# Twitter User Geolocation Using a Unified Text and Network Prediction Model

arXiv: https://arxiv.org/abs/1506.08259 · HTML: https://mlsanity.com/paper/1506.08259/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2015.

## Built on by (most cited first, 4 of 4)

- [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)
- [Designing weighted and multiplex networks for deep learning user geolocation in Twitter](https://mlsanity.com/paper/2112.06999/) (2021)
- [ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation](https://mlsanity.com/paper/2609.00057/) (2026)

## Abstract (arXiv)

We propose a label propagation approach to geolocation prediction based on Modified Adsorption, with two enhancements:(1) the removal of "celebrity" nodes to increase location homophily and boost tractability, and (2) he incorporation of text-based geolocation priors for test users. Experiments over three Twitter benchmark datasets achieve state-of-the-art results, and demonstrate the effectiveness of the enhancements.