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        <title>A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter</title>
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    <name type="personal">
        <namePart type="given">Yasuhide</namePart>
        <namePart type="family">Miura</namePart>
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    <name type="personal">
        <namePart type="given">Motoki</namePart>
        <namePart type="family">Taniguchi</namePart>
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    <name type="personal">
        <namePart type="given">Tomoki</namePart>
        <namePart type="family">Taniguchi</namePart>
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    <name type="personal">
        <namePart type="given">Tomoko</namePart>
        <namePart type="family">Ohkuma</namePart>
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            <title>Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)</title>
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            <namePart type="given">Bo</namePart>
            <namePart type="family">Han</namePart>
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            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Wei</namePart>
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            <namePart type="given">Tim</namePart>
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            <publisher>The COLING 2016 Organizing Committee</publisher>
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    <abstract>This paper describes a model that we submitted to W-NUT 2016 Shared task #1: Geolocation Prediction in Twitter. Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes. From the findings of previous geolocation prediction approaches, we integrated various user metadata along with message texts and trained the model with them. In the test run of the task, the model achieved the accuracy of 40.91% and the median distance error of 69.50 km in message-level prediction and the accuracy of 47.55% and the median distance error of 16.13 km in user-level prediction. These results are moderate performances in terms of accuracy and best performances in terms of distance. The results show a promising extension of neural networks based models for geolocation prediction where recent advances in neural networks can be added to enhance our current simple model.</abstract>
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%0 Conference Proceedings
%T A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter
%A Miura, Yasuhide
%A Taniguchi, Motoki
%A Taniguchi, Tomoki
%A Ohkuma, Tomoko
%Y Han, Bo
%Y Ritter, Alan
%Y Derczynski, Leon
%Y Xu, Wei
%Y Baldwin, Tim
%S Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F miura-etal-2016-simple
%X This paper describes a model that we submitted to W-NUT 2016 Shared task #1: Geolocation Prediction in Twitter. Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes. From the findings of previous geolocation prediction approaches, we integrated various user metadata along with message texts and trained the model with them. In the test run of the task, the model achieved the accuracy of 40.91% and the median distance error of 69.50 km in message-level prediction and the accuracy of 47.55% and the median distance error of 16.13 km in user-level prediction. These results are moderate performances in terms of accuracy and best performances in terms of distance. The results show a promising extension of neural networks based models for geolocation prediction where recent advances in neural networks can be added to enhance our current simple model.
%U https://aclanthology.org/W16-3931
%P 235-239

Markdown (Informal)

[A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter](https://aclanthology.org/W16-3931) (Miura et al., WNUT 2016)

• A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter (Miura et al., WNUT 2016)

ACL

• Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi, and Tomoko Ohkuma. 2016. A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 235–239, Osaka, Japan. The COLING 2016 Organizing Committee.