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        <title>Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features</title>
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        <namePart type="given">Lianhua</namePart>
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        <namePart type="given">Hui</namePart>
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    <abstract>Knowing the location of a social media user and their posts is important for various purposes, such as the recommendation of location-based items/services, and locality detection of crisis/disasters. This paper describes our submission to the shared task “Geolocation Prediction in Twitter” of the 2nd Workshop on Noisy User-generated Text. In this shared task, we propose an algorithm to predict the location of Twitter users and tweets using a multinomial Naive Bayes classifier trained on Location Indicative Words and various textual features (such as city/country names, #hashtags and @mentions). We compared our approach against various baselines based on Location Indicative Words, city/country names, #hashtags and @mentions as individual feature sets, and experimental results show that our approach outperforms these baselines in terms of classification accuracy, mean and median error distance.</abstract>
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%0 Conference Proceedings
%T Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features
%A Chi, Lianhua
%A Lim, Kwan Hui
%A Alam, Nebula
%A Butler, Christopher J.
%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 chi-etal-2016-geolocation
%X Knowing the location of a social media user and their posts is important for various purposes, such as the recommendation of location-based items/services, and locality detection of crisis/disasters. This paper describes our submission to the shared task “Geolocation Prediction in Twitter” of the 2nd Workshop on Noisy User-generated Text. In this shared task, we propose an algorithm to predict the location of Twitter users and tweets using a multinomial Naive Bayes classifier trained on Location Indicative Words and various textual features (such as city/country names, #hashtags and @mentions). We compared our approach against various baselines based on Location Indicative Words, city/country names, #hashtags and @mentions as individual feature sets, and experimental results show that our approach outperforms these baselines in terms of classification accuracy, mean and median error distance.
%U https://aclanthology.org/W16-3930
%P 227-234

Markdown (Informal)

[Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features](https://aclanthology.org/W16-3930) (Chi et al., WNUT 2016)

• Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features (Chi et al., WNUT 2016)

ACL

• Lianhua Chi, Kwan Hui Lim, Nebula Alam, and Christopher J. Butler. 2016. Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 227–234, Osaka, Japan. The COLING 2016 Organizing Committee.