This is a map of the network of 2,785 Twitter users whose recent tweets contained ““kansas state” OR KState” over the 1-day, 23-hour, 14-minute period from Monday, 13 January 2014 at 17:06 UTC to Wednesday, 15 January 2014 at 16:20 UTC.
Graduate students in Computer Science at the University of Maryland in a class on information visualization produced a striking variety of NodeXL network analysis visualizations for their recent homework projects. The class, taught by Prof. Ben Shneiderman (www.cs.umd.edu/~ben), covers commercial tools, such as Spotfire and Tableau, and research software, giving students a chance to learn a range of existing visualization techniques and tools. The NodeXL homework project is done by individual students, midway in the semester, while 5-person student teams are also busy working on their major term projects, which create novel visualization tools for specialized applications. To see all the projects, click:
(Don’t be deterred by security warnings, the class wiki is open for all to read, but only students can edit)!
Several of the 30 projects deal with Facebook, Twitter, email, Wikipedia, and YouTube social networks, with academic citation patterns and sports networks adding variety. Entertainment, finance and medical analyses round out the collection, showing the huge range of potential NodeXL applications. Students had only two weeks to find data, import it, clean it, and then create meaningful visualizations that enabled them to find interesting insights into connected structures.
Gregory Kramida’s analysis of stock symbol co-occurrences in financial articles
Gregory Kramida analyzed the connections among company names in the business press. See:
The project shows the strong linkages between technology companies and consumer services, finance and public utilities. The data set of more than 50,000 financial articles had more than 400,000 co-occurrences of stock ticker symbols. He used the NodeXL grouping feature to organize the stocks into groups by industry and then showed results using the Group-in-a-Box layout feature. This network is limited to companies that were mentioned together at least 50 times.
Ruofei Du’s analysis of co-authorship patterns
Ruofei Du probed the relationship among authors in 1033 scientific papers from the 1988 to 2013 User Interface Software & Technology (UIST) conference. See:
The co-author collaborations followed commonly seen patterns of professors and their students, but the relationships between academia and industry showed novel patterns. After grouping authors by their organizations, it is apparent that Microsoft is well-represented at this conference through numerous collaborations with universities.
Joshua Brule’s analysis of actor co-performance connections from the television series Firefly
Joshua Brule created an intriguing story of television and film actors and actresses that emerges from analysis of ten actors from the cancelled television series Firefly. See:
The actors had few collaborations before appearing on the program, but many afterwards. The carefully constructed bipartite network shows how ten actors collaborated in 38 films, television shows, or videogames.
I will speak about social media networks on October 24th, 2013 at the department of Computer Science at the Arizona State University.
The graph represents a network of 712 Twitter users whose recent tweets contained “@ASU”, taken from a data set limited to a maximum of 10,000 tweets. The network was obtained from Twitter on Sunday, 13 October 2013 at 19:56 UTC.
The tweets in the network were tweeted over the 4-day, 21-hour, 47-minute period from Tuesday, 08 October 2013 at 21:48 UTC to Sunday, 13 October 2013 at 19:35 UTC.
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 graph is directed.
The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values
I am delighted to be attending and presenting a workshop on social media network analysis at the University of Kentucky’sLINKS 2013 program on June 7th, 2013.
This week long program has for many years provided intensive training in network methods, research, and tools.
I am excited to attend some of the program and meet researchers and students working on networks of all sorts. I will do a short hands-on talk about NodeXL and a longer day devoted to the broader ways networks are useful for the study of social media.
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.
I gave a talk that describes ways of analyzing the social and semantic networks found in social media at a workshop at the Web Science conference. The event is a great collection of people interested in the exploration of complex systems and internet applications, particularly social applications. It describes itself as ” inherently interdisciplinary, integrating computer and information sciences, communication, linguistics, sociology, psychology, economics, law, political science, and other disciplines.”
I will speak about the results of collecting, analyzing and visualizing the collections of connections that form in political discussions in social media.
For example, this is a map of the connections among the people who recently tweeted about Scott Walker.
The graph represents a network of up to 1000 Twitter users whose recent tweets contained “scott AND walker”. The network was obtained on Friday, 13 April 2012 at 07:40 UTC. 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 Thursday, 12 April 2012 at 03:32 UTC. The latest tweet in the network was tweeted on Friday, 13 April 2012 at 04:12 UTC. Continue reading →
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.
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.
Summer Social Webshop
on
Technology-Mediated Social Participation University of Maryland, College Park August 23-26, 2011
Eventful. The 2011 Webshop at the University of Maryland was certainly that with both an earthquake and a hurricane to mark the start and end of the event. We really moved heaven and earth at this workshop.
In 4 days, 20 talks, 45 students, an earthquake, a hurricane and many new connections – the Webshop touched on a set of related concepts, methods, and findings about ways to use communication and computation technology to help groups, neighborhoods, cities, states, and nations work collectively towards common goals.
Several years ago a program at the University of Maryland called “Webshop” (Web Workshop) was organized by Professor John Robinson and held for three consecutive Summers. I visited and spoke at two of these events and know many people who attended or spoke at one or more and remember the event enthusiastically. The students who attended include some of the now leading researchers in the field of social science studies of the internet. There is an impressive alumni list.
The last Webshop was held in 2003 and many years and significant changes have occurred in the time since. Twitter, Facebook, StreetView, iPad,FourSquare, Android, Kinect, EC2, Mechanical Turk, Arduino, were all new or non-existent when the first Webshops were run. Today we have more reason than ever to focus on the details and patterns of computer-mediated human association. Ever more people channel more of their communications with others through more digital media, often of the social kind. A new data resource for the social sciences is growing in scale and promise: from billions of events it is possible to start to build a picture of an aggregate whole, and to start to grasp the terrain and landscape of social media.
After many years of inactivity, the Summer Social Webshop (@Webshop2011) happened again! With the generous support of the National Science Foundation and additional assistance from Google Research, on August 23-26, 2011 at the University of Maryland, College Park, a group of students heard and engaged with more than two dozen leading researchers exploring digital social landscapes from a variety of perspectives. Organized by a collaboration between the University of Maryland’s Human Computer Interaction Laboratory (HCIL), the College of Information Studies, the Sociology and Computer Science Department, and the Social Media Research Foundation, the event gathered students from a wide range of disciplines to get a concentrated dose of advanced efforts to gather data from social media and people’s understanding and practices around digital technologies. Doctoral students in computer science, iSchools, sociology, communications, political science, anthropology, psychology, journalism, and related disciplines applied to attend the 4-day intensive workshop on Technology-Mediated Social Participation (TMSP). The workshop explored the many ways social media can be applied to national priorities such as health, energy, education, disaster response, political participation, environmental protection, business innovation, or community safety. The workshop attracted graduate students at US universities studying social-networking tools, blogs and microblogs, user-generated content sites, discussion groups, problem reporting, recommendation systems, mobile and location aware media creation, and other social media.
