Public health applications using social media often require accurate, broad-coverage location information. However, the standard information provided by social media APIs, such as Twitter, cover a limited number of messages. This paper presents Carmen, a geolocation system that can determine structured location information for messages provided by the Twitter API. Our system utilizes geocoding tools and a combination of automatic and manual alias resolution methods to infer location structures from GPS positions and user-provided profile data. We show that our system is accurate and covers many locations, and we demonstrate its utility for improving influenza surveillance.
Description
Carmen: A Twitter Geolocation System with Applications to Public Health
%0 Generic
%1 Dredze_carmen:a
%A Dredze, Mark
%A Paul, Michael J.
%A Bergsma, Shane
%A Tran, Hieu
%D 2013
%K carmen k3 location twitter
%T Carmen: A Twitter Geolocation System with Applications to Public Health
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.6126
%X Public health applications using social media often require accurate, broad-coverage location information. However, the standard information provided by social media APIs, such as Twitter, cover a limited number of messages. This paper presents Carmen, a geolocation system that can determine structured location information for messages provided by the Twitter API. Our system utilizes geocoding tools and a combination of automatic and manual alias resolution methods to infer location structures from GPS positions and user-provided profile data. We show that our system is accurate and covers many locations, and we demonstrate its utility for improving influenza surveillance.
@misc{Dredze_carmen:a,
abstract = {Public health applications using social media often require accurate, broad-coverage location information. However, the standard information provided by social media APIs, such as Twitter, cover a limited number of messages. This paper presents Carmen, a geolocation system that can determine structured location information for messages provided by the Twitter API. Our system utilizes geocoding tools and a combination of automatic and manual alias resolution methods to infer location structures from GPS positions and user-provided profile data. We show that our system is accurate and covers many locations, and we demonstrate its utility for improving influenza surveillance.},
added-at = {2016-02-09T10:39:28.000+0100},
author = {Dredze, Mark and Paul, Michael J. and Bergsma, Shane and Tran, Hieu},
biburl = {https://www.bibsonomy.org/bibtex/2fd0762e2502f7bca179d8081dca329ee/asmelash},
description = {Carmen: A Twitter Geolocation System with Applications to Public Health},
interhash = {1cb7bfe3de32f2b6d7e3b769eda81b18},
intrahash = {fd0762e2502f7bca179d8081dca329ee},
keywords = {carmen k3 location twitter},
timestamp = {2016-02-09T10:39:28.000+0100},
title = {Carmen: A Twitter Geolocation System with Applications to Public Health},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.6126},
year = 2013
}