Examine Cross-Ideology Exposure on Twitter“

Abstract

This study integrates network and content analyses to examine exposure to cross-ideological political views on Twitter. We mapped the Twitter networks of 10 controversial political topics, discovered clusters – subgroups of highly self-connected users – and coded messages and links in them for political orientation. We found that Twitter users are unlikely to be exposed to cross-ideological content from the clusters of users they followed, as these were usually politically homogeneous. Links pointed at grassroots web pages (e.g.: blogs) more frequently than traditional media websites. Liberal messages, however, were more likely to link to traditional media. Last, we found that more specific topics of controversy had both conservative and liberal clusters, while in broader topics, dominant clusters reflected conservative sentiment.

View Full Article (HTML) Get PDF (1077K)

2012 Summer Social Webshop at University of Maryland

23AugMay 7, 2015 By Marc Smith

[flickr id=”7831570160″ thumbnail=”medium” overlay=”true” size=”medium” group=”” align=”none”]

During August 21-24, 2012 Summer Social Webshop gathered 55 students and 20 speakers for a week of presentations, discussions, and collaboration around the study and application of social media to social good. Sponsored by the U.S. National Science Foundation, the Social Media Research Foundation, and Grand, the Webshop brings doctoral students in computer science, iSchool, sociology, communications, political science, anthropology, psychology, journalism, and related disciplines together for 4-days of intensive workshop on Technology-Mediated Social Participation (TMSP).

Technology-Mediated Social Participation includes social networking tools, blogs and microblogs, user-generated content sites, discussion groups, problem reporting, recommendation systems, and other social media applied to national priorities such as health, energy, education, disaster response, political participation, environmental protection, business innovation, or community safety.

During the 4-day workshop, students attended presentations from an interdisciplinary group of leaders in the field and engage in other research and community-building activities like working on short-term projects, sharing research plans, developing new research collaborations, learning relevant software, analysis methods and data collection tools, and meeting Federal policy makers.

Videos and slides from talks:

Ben	Shniederman	Introduction to TMSP
Elizabeth	Churchill	Data by Design
Bernie	Hogan	A survey of Facebook as a research site
Noshir	Contractor	Organizing in the 21st century
David	MacDonald	Social Translucence in Wikipedia
Marc	Smith	Mapping social media spaces
Alan	Neustadtl	Realizing the potential of data
Eszter	Hargittai	Digital Inequality for Internet Research
Ines	Mergel	Social media adoption in the public sector
Libby	Hemphill	Elected Officials and Social Media
Nancy	Baym	Musicians and Social Media
Lise	Getoor	Link Mining
David	Huffaker	Applying Social Research at Google
Cliff	Lampe	Understanding Large-scale Interaction
Gerhard	Fischer	Cultures of Participation
Lee	Rainie	Networked
Zeynep	Tufekci	Why <<More>> is Different
Zeynep	Tufekci	Converstation at Brookings Institution
Jessica	Vitak	Tech & Relationships: It’s Complicated
Katie	Shilton	Participatory Personal Data
Jennifer	Golbeck	Politics on Twitter
Kevin	Crowston	Socio-Computational Research
Jana	Diesner	Words and Networks
Paul	Resnick	Social Approaches to Health and Wellness
Zeynep	Tufekci	Why <<More>> is Different
Itai	Himelboim	Information Sources on Twitter

Photos from the event:

[flickrset id=”72157631177125858″ thumbnail=”thumbnail” photos=”” overlay=”true” size=”small”]

How to summarize the URLs, Hashtags and @Users mentioned in clusters of users discussing a Twitter Topic with NodeXL

19MayMay 7, 2015 By Marc Smith

Social media networks tend to be “clumpy”. Here is the map of connections among people who tweeted the term “global warming”:

NodeXL v.210 and newer now supports text analysis of content collected from social media data sources. NodeXL applies social network clustering and then analyzes text that is grouped by social clusters.

Connections among people who tweet about a topic, keyword or hashtag form patterns that can lead to the formation of sub-groups and clusters. Multiple clusters are formed within a network when a sub-population of people link to one another far more than to people in other groups. These regions of dense connections define the boundaries between sub-populations. Clusters often reflect the variation in interest in certain people and topics in the population. Some people and topics are more interesting to one group than others. Within these groups certain people and words get repeated more often than others.

Networks can be partitioned by many methods. NodeXL implements several. A collection of vertices can be grouped by the user by applying labels to the vertex worksheet (“Group by vertex attribute”). Or a group of vertices can be determined by an algorithm that looks for differences in the density of connections and divides by the points of least association (“Group by cluster algorithm”). Networks can also be grouped into separate isolated collections of nodes, called “connected components”.

In NodeXL groups can be visualized in multiple ways. Groups can be collapsed into meta-vertices that stand-in for the members of that group (right-click the graph pane and select “Groups>Collapse all groups”). Group members can also be displayed within a “box” with the “group-in-a-box” feature (found in the layout selection menu in the Graph Pane – select “Layout Options”).

Within each group is a population of people along with the tweets they authored in the time period captured by the data set. Each group has a collection of tweets that can be analyzed. The contents of all the tweets in a network can be scanned and certain types of strings can be counted to measure its frequency of mention. These counts can be repeated for each group, allowing groups to be contrasted based on the relative rates strings like URLs, hashtags, and @usernames. Here is a sample of the worksheet NodeXL creates to display all the data about people, URLs, and hashtags frequently mentioned in each group:

The worksheets offers top URLs, hashtags, and users across the entire network, and within each sub-group. The details offer insights into the people and topics of greatest interest.

Top Hashtags in Tweets in G7	G7 Count
globalwarming	24
climate	14
climatechange	10
environment	9
agw	6
books	6
glennbeck	6
rushlimbaugh	6
wildlife	5
science	5

Top Hashtags in Tweets in G5	G5 Count
tcot	13
teaparty	4
oil	4
globalwarming	4
p2	2
wrp	2
yyc	2
blameman	1
libtards	1
climatechange	1

Top Hashtags in Tweets in G4	G4 Count
ff	2
globalwarming	2
jokeswritethemselves	1
silverlining	1
ulooklikechazbonoonroids	1
jclogic	1
climatechange	1

Top URLs in Tweet, in Entire Graph	Entire Graph Count
http://LiveScience.com	16
http://bit.ly/IdTUlC	14
http://ow.ly/apxEv	10
http://is.gd/ZSXuVT	10
http://stevengoddard.wordpress.com/2012/04/21/arctic-ice-area-approaching-abnormally-high-range/	9
http://bit.ly/IbMs8o	9
http://www.financialpost.com/m/wp/fp-comment/blog.html?b=opinion.financialpost.com/2012/04/20/aristotles-climate	8
http://bit.ly/JwlWYw	8
http://yhoo.it/JdLq0Q	7
http://usat.ly/JdNKFh	7

This feature allows the content in sub-groups to be contrasted, thus answering the question: how is this sub-group the same or different from another sub-group?

NodeXL describes the networks you create: Graph Summary in v.203

09MarMay 7, 2015 By Marc Smith

Here is a map of connections among people who recently tweeted the term “peoplebrowsr”.

“But what does that picture mean?”

I hear this reaction frequently when I show people maps I have made of social media connections.

I often point out that the map and the data can reveal people who occupy important locations in the network as well as emergent clusters and groups.

“So why didn’t you just say so?”

I hear this reaction frequently when I explain what is important about a network.

In NodeXL version 203 we have released a new feature called Graph Summary. Our goal is to “just say so”.

In this version we introduce the basics of automatic captioning. In the NodeXL>Graph menu we now have a “Summary” button:

NodeXL will collect information about the creation and configuration of the network. The dialog box looks like this:

Note that NodeXL>Data>Save Import Details in Graph Summary must be selected in the Import menu for the “Data Import” field to be populated.

Selecting “Copy to Clipboard” will load a copy of these text fields into the buffer. An example of that caption is here:

The graph represents a network of up to 1000 Twitter
users whose recent tweets contained "peoplebrowsr". 

The network was obtained on
Friday, 09 March 2012 at 01:21 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 earliest tweet in the network was tweeted on
Friday, 02 March 2012 at 02:39 UTC. 

The latest tweet in the network was tweeted on
Friday, 09 March 2012 at 00:47 UTC.

The graph is directed.

The graph was laid out using the
Harel-Koren Fast Multiscale layout algorithm.

The edge colors are based on relationship values. 
The vertex sizes are based on followers values.

Overall Graph Metrics:
Vertices: 74
Unique Edges: 172
Edges With Duplicates: 123
Total Edges: 295
Self-Loops: 42
Connected Components: 15
Single-Vertex Connected Components: 13
Maximum Vertices in a Connected Component: 58
Maximum Edges in a Connected Component: 276
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 2.014176
Graph Density: 0.036653091447612
Modularity: 0.288302

Top 10 Vertices, Ranked by Betweenness Centrality:
@peoplebrowsr
@andrewgrill
@traviswallis
@thenickfrost
@jas
@alexbudge
@getmingly
@milener
@jeffreyhayzlett
@johnnosta

The graph's vertices were grouped by cluster using the
Clauset-Newman-Moore cluster algorithm.

More NodeXL network visualizations are here:
www.flickr.com/photos/marc_smith/sets/72157622437066929/
and here:
www.nodexlgraphgallery.org/Pages/Default.aspx

A gallery of NodeXL network data sets is available here:
nodexlgraphgallery.org/Pages/Default.aspx?search=twitter

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation
(www.smrfoundation.org) - which is dedicated to
open tools, open data, and open scholarship.

Donations to support NodeXL are welcome through PayPal:
https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=J5AERGAAN552S

The book, Analyzing social media networks with NodeXL:
Insights from a connected world, is available from Morgan Kaufmann and from Amazon.
http://www.amazon.com/gp/product/0123822297?ie=utf8&tag=conneactio-20&linkcode=as2&camp=1789&creative=390957&creativeasin=0123822297

This caption will expand in our next several releases to include information about the top URLs, hashtags, and @usernames in text fields associated with nodes and edges. Following that we will release a series of features to allow for the extraction of keyword pairs in those text fields (our current version of this feature is described here: Keyword Networks: create word association networks from text with NodeXL (with a macro)).

March 5th Talk at Predictive Analytics World 2012 in San Francisco: Crowd Photography for Social Media

04JanMay 7, 2015 By Marc Smith

I will speak this March 4th at the 2012 Predictive Analytics World in San Francisco about ” Crowd Photography for Social Media“.

http://www.predictiveanalyticsworld.com/sanfrancisco/2012/speakers.php
http://www.predictiveanalyticsworld.com/sanfrancisco/2012/agenda.php#

Monday @ 5:25-5:45pm

Track 1:
Social Data Case Study:
Social Media Research Foundation
Crowd Photography for Social Media

Crowds of people gather in social media around many products, services, businesses, and events but they can be difficult to see and understand. With new free and open tools, it is now possible to map and measure social media spaces, capturing the sub-groups and key people within and between them. Learn how to capture social media data and quickly generate a visual map of the crowd. With maps in hand, we will discuss ways they guide a journey to the key influencers and concepts in the crowd.

Speaker: Marc Smith, Director, Social Media Research Foundation

Contrasting teaparty and occupywallstreet twitter networks

16NovMay 7, 2015 By Marc Smith

Both teaparty and occupywallstreet are actively discussed in twitter.

This map of connections among people who tweeted Teaparty starts on 11/15/2011 14:22 UTC and ends on 11/15/2011 17:23, a total of 3 hours and 1 minute of traffic.

The Teaparty data set contained 1,533 tweets, replies and mentions.
Blue edges are connections created by replies and mentions. Grey lines are follows relationships.

Top most between users:
@ronpaul
@michellemalkin
@christopherhull
@theteaparty_net
@capaction
@thedailyedge
@bill1phd
@dbargen
@gulagbound
@rightcandidates

Graph Metric: Value
Graph Type: Directed
Vertices: 659
Unique Edges: 8808
Edges With Duplicates: 1423
Total Edges: 10231
Self-Loops: 1084
Connected Components: 49
Single-Vertex Connected Components: 44
Maximum Vertices in a Connected Component: 606
Maximum Edges in a Connected Component: 10148
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance:2.693965
Graph Density: 0.02036797
NodeXL Version: 1.0.1.193

The major clusters are composed of teaparty supporters. The center bottom cluster are teaparty critics.

This map of the connections among people who tweeted Occupywallstreet starts on 11/15/2011 23:08 and ends on 11/15/2011 23:34 UTC, a total of 26 minutes of traffic.

Occupywallstreet 1,370 tweets, replies and mentions
Blue edges are connections created by replies and mentions. Grey lines are follows relationships.

Top most between users:
@occupywallst
@mmflint
@nyclu
@allisonkilkenny
@andrewbreitbart
@operationleaks
@occupydenver
@theatlantic
@usgeneralstrike
@rt_com

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 3546
Edges With Duplicates: 826
Total Edges: 4372
Self-Loops: 794
Connected Components: 241
Single-Vertex Connected Components: 230
Maximum Vertices in a Connected Component: 747
Maximum Edges in a Connected Component: 3998
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.65438
Graph Density: 0.003246246
NodeXL Version: 1.0.1.194

Some notable contrasts:
Teaparty Graph Density: 0.002652645
Occupywallstreet Graph Density: 0.02036797 – significantly lower levels of interconnection
Teaparty: Single-Vertex Connected Components 44 of 1000
Occupywallstreet: Single-Vertex Connected Components 283 of 1000

Many more “isolates” (Single-Vertex Connected Components) in Occupywallstreet.
Many more hubs, and more retweeting activity in Occupywallstreet.

The difference in duration of these data sets illustrates the relative speed of content creation in the topics. The data sets are commensurable in that they are both the result of a single query against the Twitter search API. So both maps are the results of charting connections among the authors of the last 1500 tweets, how ever long that takes to create.

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.

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

Network clusters and communities

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