Paper Info
Title
Homing in on Twitter Users: Evaluating an Enhanced Geoparser for User Profile Locations.
Abstract
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.
Year
Venue
Keywords
2016
LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
Geo-parsing,Twitter,Social Media Analytics
Field
DocType
Citations 
Metadata,World Wide Web,User profile,Computer science,Sentiment analysis,Geolocation,Geoparsing,Trade study,Geotagging
Conference
3
PageRank 
References 
Authors
0.37
14
5
Name
Order
Citations
PageRank
Beatrice Alex123725.59
Clare Llewellyn2377.26
Claire Grover3729100.15
Jon Oberlander478378.55
Richard Tobin514514.83