Limits to Socio-Cultural Inference from Tweets & Books

Wednesdays@NICO | 12:00-1:00 PM, May 4, 2016 | Chambers Hall, Lower Level

Chris Danforth, Associate Professor, Mathematics & Statistics, College of Engineering and Mathematical Sciences, University of Vermont

Abstract

Scientific analysis of large-scale text has begun to reveal remarkable insights into human behavior. Indeed, there is growing evidence that our society's daily online interactions can be appropriately aggregated into digital measures of physical mobility, emotional health, and linguistic evolution. However, the socio-technical instruments of tomorrow will be limited by the quality of the data they are fed, as well as human awareness of their existence. This talk will describe our ongoing effort to quantify the population-scale sentiment associated with any topic using Twitter, including a public health application where we found 80% of opinions to be expressed by non-human actors. We will also describe our recent analysis of the Google Books corpus, and point to the need to fully characterize its behavior before drawing broad conclusions about cultural dynamics.

Bio

Chris Danforth is the Flint Professor of Mathematical, Natural, and Technical Sciences at the University of Vermont. With colleague Peter Dodds, he co-directs the Computational Story Lab, a group of applied mathematicians and data scientists at the undergraduate, masters, PhD, and postdoctoral level working on large-scale, systems problems in many fields including sociology, nonlinear dynamics, networks, ecology, and physics. The group has built several socio-technical instruments including the Hedonometer and the Lexicocalorimeter. Danforth's formal background is in nonlinear dynamics applied to weather and climate prediction, and he is a member of the Mathematics & Climate Research Network.

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