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Title: Twitter Geolocation: A Hybrid Approach

Journal Article · · ACM Transactions on Knowledge Discovery from Data
DOI:https://doi.org/10.1145/3178112· OSTI ID:1438417
 [1];  [1];  [1];  [2];  [2]
  1. North Carolina State Univ., Raleigh, NC (United States). Statistics Dept.
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Research Organization:
North Carolina State University, Raleigh, NC (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22); National Science Foundation (NSF)
Grant/Contract Number:
NA0002576; DGE-1633587; AC52-06NA25396
OSTI ID:
1438417
Alternate ID(s):
OSTI ID: 1467267
Report Number(s):
LA-UR-24700
Journal Information:
ACM Transactions on Knowledge Discovery from Data, Vol. 12, Issue 3; ISSN 1556-4681
Publisher:
Association for Computing MachineryCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 18 works
Citation information provided by
Web of Science

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Cited By (2)

A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments journal January 2019
A multilayer recognition model for twitter user geolocation journal January 2019