Temporal Orientation of Tweets for Predicting Income of Users

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


Abstract
Automatically estimating a user’s socio-economic 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.
Anthology ID:
P17-2104
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
659–665
Language:
URL:
https://aclanthology.org/P17-2104
DOI:
10.18653/v1/P17-2104
Bibkey:
Cite (ACL):
Mohammed Hasanuzzaman, Sabyasachi Kamila, Mandeep Kaur, Sriparna Saha, and Asif Ekbal. 2017. Temporal Orientation of Tweets for Predicting Income of Users. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 659–665, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Temporal Orientation of Tweets for Predicting Income of Users (Hasanuzzaman et al., ACL 2017)
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PDF:
https://preview.aclanthology.org/auto-file-uploads/P17-2104.pdf