# RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation

arXiv: https://arxiv.org/abs/2111.06515 · HTML: https://mlsanity.com/paper/2111.06515/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 12 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [Towards Real-Time, Country-Level Location Classification of Worldwide Tweets](https://mlsanity.com/paper/1604.07236/) (2016)

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

- [Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification](https://mlsanity.com/paper/2202.05932/) (2022)

## Abstract (arXiv)

Real-time location inference of social media users is the fundamental of some spatial applications such as localized search and event detection. While tweet text is the most commonly used feature in location estimation, most of the prior works suffer from either the noise or the sparsity of textual features. In this paper, we aim to tackle these two problems. We use topic modeling as a building block to characterize the geographic topic variation and lexical variation so that "one-hot" encoding vectors will no longer be directly used. We also incorporate other features which can be extracted through the Twitter streaming API to overcome the noise problem. Experimental results show that our RATE algorithm outperforms several benchmark methods, both in the precision of region classification and the mean distance error of latitude and longitude regression.