Google

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.

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Organizers

Alan Neustadtl (@smilex3md) – Sociology, University of Maryland
Jennifer Preece (@jenpre) – iSchool, University of Maryland
Marc Smith(@Marc_Smith) – Social Media Research Foundation
Ben Shneiderman (@benbendc) – Computer Science, University of Maryland
PJ Rey (@pjrey) – Sociology, University of Maryland, Student Coordinator

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Photos

[flickrset id=”72157627509211294″ thumbnail=”thumbnail” photos=”” overlay=”true” size=”small”]

by Dru!

Can useful observations be made by studying the social media sea one bucket at a time?

NodeXL has data import “spigots” for pulling social networks out of several social media systems including Twitter, YouTube, flickr, and email. Twitter networks of follows and followers, reply and mentions can be extracted based on either a user name or a search string “seed”. There are additional networks inside Twitter: a tie is created whenever two people tweet the same URL, for example, or are connected by tweeting from the same general location. For now, the NodeXL Twitter Data Importer is starting with these three initial twitter “tie-types”.

NodeXL queries are not exhaustive collections of Twitter data, we provide a more modest approach, grabbing a slice of recent content and analyzing that. Twitter has a sea of data, NodeXL is importing something like a study of buckets of ocean water. A recent scientific voyage to the Great Pacific Garbage Gyre, for example, collected hundreds of samples of ocean water as they sailed to the central location of the gyre. Each bucket revealed details about the larger state of the ocean (which does not look good). Simlarly, NodeXL is puling buckets of social media network data from the ocean of twitter and, despite the lack of scale, can do some useful science. In part this is a virtue imposed by necessity – constraints imposed by Twitter (even with a rate limit lifted “whitelisted” account) impose significant limits on what can be squeezed out of the Twitter API. For those who lack access to large data center resources, there are scale limits imposed by the capacities of a desktop/laptop device.

Access to large data sets is certainly a hallmark of the “new era of science” that generates observations not from samples but from exhaustive surveys of data terrains. Small samples miss important phenomena it is argued. The counter argument is that many important phenomena appear in most samples, even small ones.

Using the existing features in NodeXL, I can extract the twitter social network for a small group of user accounts. I can provide the names or ask twitter search to deliver them. Alternatively, a keyword can be used to collect all the users and their connections who recently tweeted containing that term. From this selected sample, several observations can be made:

> Not every keyword is equally connected

> Not every twitter user is equally connected nor are their neighbors

> Selected data extractions can be useful in the absence of a global view