Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/66815
Título: | A Google trends spatial clustering approach for a worldwide Twitter user geolocation |
Autor(es): | Zola, Paola Ragno, Costantino Cortez, Paulo |
Palavras-chave: | City-level geolocation Clustering Google Trends Natural language processing |
Data: | 2020 |
Editora: | Elsevier 1 |
Revista: | Information Processing and Management |
Resumo(s): | User location data is valuable for diverse social media analytics. In this paper, we address the non-trivial task of estimating a worldwide city-level Twitter user location considering only historical tweets. We propose a purely unsupervised approach that is based on a synthetic geographic sampling of Google Trends (GT) city-level frequencies of tweet nouns and three clustering algorithms. The approach was validated empirically by using a recently collected dataset, with 3,268 worldwide city-level locations of Twitter users, obtaining competitive results when compared with a state-of-the-art Word Distribution (WD) user location estimation method. The best overall results were achieved by the GT noun DBSCAN (GTN-DB) method, which is computationally fast, and correctly predicts the ground truth locations of 15%, 23%, 39% and 58% of the users for tolerance distances of 250 km, 500 km, 1,000 km and 2,000 km. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/66815 |
DOI: | 10.1016/j.ipm.2020.102312 |
ISSN: | 0306-4573 |
Versão da editora: | https://www.sciencedirect.com/science/article/pii/S0306457320308074 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |