Controlling Human Perception of Basic User Traits

Daniel Preoţiuc-Pietro, Sharath Chandra Guntuku, Lyle Ungar


Abstract
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 – an over 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.
Anthology ID:
D17-1248
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2335–2341
Language:
URL:
https://www.aclweb.org/anthology/D17-1248
DOI:
10.18653/v1/D17-1248
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1248.pdf
Poster:
 D17-1248.Poster.pdf