Homing in on Twitter users: Evaluating an Enhanced Geoparser for User Profile Locations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Twitter-related studies often need to geo-locate Tweets or Twitter users, identifying their real-world geographic locations. As tweet-level geotagging remains rare, most prior work exploited tweet content, timezone and network information to inform geolocation, or else relied on off-the-shelf tools to geolocate users from location information in their user profiles. However, such user location metadata is not consistently structured, causing such tools to fail regularly, especially if a string contains multiple locations, or if locations are very fine-grained. We argue that user profile location (UPL) and tweet location need to be treated as distinct types of information from which differing inferences can be drawn. Here, we apply geoparsing to UPLs, and demonstrate how task performance can be improved by adapting our Edinburgh Geoparser, which was originally developed for processing English text. We present a detailed evaluation method and results, including inter-coder agreement. We demonstrate that the optimised geoparser can effectively extract and geo-reference multiple locations at different levels of granularity with an F1-score of around 0.90. We also illustrate how geoparsed UPLs can be exploited for international information trade studies and country-level sentiment analysis.
Original languageEnglish
Title of host publicationProceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)
PublisherEuropean Language Resources Association (ELRA)
Number of pages9
ISBN (Print)978-2-9517408-9-1
Publication statusPublished - May 2016
Event10th edition of the Language Resources and Evaluation Conference - Portorož , Slovenia
Duration: 23 May 201628 May 2016


Conference10th edition of the Language Resources and Evaluation Conference
Abbreviated titleLREC 2016
Internet address


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