On Predicting Geolocation of Tweets Using Convolutional Neural Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)


In many Twitter studies, it is important to know where a tweet came from in order to use the tweet content to study regional user behavior. However, researchers using Twitter to understand user behavior often lack sufficient geo-tagged data. Given the huge volume of Twitter data there is a need for accurate automated geolocating solutions. Herein, we present a new method to predict a Twitter user’s location based on the information in a single tweet. We integrate text and user profile meta-data into a single model using a convolutional neural network. Our experiments demonstrate that our neural model substantially outperforms baseline methods, achieving 52.8% accuracy and 92.1% accuracy on city-level and country-level prediction respectively.


Convolutional Neural Network Location Prediction Output Probability Twitter User Text Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by the Office of Naval Research (ONR) N000140811186, and the National Science Foundation (NSF) 00361150115291. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ONR or the NSF. We want to thank tutors in the Global Communication Center at Carnegie Mellon for their valuable advice.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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