Work

Twitter Geolocation and Regional Classification via Sparse Coding

Year: 2021

Type: conference-paper

Abstract: We present a data-driven approach for Twitter geolocation and regional classification. Our method is based on sparse coding and dictionary learning, an unsupervised method popular in computer vision and pattern recognition. Through a series of optimization steps that integrate information from both feature and raw spaces, and enhancements such as PCA whitening, feature augmentation, and voting-based grid selection, we lower geolocation errors and improve classification accuracy from previously known results on the GEOTEXT dataset.

Source: Proceedings of the International AAAI Conference on Web and Social Media

DOI: 10.1609/icwsm.v9i1.14664

Authors: Miriam Cha, Youngjune Gwon, H. Kung

Institution: Harvard University

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

Open Access status: diamond

Funders: National Science Foundation, Intel Corporation

Award: Graduate Research Fellowship Program (GRFP)