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Temporal orientation of tweets for predicting income of users

Hasanuzzaman, Mohammed ORCID: 0000-0003-1838-0091, Kamila, Sabyasachi, Kaur, Mandeep, Saha, Sriparna and Ekbal, Asif (2017) Temporal orientation of tweets for predicting income of users. In: 55th Annual Meeting of the Association for Computational Linguistics, 30 Jul - 4 Aug 2017, Vancouver, Canada.

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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.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Barzilay, Regina and Kan, Min-Yen, (eds.) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Short Papers). . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://doi.org/10.18653/v1/P17-2104
Copyright Information:© 2017 Association for Computational Linguistics (ACL)
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
ID Code:23373
Deposited On:29 May 2019 09:41 by Thomas Murtagh . Last Modified 04 Jan 2021 16:57

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