A multilayer recognition model for twitter user geolocation


Geolocation is important for many emerging applications such as disaster management and recommendation system. In this paper, we propose a multilayer recognition model (MRM) to predict the city-level location for social network users, solely based on the user’s tweet content. Through a series of optimizations such as entity selection, spatial clustering and outlier filtering, suitable features are extracted to model the geographic coordinates of tweet users. Then, the Multinomial Naive Bayes is applied to classify the datasets into different groups. The model is evaluated by comparing with an existing algorithm on twitter datasets. The experimental results reveal that our method achieves a better prediction accuracy of 54.82% on the test set, and the average error is reduced to 400.97 miles at best.

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This work was partially supported by the China National Science and Technology Major Project (2017ZX03001015, 2018ZX03001015, and 2018ZX03001021). Furthermore, this work is done also with the support of the Chinese Academy of Sciences project under Grant No. CXJJ-16M119.

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Correspondence to Haina Tang.

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Tang, H., Zhao, X. & Ren, Y. A multilayer recognition model for twitter user geolocation. Wireless Netw (2019). https://doi.org/10.1007/s11276-018-01897-1

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  • Twitter
  • Geolocation
  • Spatial clustering
  • Text classification