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Social interactions have always been an important part of human learning and experience. We now know that social interactions are critical in many knowledge and information processes. Research has shown results ranging from influences on our behavior from social networks [Aral2012] to our understanding of social belonging on health [Walton2011], as well as how conflicts and coordination play out in Wikipedia [Kittur2007]. Interestingly, social scientists have studied social interactions for many years, but it wasn’t until very recently that researchers can study these mechanisms through the explosion of services and data available on web-based social systems.
In this talk, I plan to illustrate a model-driven approach to researching social interactions on the Web. Our research methods and systems are informed by models such as information scent, sensemaking, information theory, probabilistic models, and evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmarks in Delicious to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors. By using this model-driven approach, we constructed a path forward for further social interaction research.
Bio:
Ed H. Chi is a Staff Research Scientist at Google, focusing on social interaction research relating to social search, recommendation, annotations, and analytics. Previous to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center’s Augmented Social Cognition Group, where he led the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press.
With over 20 patents and over 90 research articles, he is known for research in Web and online social sites, and the effects of social signals on user behavior. For example, he led a group of researchers at PARC to understand the underlying mechanisms in Wikipedia. he has also worked on information visualization, computational molecular biology, ubicomp, and recommendation/search engines, and has won awards for both teaching and research. In his spare time, Ed is an avid photographer and snowboarder.
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