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    <titleInfo>
        <title>Temporal Orientation of Tweets for Predicting Income of Users</title>
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    <name type="personal">
        <namePart type="given">Mohammed</namePart>
        <namePart type="family">Hasanuzzaman</namePart>
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        <namePart type="given">Sabyasachi</namePart>
        <namePart type="family">Kamila</namePart>
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    <name type="personal">
        <namePart type="given">Mandeep</namePart>
        <namePart type="family">Kaur</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Sriparna</namePart>
        <namePart type="family">Saha</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    </name>
    <name type="personal">
        <namePart type="given">Asif</namePart>
        <namePart type="family">Ekbal</namePart>
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        <dateIssued>2017-07</dateIssued>
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            <namePart type="family">Barzilay</namePart>
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        <name type="personal">
            <namePart type="given">Min-Yen</namePart>
            <namePart type="family">Kan</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/P17-2104</identifier>
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        <date>2017-07</date>
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            <start>659</start>
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%0 Conference Proceedings
%T Temporal Orientation of Tweets for Predicting Income of Users
%A Hasanuzzaman, Mohammed
%A Kamila, Sabyasachi
%A Kaur, Mandeep
%A Saha, Sriparna
%A Ekbal, Asif
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F hasanuzzaman-etal-2017-temporal
%X 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.
%R 10.18653/v1/P17-2104
%U https://aclanthology.org/P17-2104
%U https://doi.org/10.18653/v1/P17-2104
%P 659-665

Markdown (Informal)

[Temporal Orientation of Tweets for Predicting Income of Users](https://aclanthology.org/P17-2104) (Hasanuzzaman et al., ACL 2017)

• Temporal Orientation of Tweets for Predicting Income of Users (Hasanuzzaman et al., ACL 2017)

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.