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.

用户位置数据对于各种社交媒体分析非常有价值。在本文中，我们仅通过历史推文来解决估算全球城市级Twitter用户位置这一艰巨任务。我们提出了一种纯无监督的方法，该方法基于对Google趋势（GT）鸣叫名词的城市级频率的综合地理采样和三种聚类算法。该方法已通过使用最近收集的数据集进行了经验验证，该数据集具有3268个全球Twitter用户在城市级别的城市位置，与最新的Word Distribution（WD）用户位置估计方法相比，可获得竞争性结果。GT名词DBSCAN（GTN-DB）方法获得了最佳的总体结果，该方法计算速度快，可以正确预测地面真实位置的15％，23％，