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

User location data is valuable for diverse social media analytics. In this paper, we address the non-trivialtaskofestimatingaworldwidecity-levelTwitteruserlocationconsideringonlyhistorical 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 clusteringalgorithms.Theapproachwasvalidatedempiricallybyusingarecentlycollecteddataset, with 3,268 worldwide city-level locations of Twitter users, obtaining competitive results when comparedwithastate-of-the-artWordDistribution(WD)userlocationestimationmethod.The best overall results were achieved by the GT noun DBSCAN (GTN-DB) method, which is computationallyfast,andcorrectlypredictsthegroundtruthlocationsof15%,23%,39%and58%of theusersfortolerancedistancesof250km,500km,1,000kmand2,000km.

ORCID: https://orcid.org/0000-0001-5956-5389


information processing and management, 2020

Autori esterni: Ragno Costantino (ANIMA Sgr S.p.a., Milan, Italy), Paulo Cortez (ALGORITMI Centre, Department of Information Systems, University of Minho, Guimarães, Portugal)
Autori IIT:

Paola Zola

Foto di Paola Zola

Tipo: Contributo in rivista ISI
Area di disciplina: Information Technology and Communication Systems

File: Zola,AGoogletrendspatialClustering .pdf

Attività: Social Media Analysis