For our March Meetup, we are very pleased to have Prof. Jennifer Golbeck from the University of Maryland talking about her research in the field of social media and social network analysis. Public information on social network sites can be very informative about individuals' interests and about group communication and influence. Building computational and statistical models of these processes is a rapidly advancing field, and any data scientist will want to learn how to think about and work with this sort of data.
NOTE: We are holding this event at a new venue -- the Microsoft offices in Chevy Chase/Friendship Heights. We realize that this will make it much easier for some of you to attend than our usual downtown location, and much harder for others. In general, expect DSDC events to be downtown most of the time, and Metro-accessible all of the time.
- 6:30pm -- Networking and Refreshments
- 7:00pm -- Introduction
- 7:15pm -- Presentation and discussion
- 8:30pm -- Post presentation conversations
- 8:45pm -- Adjourn for Data Drinks (Clyde's, 5441 Wisconsin Ave.)
Uncovering Hidden Social Information: Inferring User Traits and Relationships Online
People share a lot of information about themselves in social media such as Facebook and Twitter. However, through all that sharing, additional hidden information is embedded. In this talk, I will discuss several studies we have run that infer information about users and their relationships online, including predictions of users' personality traits, their political preferences, and the trust they have in one another. I will discuss the results of these studies, the methodologies used, and how our techniques can be applied to similar
Jennifer Golbeck is Director of the Human-Computer Interaction Lab and an Associate Professor in the College of Information Studies at the University of Maryland, College Park.
Her research focuses on analyzing and computing with social media. This includes building models of social relationships, particularly trust, as well as user preferences and attributes, and using the results to design and build systems that improve the way people interact with information online.
She is a Research Fellow of the Web Science Research Initiative and in 2006, she was selected as one of IEEE Intelligent Systems' Top Ten to Watch, a list of their top young AI researchers. She has a PhD in Computer Science from the University of Maryland, College Park.
Her new book, Analyzing the Social Web, was published on March 26th. You should definitely buy a copy.