@inproceedings{kim-etal-2017-demographic,
title = "Demographic Inference on {T}witter using Recursive Neural Networks",
author = "Kim, Sunghwan Mac and
Xu, Qiongkai and
Qu, Lizhen and
Wan, Stephen and
Paris, C{\'e}cile",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2075",
doi = "10.18653/v1/P17-2075",
pages = "471--477",
abstract = "In social media, demographic inference is a critical task in order to gain a better understanding of a cohort and to facilitate interacting with one{'}s audience. Most previous work has made independence assumptions over topological, textual and label information on social networks. In this work, we employ recursive neural networks to break down these independence assumptions to obtain inference about demographic characteristics on Twitter. We show that our model performs better than existing models including the state-of-the-art.",
}
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<abstract>In social media, demographic inference is a critical task in order to gain a better understanding of a cohort and to facilitate interacting with one’s audience. Most previous work has made independence assumptions over topological, textual and label information on social networks. In this work, we employ recursive neural networks to break down these independence assumptions to obtain inference about demographic characteristics on Twitter. We show that our model performs better than existing models including the state-of-the-art.</abstract>
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%0 Conference Proceedings
%T Demographic Inference on Twitter using Recursive Neural Networks
%A Kim, Sunghwan Mac
%A Xu, Qiongkai
%A Qu, Lizhen
%A Wan, Stephen
%A Paris, Cécile
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F kim-etal-2017-demographic
%X In social media, demographic inference is a critical task in order to gain a better understanding of a cohort and to facilitate interacting with one’s audience. Most previous work has made independence assumptions over topological, textual and label information on social networks. In this work, we employ recursive neural networks to break down these independence assumptions to obtain inference about demographic characteristics on Twitter. We show that our model performs better than existing models including the state-of-the-art.
%R 10.18653/v1/P17-2075
%U https://aclanthology.org/P17-2075
%U https://doi.org/10.18653/v1/P17-2075
%P 471-477
Markdown (Informal)
[Demographic Inference on Twitter using Recursive Neural Networks](https://aclanthology.org/P17-2075) (Kim et al., ACL 2017)
ACL
- Sunghwan Mac Kim, Qiongkai Xu, Lizhen Qu, Stephen Wan, and Cécile Paris. 2017. Demographic Inference on Twitter using Recursive Neural Networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 471–477, Vancouver, Canada. Association for Computational Linguistics.