Introduction (family picture, Russia picture, BYU, Umich and iSchools, Maryland - 4 years, HCIL, CASCI, IGERT)Talk about iSchools
I’m interested in findings ways of leveraging these social technologies to promote the public good.See special issue of IEEE Computer, Nov. 2010 focused on Technology-Mediated Social Participation.
Much of my work in this area focuses on the analysis of online social networks. Our goal is to democratize the analysis of social network analysis through the creation of new tools, such as NodeXL, and new methods and visualization techniques that help make sense of the mass of data created everyday by social sites.
My work in this area focuses on designing novel socio-technical solutions to practical TMSP problems. For example, I’ve partnered with FamilySearch Indexing to assess alternate crowdsourcing quality control mechanisms, and developed the “veiled viral marketing” approach to disseminate information on stigmatized illnesses via social networking sites. My current work focuses largely on social games with a purpose – such as Floracaching, which helps collect useful scientific data on plant phenology via a location-based game in the form of a mobile app; Odd Leaf Out, which helps identify errors in plant identification datasets; and Alternate Reality Games which help teach computational thinking skills to youth who help fictional characters solve problems to advance a fictional narrative.
Thanks to Marc Smith for the slideOne key characteristic of technology-mediated communication is that it can (and typically does) capture detailed data on social interactions.Just like footprints left on the sand tell a story about walking a dog on the beach, our digital footprints tell stories about our online behaviors and interactions.The mass of data created by social media has the potential to usher in a golden age of social science and data-driven decision making.However, to put this social data to good use by researchers, as well as non-technical community managers and decision makers, we need usable and powerful tools that support social media data analysis.
Thanks to Marc Smith for the slideName as many connections (ties) as you can between people on Facebook or Pinterest.
Thanks to Marc Smith for the slide
This is my personal facebook network. Noticed those with high betweenness centrality (images and larger), clear clusters identified by a clustering algorithm that match up well with the descriptions I added later.
Thanks to Marc Smith for this slide
Thanks to Marc Smith for his slides.
Options for getting network data
Guest Lecture Irvine
Analyzing Social Media Networks
Derek L. Hansen
Center for the Advanced Study
of Communities and Information
New Tools & Methods to Analyze
What is Social Media?
A set of networked technologies that
supports social interactions.
Social media is about “transforming
monologue (one-to-many) into dialog
Types of Social Media
Email, Google groups, Yahoo Answers, Listservs, Stack
Instant Messaging, IRC, Skype, Google Hangouts
Collaborative Authoring Wikipedia, Wikia, Google Docs
Blogs & Podcasts Livejournal, Blogger, Twitter, Vlogs, podcasts, photo blogs
Social Sharing YouTube, Flickr, Instagram, Pinterest, Last.Fm, Delicious,
Social Networking Facebook, LinkedIn, eHarmony, Ning, Ravelry
Online Markets &
eBay, Amazon, craigslist, Kiva, Thraedless, TopCoder,
Idea Generation IdeaConnection, IdeaScale
Virtual Worlds Webkinz, World of Warcraft, Club Penguin, Second Life
Mobile-based Services Foursquare, MapMyRun, Geocaching
• Central tenet
– Social structure emerges from
– the aggregate of relationships (ties)
– among members of a population
• Phenomena of interest
– Emergence of cliques and clusters
– from patterns of relationships
– Centrality (core), periphery (isolates),
– Surveys, interviews, observations,
log file analysis, computational
analysis of matrices
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)
Source: Richards, W.
(1986). The NEGOPY
program. Burnaby, BC:
Fraser University. pp.7-
Social Network Theory
– “actor” on which relationships act; 1-mode versus 2-mode networks
– Relationship connecting nodes; can be directional
• Cohesive Sub-Group
– Well-connected group; clique; cluster
• Key Metrics
– Centrality (group or individual measure)
• Number of direct connections that individuals have with others in the group (usually look at
incoming connections only)
• Measure at the individual node or group level
– Cohesion (group measure)
• Ease with which a network can connect
• Aggregate measure of shortest path between each node pair at network level reflects
– Density (group measure)
• Robustness of the network
• Number of connections that exist in the group out of 100% possible
– Betweenness (individual measure)
• # shortest paths between each node pair that a node is on
• Measure at the individual node level
• Node roles
– Peripheral – below average centrality
– Central connector – above average centrality
– Broker – above average betweenness
A B D E
Social Network Analysis
A systematic method for understanding relationships
• Betweenness Centrality
• Degree Centrality
• Eigenvector Centrality
• Closeness Centrality
Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith.
2007. Visualizing the Signatures of Social Roles in Online
The Journal of Social Structure. 8(2).
Experts and “Answer People”
Discussion starters, Topic setters
Discussion people, Topic setters
Social Media Research Foundation
People Disciplines Institutions
University Faculty Computer Science
University of Maryland
Brigham Young University
Students HCI, CSCW Oxford Internet Institute
Industry Machine Learning Stanford University
Independent Information Visualization Microsoft Research
Researchers UI/UX Illinois Institute of
Developers Social Science/Sociology Connected Action
Network Analysis Cornell
Collective Action Morningside Analytics