# Twitter User Geolocation using Deep Multiview Learning

arXiv: https://arxiv.org/abs/1805.04612 · HTML: https://mlsanity.com/paper/1805.04612/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2018.

## Built on by (most cited first, 5 of 5)

- [Predicting the Geolocation of Tweets Using transformer models on Customized Data](https://mlsanity.com/paper/2303.07865/) (2023)
- [Georeferencing complex relative locality descriptions with large language models](https://mlsanity.com/paper/2512.14228/) (2025)
- [FewUser: Few-Shot Social User Geolocation via Contrastive Learning](https://mlsanity.com/paper/2404.08662/) (2024)
- [Do We Trust What They Say or What They Do? A Multimodal User Embedding Provides Personalized Explanations](https://mlsanity.com/paper/2409.02965/) (2024)
- [ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation](https://mlsanity.com/paper/2609.00057/) (2026)

## Abstract (arXiv)

Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows either a content-based or a network-based approach. The former is based on user-generated content while the latter exploits the structure of the network of users. In this paper, we propose a more generic approach, which incorporates not only both content-based and network-based features, but also other available information into a unified model. Our approach, named Multi-Entry Neural Network (MENET), leverages the latest advances in deep learning and multiview learning. A realization of MENET with textual, network and metadata features results in an effective method for Twitter user geolocation, achieving the state of the art on two well-known datasets.