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Talk Announcement — Narrating with Networks: Making Sense of Event Log Data with Socio-Technical Trajectories — Fri 8/16

By andresmh8 months ago - permalink

Tags: talks networks computational social science wikipedia news

UPDATE: Check out the video recording of this research talk.
Brian Keegan is visiting us from Northeastern University to talk about his work studying a wide range of networks: from Wikipedia, to Twitter, to MMGO’s. One of his areas of research that I find most fascinating is the use of network science to understand how people react to breaking news events such as natural disasters, accidents, and crimes. For example, in one of his recent studies, he and his colleagues found that most of the Wikipedia articles about breaking news had a cohesive group of editors that “coalesced” in less than 24 hrs (compared to a year for other types articles!), and these articles attracted journalist-like Wikipedians who specialize in reporting breaking news events. Here is a plot where they showed this: 

Breaking news events are in red, articles about recent but non-breaking news events are in blue, and articles about historical events are in green. The x-axis is time and the y-axis is the number of articles in the giant component.

Brian tells us how he goes beyond studying networks in terms of friendships or affiliations, by looking at event logs, and how this approach helps us understand social phenomena in new ways:

Network science provides a rich set of theories and methods to understand the structure and dynamics of complex social, information, and biological systems. These approaches traditionally demand data with explicitly declared dyadic relationships or interactions such as friendship or affiliation. However, socio-technical systems like Wikipedia, Github, or Twitter often encode latent relationships within event logs and other databases. Using several case studies, I describe how complex networks called “socio-technical trajectories” can be extracted from event logs to understand the behavior of both users and artifacts within these systems. These trajectories encode a variety of rich structural and dynamic data distinct from traditional network approaches and illustrate user social roles within distributed collaboration as well as context and shifting interests of users based on their contributions. This approach has rich implications for mixed-methods research as it allows researchers to collapse large-scale event log data into more parsimonious network representations that can motivate qualitative analysis, visualization, and statistical modeling of complex user behavior.
Brian Keegan is a computational social scientist and post-doctoral research fellow at Northeastern University. He received his PhD in 2012 from Northwestern University and his dissertation examined the history, structure, and dynamics of Wikipedia’s coverage of breaking news events. He draws upon theories and methods from network science, computer-supported cooperative work, computer-mediated communication, and organizational studies to understand high-tempo knowledge work, online political communication, and network forms of organization and innovation. His research has been published in the American Behavioral Scientist, CSCW, ICWSM, WWW, and IEEE Social Computing.
This talk is on Friday August 16 at 10:30 AM at Microsoft Research’s Building 99 (room 1915A). For more info, please contact me at msrtalks@andresmh.com.
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