The SBP conference provides a forum for researchers and practitioners from academia, industry, and government agencies to exchange ideas on current challenges in social computing, behavioral modeling and prediction, and on state-of-the-art methods and best practices being adopted to tackle these challenges. Interactive events at the conference are designed to promote cross-disciplinary contact.
Social Computing harnesses the power of computational methods to study social behavior within a social context. Behavioral Cultural modeling refers to representing behavior and culture in the abstract, and is a convenient and powerful way to conduct virtual experiments and scenario analysis. Both social computing and behavioral cultural modeling are techniques designed to achieve a better understanding of complex behaviors, patterns, and associated outcomes of interest. Moreover, these approaches are inherently interdisciplinary; subsystems and system components exist at multiple levels of analysis (i.e., “cells to societies”) and across multiple disciplines, from engineering and the computational sciences to the social and health sciences.
This Deep Dive will be an active event. We will mix thoughtful discussions with experiential activities, building social capital while we learn about social networks. Participants are encouraged to submit social media topics in advance so maps and reports can be generated for the event.
I am excited to have the opportunity to present a NodeXL workshop at the DC Data Community on November 13th at 6pm in Washington, D.C.
In this session I will describe the ways NodeXL can simplify the process of collecting, storing, analyzing, visualizing and publishing reports about connected structures.
For example, this is a map of the connections among the people who recently tweeted about the DataCommunityDC Twitter account was created with just a few clicks and no coding:
This graph represents a network of 67 Twitter users whose recent tweets contained “DataCommunityDC“, taken from a data set limited to a maximum of 10,000 tweets. The network was obtained from Twitter on Tuesday, 05 November 2013 at 15:15 UTC.
The tweets in the network were tweeted over the 7-day, 16-hour, 4-minute period from Monday, 28 October 2013 at 22:38 UTC to Tuesday, 05 November 2013 at 14:42 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 network has been segmented into groups (“G1, G2, G3…”) and each group is labeled with the words most frequently used in the tweets from the people in that group.
The size of each Twitter user’s profile picture represents the log scaled value of their follower count.
Analysis of the network location of each participant reveals the people in key locations in the network, people at the “center” of the graph.
July 28 – August 1, 2013
DSST 2013 Digital Societies and Social Technologies Summer Institute: NodeXL Training University of Maryland — College Park, Maryland USA
I will be teaching a workshop on Thursday August 1st on using NodeXL for social media network analysis at the upcoming 2013 Digital Societies and Social Technologies Summer Institute at the University of Maryland. The Institute is devoted to training researchers in methods and theory that can help frame research into the social impacts of information technology:
MOOCs, Education and learning; personal health and well-being; open innovation, eScience, and citizen science; co-production, open source, and new forms of work; cultural heritage and information access; energy management and climate change; civic hacking, engagement and government; disaster response; cybersecurity and privacy – these are just a few problem domains where effective design and robust understanding of complex sociotechnical systems is critical. To meet these challenges a trans-disciplinary community of scholars has come together from fields as wide ranging as CSCW, HCI, social computing, organization studies, information visualization, social informatics, sociology, information systems, medical informatics, computer science, ICT for development, education, learning science, journalism, and political science.
ICWSM-12, features a program of workshops, tutorials, contributed technical talks, posters and invited presentations. The main conference features keynote talks from prominent social scientists and technologists.
Andrew Tomkins is an engineering director at Google working on measurement, modelling, and analysis of content, communities, and users on the World Wide Web. Prior to joining Google, he spent four years at Yahoo! as chief scientist of search, and eight years at IBM’s Almaden Research Center, where he co-founded the WebFountain project. Andrew holds Bachelors degrees in Math and CS from MIT, and a PhD in CS from Carnegie Mellon University; he has published over a hundred technical papers.
Patrick Meier is a recognized expert and thought leader on the intersection between new technologies, crisis early warning, humanitarian response and human rights. He is the co-founder of the International Network of Crisis Mappers and previously co-directed Harvard University’s Program on Crisis Mapping and Early Warning. Over the past 10 years, Patrick has consulted extensively with several international organizations including the UN, OSCE and OECD in Africa, Asia and Europe. Patrick is also a distinguished scholar completing his PhD at The Fletcher School during which time he was a Doctoral Fellow at Stanford University. In 2010, President Bill Clinton publicly thanked him for his leadership and contributions. He blogs at iRevolution.net.
Lada A. Adamic is an associate professor in the School of Information and the Center for the Study of Complex Systems at the University of Michigan. She is also affiliated with EECS. Her research interests center on information dynamics in networks: how information diffuses, how it can be found, and how it influences the evolution of a network’s structure. Her projects have included identifying expertise in online question and answer forums, studying the dynamics of viral marketing, and characterizing the structure in blogs and other online communities. She has received an NSF CAREER award, and best paper awards from Hypertext ’08, ICWSM-10 and ICWSM-11, and the most influential paper of the decade award from Web Intelligence ’11.
“The goal of the workshop is to bring together researchers and industry practitioners interested in visual and interactive techniques for social media analysis, particularly in social sciences and humanities as well as in industry and to discuss ideas, techniques, and applications to support social media analysis.”
I will present a tutorial on Social Media Network Analysis with NodeXL on June 4th at the event:
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.
This network graph represents a network of 29 Twitter users whose recent tweets contained “icwsm”. The network was obtained on Saturday, 21 April 2012 at 20:33 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 Saturday, 14 April 2012 at 18:55 UTC. The latest tweet in the network was tweeted on Saturday, 21 April 2012 at 05:48 UTC.
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 layout algorithm.
The edge colors are based on relationship values. The vertex sizes are based on followers values.
Top 10 Vertices, Ranked by Betweenness Centrality:
@_akisato Overall Graph Metrics:
Unique Edges: 68
Edges With Duplicates: 32
Total Edges: 100
Connected Components: 5
Single-Vertex Connected Components: 4
Maximum Vertices in a Connected Component: 25
Maximum Edges in a Connected Component: 96
Maximum Geodesic Distance (Diameter): 3
Average Geodesic Distance: 1.866455
Graph Density: 0.082512315270936
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 . 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 , 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.
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
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.
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.
This workshop provides an overview of Social Network Analysis (SNA) and its application to social media. The network or directed graph is a common structure in a wide range of different kinds of social media. Social Network Analysis is a set of tools, concepts, and techniques that can help measure a graph and the location and connection pattern of each component part.
Using NodeXL, workshop participants will learn how to take data from common social media sources (including enterprise discussions and online communities, Twitter, Flickr, your own email) and perform various types of analysis. Through this workshop, participants will:
· be able to understand the basics of SNA, its terminology and background.
· be able to transform communication data (e.g. Twitter, email, Flickr, message boards etc.) into network data.
· understand the different possible presentations of social networks, e.g. in a matrix or a sociogram.
· apply network metrics and visualizations to find clusters and key contributors in real world social media data.
· get familiar with the use of standard SNA tools and software in general and the NodeXL social network analysis add-in for Excel in particular.
· be able to derive practical and useful information through SNA analysis that would help design an innovative and successful online community.
We plan to continue to expand the tutorial to include a step-by-step guide to the analysis of several major social media sites like Twitter, Facebook, Wikipedia, YouTube, delicious, and flickr as well as personal stores of social media like your own email (if it is stored in a Windows Search Index found on most Windows desktops). Our goal is to create an easy-to-follow guide to network theory for people who new to the field or who do not want to develop programming skills to perform network analysis. We are focused on social media as a data source for social media although other examples are included, like the United States Senate voting network that reveals interesting patterns in the connections created when votes are cast. Using 2007 data it reveals which Senators are most likely to change party affiliation.
Your comments, corrections, and suggestions for improving the document are welcome.
Instructors interested in teaching classes about social networks are welcome to make use of both the NodeXL toolkit and the document to guide students through the core concepts of social network theory.