A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

2019/10/28 by Huang, Binxuan, Carley, Kathleen M.
#Computation and Language (cs.CL) #FOS: Computer and information sciences #Social and Information Networks (cs.SI)

paper · doi:10.48550/arxiv.1910.12941

Abstract

Accurate estimation of user location is important for many online services. Previous neural network based methods largely ignore the hierarchical structure among locations. In this paper, we propose a hierarchical location prediction neural network for Twitter user geolocation. Our model first predicts the home country for a user, then uses the country result to guide the city-level prediction. In addition, we employ a character-aware word embedding layer to overcome the noisy information in tweets. With the feature fusion layer, our model can accommodate various feature combinations and achieves state-of-the-art results over three commonly used benchmarks under different feature settings. It not only improves the prediction accuracy but also greatly reduces the mean error distance.

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