# On Predicting Geolocation of Tweets using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1704.05146 · HTML: https://mlsanity.com/paper/1704.05146/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2017.

## Built on by (most cited first, 2 of 2)

- [A Transformer-based Framework for POI-level Social Post Geolocation](https://mlsanity.com/paper/2211.01336/) (2022)
- [Deriving Disinformation Insights from Geolocalized Twitter Callouts](https://mlsanity.com/paper/2108.03067/) (2021)

## Abstract (arXiv)

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.