# A Neural Model for User Geolocation and Lexical Dialectology

arXiv: https://arxiv.org/abs/1704.04008 · HTML: https://mlsanity.com/paper/1704.04008/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2017.

## Built on by (most cited first, 12 of 12)

- [Are Large Language Models Temporally Grounded?](https://mlsanity.com/paper/2311.08398/) (2023)
- [Granular Privacy Control for Geolocation with Vision Language Models](https://mlsanity.com/paper/2407.04952/) (2024)
- [Geographic Adaptation of Pretrained Language Models](https://mlsanity.com/paper/2203.08565/) (2022)
- [Towards Interactive Global Geolocation Assistant](https://mlsanity.com/paper/2412.08907/) (2024)
- [Variation and Instability in Dialect-Based Embedding Spaces](https://mlsanity.com/paper/2303.14963/) (2023)
- [Real-time Spatio-temporal Event Detection on Geotagged Social Media](https://mlsanity.com/paper/2106.13121/) (2021)
- [UnibucKernel: Geolocating Swiss German Jodels Using Ensemble Learning](https://mlsanity.com/paper/2102.09379/) (2021)
- [Designing weighted and multiplex networks for deep learning user geolocation in Twitter](https://mlsanity.com/paper/2112.06999/) (2021)
- [Predicting the Geolocation of Tweets Using transformer models on Customized Data](https://mlsanity.com/paper/2303.07865/) (2023)
- [Syntactic Variation Across the Grammar: Modelling a Complex Adaptive System](https://mlsanity.com/paper/2309.11869/) (2023)
- [FewUser: Few-Shot Social User Geolocation via Contrastive Learning](https://mlsanity.com/paper/2404.08662/) (2024)
- [GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks](https://mlsanity.com/paper/2606.08303/) (2026)

## Abstract (arXiv)

We propose a simple yet effective text- based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods.