I will talk about the themes Thinking across Boundaries, Learning by Doing, and Innovating through Collaboration in the context of the work of the Social Media Research Foundation to deliver an end-user friendly, free and open tool for social media network analysis.
The NodeXL project from the Social Media Research Foundation has crossed many boundaries, notably bridging the divide between the social sciences and the computer sciences.
The Social Media Research Foundation team has innovated at multiple levels: organizationally we are a modern, virtual, distributed group of collaborators. Technically, we have focused our project on ease of use and automation rather than scale and sophistication, our users are not programmers. We have implemented many innovative network analysis and visualization techniques because we have been driven by a need to serve a diverse user population. The contributors to the project are themselves from a diverse range of disciplinary backgrounds, making it easier to shape the tool for the broadest audience.
Papers and Participation
As part of the
2014 International Conference on Collaboration Technologies and Systems
May 19-23, 2014
The Commons Hotel
Minneapolis, Minnesota, USA
ACM, IEEE, and IFIP
Hosted by The Innovation Enterprise, the event provides 30+ industry case studies and over 20 hours of networking opportunities across 2 days.
The summit will cover the important topics relevant to business today:
- Measuring, Evaluating & Predicting the Social Consumer
- What Consumers Say vs. What They Do
- Multi-Platform Consumer Engagement
- Journey to Social Media ROI
- Multi-Variate Experiments on the Web
- Scientifically Measuring the Wealth of your Website
You can follow the event on Twitter with the hashtag: #IESocialWeb.
|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 HCIL Government Applications of Social Media Networks &
Communities Workshop, as part of the 27th Annual Human Computer Interaction Lab (HCIL) Symposium, at the University of Maryland, examined how social media can be systematically applied to increase civic participation on national priorities.
When: Friday, May 28, 2010, 9:30am-4:00pm
Where: CSIC Building, UMD, College Park
Who: Government thought leaders, system developers, and agencies; industry partners, researchers, and students
Front row (left to right): Brad Hesse, Betsy Rebert, Claudia Louis,
Vladimir Barash, Derek Hansen, Robin Naughton.
Middle row: Scot Golder, Rex Robison, Yan Qu, Joe Pringle, Natasa
Milic-Frayling, Amanda Shanor, Leonard Lidov, Laura Milner
Back row: Robert Altiero, Mark Edson, Keith Walker, Tim Clausner, Marc
Smith, Nick Violi, Brian Dennis, Manuel Freire, John Bertot, Derrick
Cogburn, Jennifer Preece, Francy Stilwell
Not pictured: Ben Shneiderman
The Friday, May 28th all day event focused on the use of social media data in improving the quality of government.
May 28th, 2010: Government Applications of Social Media Networks and Communities
Derek Hansen, Marc Smith, Jenny Preece, Ben Shneiderman
Federal, state, and local governments are discovering interesting and ambitious ways that social media can be used to increase civic participation in decision-making, health-care /wellness, energy sustainability, education, disaster response, community safety, scientific research, etc. This workshop will invite attendees to present current projects, design strategies, evaluation methods, and analytic tools. Issues such as universal accessibility & usability, privacy protection, and reliability will be discussed.
• discussed interesting and ambitious ways that federal, state, and local governments are using social media in decision-making,healthcare/wellness, energy sustainability, education, disaster response, community safety, scientific research, etc.
• identified the unique challenges of using social technologies in a government context and design strategies and policies that help overcome those challenges
• Vladimir Barash, Doctoral student, Information Science, Cornell University
• John Bertot, Professor, iSchool, UMD
• Derek Hansen – Assistant Professor, iSchool, UMD and director of Center for the Advanced Study of Communities and Information (CASCI)
• Scott Golder, Doctoral student, Sociology, Cornell University
• Bradford Hesse, Chief of the National Cancer Institute’s Health Communication and Informatics Research Branch (HCIRB)
• Natasa Milic-Frayling, Principal Researcher, Microsoft Research Cambridge
• Cynthia Parr, Director, Special Pages Group, Encyclopedia of Life, Smithsonian
• Jenny Preece – Dean, iSchool, UMD.
• Ben Shneiderman – Professor, Department of Computer Science, UMD, and founder of the Human Computer Interaction Lab (HCIL).
• Marc Smith – Chief Social Scientist, Connected Action Consulting Group, director of the Social Media Research Foundation
Slides available from:
Michael Kearns Keynote
Experiments: Graph Coloring / Consensus / Voting
Topology of the Network vs. what was the network used for?
Voting experiments – similar to consensus, with a crucial strategic difference.
Introduce a tension between:
-Color choices; challenge comes from competing incentives
Red, blue. People unaware of global network structure
Payoffs: if everyone picks same color w/in 2 minutes, experiment ends, and everyone gets some payoff. But different players have different incentives (e.g. I may get paid p if everyone converges to blue, but 2p if everyone converges to red). If there is no consensus, nobody gets a payoff
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment (Tumasjan et al.)
Successful use of social media in las presidential campaign has established twitter as an integral part of political campaign toolbox
Goal: analyze on Twitter: 1. Deliberation, 2. Sentiment, 3. Prediction
Deliberation: Honeycutt and Herring – Twitter not only used for one-way comm, but 31% of all tweets direct a specific addressee. Kroop and Jansen – political internet discussion boards dominated by small # of heavy users
Sentiment: How accurately can Twitter inform us about the electorate’s political sentiment?
Prediction: can Twitter serve as a predictor of the election result?
Data: examined more than 100k tweets and extracted their sentiment using LIWC
Target: German federal election 2009
1. While Twitter is used as a forum for political deliberation on substantive issues, this forum is dominated by heavy users
Two widely accepted indicators of blog-based deliberation:
-The exchange of substantive issues (31% of all messages contain “@”),
-Equality of participaion: While the distribution of users across groups is almost identical with the one found on internet message boards, we find even less equality of participation for the political debate on Twitter. Additional analyses have shown users to exhibit a party-bias in the volume and sentiment of messages.
2. The online sentiment in tweets reflects nuanced offline differences between the politicians in our sample.
-Leading candidates: Very similar profile for all leading candidates, only polarizing political characters, such as liberal leader and socialist, deviate in line with their roles as opposition leaders. Messages mentioning Steinmeir (coalition leader) are most tentative
3. Similarity of profiles is a plausible reflection of the political proximity between the parties
Key findings: high convergence of leading candidates, more divergence among politicians of governin grand coalition than among those of a potential right wing coalition
4. Activity on Twitter prior to election seems to validly reflect the election outcome (MAE 1.65%), and joint party mentions accurately reflect the political ties between parties.
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series (Brendan O’Connor)
We will be liveblogging (when possible) from ICWSM 2010, going on now!
Keynote: Bob Kraut, CMU
Fourth International AAAI Conference on Weblogs and Social Media
May 23-26, 2010
George Washington University, Washington, DC
Sponsored by the Association for the Advancement of Artificial Intelligence
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.
SA2: Introduction to Social Media Network Analysis
Marc Smith (Connected Action) and
Derek Hansen (University of Maryland)
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.