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Preoţiuc-Pietro, Daniel, Sina Samangooei, Vasileios Lampos, Trevor Cohn, Nick Gibbins, and Mahesan Niranjan. Clustering models for discovery of regional and demographic variation . Public Deliverable for Trendminer Project, 2013.PDF
Conference Proceedings
Preotiuc-Pietro, Daniel, Sharath Chandra Guntuku, and Lyle Ungar. Controlling Human Perception of Basic User Traits In EMNLP., 2017. AbstractPDFPoster

Much of our online communication is text-mediated and, lately, more common with automated agents. Unlike interacting with humans, these agents currently do not tailor their language to the type of person they are communicating to. In this pilot study, we measure the extent to which human perception of basic user trait information – gender and age – is controllable through text. Using automatic models of gender and age prediction, we estimate which tweets posted by a user are more likely to mis-characterize his traits. We perform multiple controlled crowdsourcing experiments in which we show that we can reduce the human prediction accuracy of gender to almost random – a > 20% drop in accuracy. Our experiments show that it is practically feasible for multiple applications such as text generation, text summarization or machine translation to be tailored to specific traits and perceived as such.