@inproceedings{preotiuc-pietro-etal-2017-controlling,
title = "Controlling Human Perception of Basic User Traits",
author = "Preo{\c{t}}iuc-Pietro, Daniel and
Chandra Guntuku, Sharath and
Ungar, Lyle",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1248",
doi = "10.18653/v1/D17-1248",
pages = "2335--2341",
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.",
}
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%0 Conference Proceedings
%T Controlling Human Perception of Basic User Traits
%A Preoţiuc-Pietro, Daniel
%A Chandra Guntuku, Sharath
%A Ungar, Lyle
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F preotiuc-pietro-etal-2017-controlling
%X 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.
%R 10.18653/v1/D17-1248
%U https://aclanthology.org/D17-1248
%U https://doi.org/10.18653/v1/D17-1248
%P 2335-2341
Markdown (Informal)
[Controlling Human Perception of Basic User Traits](https://aclanthology.org/D17-1248) (Preoţiuc-Pietro et al., EMNLP 2017)
ACL
- Daniel Preoţiuc-Pietro, Sharath Chandra Guntuku, and Lyle Ungar. 2017. Controlling Human Perception of Basic User Traits. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2335–2341, Copenhagen, Denmark. Association for Computational Linguistics.