# Designing weighted and multiplex networks for deep learning user geolocation in Twitter

arXiv: https://arxiv.org/abs/2112.06999 · HTML: https://mlsanity.com/paper/2112.06999/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features](https://mlsanity.com/paper/2111.12128/) (2021)
- [Semi-supervised User Geolocation via Graph Convolutional Networks](https://mlsanity.com/paper/1804.08049/) (2018)
- [A Survey of Location Prediction on Twitter](https://mlsanity.com/paper/1705.03172/) (2017)
- [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)
- [Home Location Identification of Twitter Users](https://mlsanity.com/paper/1403.2345/) (2014)
- [Twitter User Geolocation Using a Unified Text and Network Prediction Model](https://mlsanity.com/paper/1506.08259/) (2015)
- [Evolution of the political opinion landscape during electoral periods](https://mlsanity.com/paper/2011.09538/) (2020)
- [Accurately Modeling Biased Random Walks on Weighted Graphs Using textitNode2vec+](https://mlsanity.com/paper/2109.08031/) (2021)

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

- [GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework](https://mlsanity.com/paper/2112.10767/) (2021)

## Abstract (arXiv)

Predicting the geographical location of users of social media like Twitter has found several applications in health surveillance, emergency monitoring, content personalization, and social studies in general. In this work we contribute to the research in this area by designing and evaluating new methods based on the literature of weighted multigraphs combined with state-of-the-art deep learning techniques. The explored methods depart from a similar underlying structure (that of an extended mention and/or follower network) but use different information processing strategies, e.g., information diffusion through transductive and inductive algorithms -- RGCNs and GraphSAGE, respectively -- and node embeddings with Node2vec+. These graphs are then combined with attention mechanisms to incorporate the users' text view into the models. We assess the performance of each of these methods and compare them to baseline models in the publicly available Twitter-US dataset; we also make a new dataset available based on a large Twitter capture in Latin America. Finally, our work discusses the limitations and validity of the comparisons among methods in the context of different label definitions and metrics.