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.
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.
SF Online Community MeetUp is the free monthly gathering of online community managers, enthusiasts, and innovators to meet and discuss tools and strategies for building and managing effective communities.
During our March 26 Meetup we’re happy to welcome Marc A. Smith, Chief Social Scientist at Connected Action Consulting Group for his talk, “Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL.” The talk will explore how the Social Media Research Foundation, an organization formed to develop open tools and data sets and to foster scholarship related to social media, is using NodeXL to create social network maps. Learn how you can use this free and open tool to map public social media conversations happening among your online community across social networks. Find out how NodeXL can augment your existing community management practices to identify key influencers in your community, discover relationships and strategic hashtags, and more.
This is a sample NodeXL graph that represents a network of 106 Twitter users whose recent tweets contained “passbac”. The network was obtained on Wednesday, 30 January 2013 at 01:09 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 tweets were made over the 7-day, 5-hour, 32-minute period from Tuesday, 22 January 2013 at 17:40 UTC to Tuesday, 29 January 2013 at 23:12 UTC.
Learn to make your own network maps of social media at PASSBAC 2013!
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.
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.
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.
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:
Unique Edges: 172
Edges With Duplicates: 123
Total Edges: 295
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
Top 10 Vertices, Ranked by Betweenness Centrality:
The graph's vertices were grouped by cluster using the
Clauset-Newman-Moore cluster algorithm.
More NodeXL network visualizations are here:
A gallery of NodeXL network data sets is available here:
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:
The book, Analyzing social media networks with NodeXL:
Insights from a connected world, is available from Morgan Kaufmann and from Amazon.