Date: February 6
Topic: "Understanding Language Variation in Social Media"
Presenter: Jacob Eisenstein
Abstract:
An increasing amount of informal communication is conducted in written form, through computer-mediated channels such as social media. This offers new challenges and opportunities for large-scale text analysis. I argue that social media analytics should be guided by an understanding of how social media language differs from other written language, and why. I will describe a series of studies that document language variation across a number of different social variables. In some cases, social media writing tracks spoken language variation; in other cases, relatively novel "netspeak" terms like emoticons and abbreviations can also be strongly correlated with demographics and geography. Our recent work concerns language change over time, using a new dataset of hundreds of thousands of authors over nearly three years. Aggregating across thousands of words, we build a unified model of the geographic and demographic factors that drive the spread of words between cities.
This research is in collaboration with David Bamman, Brendan O'Connor, Tyler Schnoebelen, Noah A. Smith, Eric P. Xing, and Yi Yang.
Bio:
Jacob Eisenstein is an assistant professor in the School of Interactive Computing at Georgia Tech. He works on statistical natural language processing, focusing on social media analysis, discourse, and latent variable models. Eisenstein was a postdoctoral researcher at Carnegie Mellon and the University of Illinois. He completed his Ph.D. at MIT in 2008, winning the George M. Sprowls dissertation award.
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