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    <titleInfo>
        <title>A Unified Neural Network Model for Geolocating Twitter Users</title>
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    <name type="personal">
        <namePart type="given">Mohammad</namePart>
        <namePart type="family">Ebrahimi</namePart>
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    <name type="personal">
        <namePart type="given">Elaheh</namePart>
        <namePart type="family">ShafieiBavani</namePart>
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    <name type="personal">
        <namePart type="given">Raymond</namePart>
        <namePart type="family">Wong</namePart>
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    <name type="personal">
        <namePart type="given">Fang</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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            <namePart type="given">Ivan</namePart>
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    <abstract>Locations of social media users are important to many applications such as rapid disaster response, targeted advertisement, and news recommendation. However, many users do not share their exact geographical coordinates due to reasons such as privacy concerns. The lack of explicit location information has motivated a growing body of research in recent years looking at different automatic ways of determining the user’s primary location. In this paper, we propose a unified user geolocation method which relies on a fusion of neural networks. Our joint model incorporates different types of available information including tweet text, user network, and metadata to predict users’ locations. Moreover, we utilize a bidirectional LSTM network augmented with an attention mechanism to identify the most location indicative words in textual content of tweets. The experiments demonstrate that our approach achieves state-of-the-art performance over two Twitter benchmark geolocation datasets. We also conduct an ablation study to evaluate the contribution of each type of information in user geolocation performance.</abstract>
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    <identifier type="doi">10.18653/v1/K18-1005</identifier>
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%0 Conference Proceedings
%T A Unified Neural Network Model for Geolocating Twitter Users
%A Ebrahimi, Mohammad
%A ShafieiBavani, Elaheh
%A Wong, Raymond
%A Chen, Fang
%Y Korhonen, Anna
%Y Titov, Ivan
%S Proceedings of the 22nd Conference on Computational Natural Language Learning
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F ebrahimi-etal-2018-unified
%X Locations of social media users are important to many applications such as rapid disaster response, targeted advertisement, and news recommendation. However, many users do not share their exact geographical coordinates due to reasons such as privacy concerns. The lack of explicit location information has motivated a growing body of research in recent years looking at different automatic ways of determining the user’s primary location. In this paper, we propose a unified user geolocation method which relies on a fusion of neural networks. Our joint model incorporates different types of available information including tweet text, user network, and metadata to predict users’ locations. Moreover, we utilize a bidirectional LSTM network augmented with an attention mechanism to identify the most location indicative words in textual content of tweets. The experiments demonstrate that our approach achieves state-of-the-art performance over two Twitter benchmark geolocation datasets. We also conduct an ablation study to evaluate the contribution of each type of information in user geolocation performance.
%R 10.18653/v1/K18-1005
%U https://aclanthology.org/K18-1005
%U https://doi.org/10.18653/v1/K18-1005
%P 42-53

Markdown (Informal)

[A Unified Neural Network Model for Geolocating Twitter Users](https://aclanthology.org/K18-1005) (Ebrahimi et al., CoNLL 2018)

• A Unified Neural Network Model for Geolocating Twitter Users (Ebrahimi et al., CoNLL 2018)

ACL

• Mohammad Ebrahimi, Elaheh ShafieiBavani, Raymond Wong, and Fang Chen. 2018. A Unified Neural Network Model for Geolocating Twitter Users. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 42–53, Brussels, Belgium. Association for Computational Linguistics.