A Google trends spatial clustering approach for a worldwide Twitter user geolocation

• Main Author: Zola, Paola
• Publication Date: 2020
• Other Authors: Ragno, Costantino, Cortez, Paulo
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://hdl.handle.net/1822/66815
• topic: City-level geolocation; Clustering; Google Trends; Natural language processing; Twitter; Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática; Science & Technology

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.