Twitter Geolocation: A Hybrid Approach

被引:27
作者
Bakerman, Jordan [1 ]
Pazdernik, Karl [1 ]
Wilson, Alyson [1 ]
Fairchild, Geoffrey [2 ]
Bahran, Rian [3 ]
机构
[1] North Carolina State Univ, Stat Dept, Raleigh, NC 27695 USA
[2] Los Alamos Natl Lab, Analyt Intelligence & Technol Div, Los Alamos, NM 87545 USA
[3] Los Alamos Natl Lab, Nucl Engn & Nonproliferat Div, Los Alamos, NM 87545 USA
基金
美国国家科学基金会;
关键词
Twitter; Geotag; Gaussian mixture model; simple accuracy error; comprehensive accmacy error; prediction region area; MESSAGES; LOCATION;
D O I
10.1145/3178112
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Geotagging Twitter messages is an important tool for event detection and enrichment. Despite the availability of both social media content and user network information these two features are generally utilized separately in the methodology. In this is article, we create a hybrid method that uses Twitter content and network information jointly as model features. We use Gaussian mixture models to map the raw spatial distribution of the model features to a predicted field. This approach is scalable to large datasets and provides a natural representation of model confidence. Our method is tested against other approaches and we achieve greater prediction accuracy. The model also improves both precision and coverage.
引用
收藏
页数:17
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