Developing age and gender predictive lexica over social media

Maarten Sap; Gregory Park; Johannes C. Eichstaedt; Margaret L. Kern; David Stillwell; Michal Kosinski; Lyle H. Ungar; H. Andrew Schwartz

doi:10.3115/v1/d14-1121

Developing age and gender predictive lexica over social media

Maarten Sap
, Gregory Park
, Johannes C. Eichstaedt
, Margaret L. Kern
, David Stillwell
, Michal Kosinski
, Lyle H. Ungar
, H. Andrew Schwartz

Computer Science

University of Pennsylvania
University of Cambridge

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

190 Scopus citations

Abstract

Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predictive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels. The lexica, made publicly available,1 achieved state-of-the-art accuracy in language based age and gender prediction over Facebook and Twitter, and were evaluated for generalization across social media genres as well as in limited message situations.

Original language	English
Title of host publication	EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
Publisher	Association for Computational Linguistics (ACL)
Pages	1146-1151
Number of pages	6
ISBN (Electronic)	9781937284961
DOIs	https://doi.org/10.3115/v1/d14-1121
State	Published - 2014
Event	2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014 - Doha, Qatar Duration: Oct 25 2014 → Oct 29 2014

Publication series

Name	EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Conference

Conference	2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014
Country/Territory	Qatar
City	Doha
Period	10/25/14 → 10/29/14

Access to Document

10.3115/v1/d14-1121

Cite this

Sap, M., Park, G., Eichstaedt, J. C., Kern, M. L., Stillwell, D., Kosinski, M., Ungar, L. H., & Schwartz, H. A. (2014). Developing age and gender predictive lexica over social media. In EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 1146-1151). (EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/d14-1121

Sap, Maarten ; Park, Gregory ; Eichstaedt, Johannes C. et al. / Developing age and gender predictive lexica over social media. EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2014. pp. 1146-1151 (EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference).

@inproceedings{a9bc2267ec64433c9ce47fc0a7a559ab,

title = "Developing age and gender predictive lexica over social media",

abstract = "Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predictive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels. The lexica, made publicly available,1 achieved state-of-the-art accuracy in language based age and gender prediction over Facebook and Twitter, and were evaluated for generalization across social media genres as well as in limited message situations.",

author = "Maarten Sap and Gregory Park and Eichstaedt, \{Johannes C.\} and Kern, \{Margaret L.\} and David Stillwell and Michal Kosinski and Ungar, \{Lyle H.\} and Schwartz, \{H. Andrew\}",

note = "Publisher Copyright: {\textcopyright} 2014 Association for Computational Linguistics.; 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014 ; Conference date: 25-10-2014 Through 29-10-2014",

year = "2014",

doi = "10.3115/v1/d14-1121",

language = "English",

series = "EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",

publisher = "Association for Computational Linguistics (ACL)",

pages = "1146--1151",

booktitle = "EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",

}

Sap, M, Park, G, Eichstaedt, JC, Kern, ML, Stillwell, D, Kosinski, M, Ungar, LH & Schwartz, HA 2014, Developing age and gender predictive lexica over social media. in EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, Association for Computational Linguistics (ACL), pp. 1146-1151, 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, Doha, Qatar, 10/25/14. https://doi.org/10.3115/v1/d14-1121

Developing age and gender predictive lexica over social media. / Sap, Maarten; Park, Gregory; Eichstaedt, Johannes C. et al.
EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2014. p. 1146-1151 (EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Developing age and gender predictive lexica over social media

AU - Sap, Maarten

AU - Park, Gregory

AU - Eichstaedt, Johannes C.

AU - Kern, Margaret L.

AU - Stillwell, David

AU - Kosinski, Michal

AU - Ungar, Lyle H.

AU - Schwartz, H. Andrew

PY - 2014

Y1 - 2014

N2 - Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predictive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels. The lexica, made publicly available,1 achieved state-of-the-art accuracy in language based age and gender prediction over Facebook and Twitter, and were evaluated for generalization across social media genres as well as in limited message situations.

AB - Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predictive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels. The lexica, made publicly available,1 achieved state-of-the-art accuracy in language based age and gender prediction over Facebook and Twitter, and were evaluated for generalization across social media genres as well as in limited message situations.

UR - https://www.scopus.com/pages/publications/84961292097

U2 - 10.3115/v1/d14-1121

DO - 10.3115/v1/d14-1121

M3 - Conference contribution

AN - SCOPUS:84961292097

T3 - EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

SP - 1146

EP - 1151

BT - EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

PB - Association for Computational Linguistics (ACL)

T2 - 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014

Y2 - 25 October 2014 through 29 October 2014

ER -

Sap M, Park G, Eichstaedt JC, Kern ML, Stillwell D, Kosinski M et al. Developing age and gender predictive lexica over social media. In EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2014. p. 1146-1151. (EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference). doi: 10.3115/v1/d14-1121

Developing age and gender predictive lexica over social media

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this