Temporal orientation of tweets for predicting income of users
Hasanuzzaman, MohammedORCID: 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.
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.
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).
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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