Location Prediction for Tweets

Chieh Yang Huang, Hanghang Tong, Jingrui He, Ross Maciejewski

Research output: Contribution to journalArticlepeer-review


Geographic information provides an important insight into many data mining and social media systems. However, users are reluctant to provide such information due to various concerns, such as inconvenience, privacy, etc. In this paper, we aim to develop a deep learning based solution to predict geographic information for tweets. The current approaches bear two major limitations, including (a) hard to model the long term information and (b) hard to explain to the end users what the model learns. To address these issues, our proposed model embraces three key ideas. First, we introduce a multi-head self-attention model for text representation. Second, to further improve the result on informal language, we treat subword as a feature in our model. Lastly, the model is trained jointly with the city and country to incorporate the information coming from different labels. The experiment performed on W-NUT 2016 Geo-tagging shared task shows our proposed model is competitive with the state-of-the-art systems when using accuracy measurement, and in the meanwhile, leading to a better distance measure over the existing approaches.

Original languageEnglish (US)
Article number5
JournalFrontiers in Big Data
StatePublished - May 24 2019
Externally publishedYes


  • data mining
  • deep learning
  • joint training
  • location prediction
  • multi-head self-attention mechanism
  • tweets

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems
  • Artificial Intelligence


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