# Predicting the Geolocation of Tweets Using transformer models on Customized Data

arXiv: https://arxiv.org/abs/2303.07865 · HTML: https://mlsanity.com/paper/2303.07865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 57 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [A Survey of Location Prediction on Twitter](https://mlsanity.com/paper/1705.03172/) (2017)
- [A Transformer-based Framework for POI-level Social Post Geolocation](https://mlsanity.com/paper/2211.01336/) (2022)
- [A Neural Model for User Geolocation and Lexical Dialectology](https://mlsanity.com/paper/1704.04008/) (2017)
- [A Hierarchical Location Prediction Neural Network for Twitter User Geolocation](https://mlsanity.com/paper/1910.12941/) (2019)
- [Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks](https://mlsanity.com/paper/1708.04358/) (2017)
- [Exploiting Text and Network Context for Geolocation of Social Media Users](https://mlsanity.com/paper/1506.04803/) (2015)
- [Twitter User Geolocation using Deep Multiview Learning](https://mlsanity.com/paper/1805.04612/) (2018)
- [Point-of-Interest Type Inference from Social Media Text](https://mlsanity.com/paper/2009.14734/) (2020)
- [Inferring the Origin Locations of Tweets with Quantitative Confidence](https://mlsanity.com/paper/1305.3932/) (2013)
- [Home Location Identification of Twitter Users](https://mlsanity.com/paper/1403.2345/) (2014)
- [Density Estimation for Geolocation via Convolutional Mixture Density Network](https://mlsanity.com/paper/1705.02750/) (2017)

## Built on by (most cited first, 1 of 1)

- [GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks](https://mlsanity.com/paper/2606.08303/) (2026)

## Abstract (arXiv)

This research is aimed to solve the tweet/user geolocation prediction task and provide a flexible methodology for the geotagging of textual big data. The suggested approach implements neural networks for natural language processing (NLP) to estimate the location as coordinate pairs (longitude, latitude) and two-dimensional Gaussian Mixture Models (GMMs). The scope of proposed models has been finetuned on a Twitter dataset using pretrained Bidirectional Encoder Representations from Transformers (BERT) as base models. Performance metrics show a median error of fewer than 30 km on a worldwide-level, and fewer than 15 km on the US-level datasets for the models trained and evaluated on text features of tweets' content and metadata context. Our source code and data are available at https://github.com/K4TEL/geo-twitter.git