Temporal orientation of tweets for predicting income of users

Mohammed Hasanuzzaman; Sabyasachi Kamila; Mandeep Kaur; Sriparna Saha; Asif Ekbal

doi:10.18653/v1/P17-2104

Temporal orientation of tweets for predicting income of users

Mohammed Hasanuzzaman^*, Sabyasachi Kamila, Mandeep Kaur, Sriparna Saha, Asif Ekbal

^*Corresponding author for this work

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

15 Citations (Scopus)

6 Downloads (Pure)

Abstract

Automatically estimating a user’s socioeconomic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user’s overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.

Original language	English
Title of host publication	Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics
Editors	Regina Barzilay, Min-Yen Kan
Place of Publication	Vancouver
Publisher	Association for Computational Linguistics
Pages	659-665
Number of pages	7
Volume	2
ISBN (Electronic)	9781945626760
DOIs	https://doi.org/10.18653/v1/P17-2104
Publication status	Published - 30 Jul 2017
Externally published	Yes
Event	55th Annual Meeting of the Association for Computational Linguistics 2017 - Vancouver, Canada Duration: 30 Jul 2017 → 04 Aug 2017

Conference

Conference	55th Annual Meeting of the Association for Computational Linguistics 2017
Abbreviated title	ACL 2017
Country/Territory	Canada
City	Vancouver
Period	30/07/2017 → 04/08/2017

ASJC Scopus subject areas

Language and Linguistics
Artificial Intelligence
Software
Linguistics and Language

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10.18653/v1/P17-2104Licence: CC BY

Temporal orientation of tweets for predicting income of users
Copyright 2017 Association for Computational Linguistics and the Authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Final published version, 177 KBLicence: CC BY

Cite this

@inproceedings{d66a1c605b6c42f6933113911a7f3d9e,

title = "Temporal orientation of tweets for predicting income of users",

abstract = "Automatically estimating a user{\textquoteright}s socioeconomic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user{\textquoteright}s overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.",

author = "Mohammed Hasanuzzaman and Sabyasachi Kamila and Mandeep Kaur and Sriparna Saha and Asif Ekbal",

year = "2017",

month = jul,

day = "30",

doi = "10.18653/v1/P17-2104",

language = "English",

volume = "2",

pages = "659--665",

editor = "Regina Barzilay and Min-Yen Kan",

booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics",

note = "55th Annual Meeting of the Association for Computational Linguistics 2017, ACL 2017 ; Conference date: 30-07-2017 Through 04-08-2017",

}

Hasanuzzaman, M, Kamila, S, Kaur, M, Saha, S & Ekbal, A 2017, Temporal orientation of tweets for predicting income of users. in R Barzilay & M-Y Kan (eds), Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. vol. 2, Association for Computational Linguistics, Vancouver, pp. 659-665, 55th Annual Meeting of the Association for Computational Linguistics 2017, Vancouver, Canada, 30/07/2017. https://doi.org/10.18653/v1/P17-2104

Temporal orientation of tweets for predicting income of users. / Hasanuzzaman, Mohammed; Kamila, Sabyasachi; Kaur, Mandeep et al.
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. ed. / Regina Barzilay; Min-Yen Kan. Vol. 2 Vancouver: Association for Computational Linguistics, 2017. p. 659-665.

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

TY - GEN

T1 - Temporal orientation of tweets for predicting income of users

AU - Hasanuzzaman, Mohammed

AU - Kamila, Sabyasachi

AU - Kaur, Mandeep

AU - Saha, Sriparna

AU - Ekbal, Asif

PY - 2017/7/30

Y1 - 2017/7/30

N2 - Automatically estimating a user’s socioeconomic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user’s overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.

AB - Automatically estimating a user’s socioeconomic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user’s overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.

U2 - 10.18653/v1/P17-2104

DO - 10.18653/v1/P17-2104

M3 - Conference contribution

AN - SCOPUS:85040563864

VL - 2

SP - 659

EP - 665

BT - Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics

A2 - Barzilay, Regina

A2 - Kan, Min-Yen

PB - Association for Computational Linguistics

CY - Vancouver

T2 - 55th Annual Meeting of the Association for Computational Linguistics 2017

Y2 - 30 July 2017 through 4 August 2017

ER -

Temporal orientation of tweets for predicting income of users

Abstract

Conference

ASJC Scopus subject areas

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