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    <titleInfo>
        <title>Changes in Tweet Geolocation over Time: A Study with Carmen 2.0</title>
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    <name type="personal">
        <namePart type="given">Jingyu</namePart>
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    <abstract>Researchers across disciplines use Twitter geolocation tools to filter data for desired locations. These tools have largely been trained and tested on English tweets, often originating in the United States from almost a decade ago. Despite the importance of these tools for data curation, the impact of tweet language, country of origin, and creation date on tool performance remains largely unknown. We explore these issues with Carmen, a popular tool for Twitter geolocation. To support this study we introduce Carmen 2.0, a major update which includes the incorporation of GeoNames, a gazetteer that provides much broader coverage of locations. We evaluate using two new Twitter datasets, one for multilingual, multiyear geolocation evaluation, and another for usage trends over time. We found that language, country origin, and time does impact geolocation tool performance.</abstract>
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%0 Conference Proceedings
%T Changes in Tweet Geolocation over Time: A Study with Carmen 2.0
%A Zhang, Jingyu
%A DeLucia, Alexandra
%A Dredze, Mark
%S Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F zhang-etal-2022-changes
%X Researchers across disciplines use Twitter geolocation tools to filter data for desired locations. These tools have largely been trained and tested on English tweets, often originating in the United States from almost a decade ago. Despite the importance of these tools for data curation, the impact of tweet language, country of origin, and creation date on tool performance remains largely unknown. We explore these issues with Carmen, a popular tool for Twitter geolocation. To support this study we introduce Carmen 2.0, a major update which includes the incorporation of GeoNames, a gazetteer that provides much broader coverage of locations. We evaluate using two new Twitter datasets, one for multilingual, multiyear geolocation evaluation, and another for usage trends over time. We found that language, country origin, and time does impact geolocation tool performance.
%U https://aclanthology.org/2022.wnut-1.1
%P 1-14

Markdown (Informal)

[Changes in Tweet Geolocation over Time: A Study with Carmen 2.0](https://aclanthology.org/2022.wnut-1.1) (Zhang et al., WNUT 2022)

• Changes in Tweet Geolocation over Time: A Study with Carmen 2.0 (Zhang et al., WNUT 2022)

ACL

• Jingyu Zhang, Alexandra DeLucia, and Mark Dredze. 2022. Changes in Tweet Geolocation over Time: A Study with Carmen 2.0. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), pages 1–14, Gyeongju, Republic of Korea. Association for Computational Linguistics.