The Promises and Pitfalls of Latent Attribute Inference
OVERVIEW: The composition of a group determines much of its behavior (are people old or young, PhDs or illiterate, artists or scientists?). As a result, organizations, governments, and companies are deeply interested in being able to quickly learn the makeup of groups. In order to approach this problem, we've been developing technologies for inferring the demographics of Twitter populations from the textual content and networks that the users in them produce. Our methods stand out as the most accurate in the literature. In this talk, I'm going to give an overview of the latent attribute inference problem, discuss the advances that we've made in solving it, and highlight some of the big issues that still need to be tackled.
READINGS:Ruths, D. A., Nakhleh, L., Iyengar, M. S., Reddy, S. A., & Ram, P. T. (2006). Hypothesis generation in signaling networks. Journal of Computational Biology, 13(9), 1546-1557.
Ruths, J & D. Ruths. (2014) Control Profiles of Complex Networks Science 343: 1373-6