# Home Location Identification of Twitter Users

arXiv: https://arxiv.org/abs/1403.2345 · HTML: https://mlsanity.com/paper/1403.2345/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2014.

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

- [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)
- [Transcending the Attention Paradigm: Representation Learning from Geospatial Social Media Data](https://mlsanity.com/paper/2310.05378/) (2023)
- [Geo-Semantic-Parsing: AI-powered geoparsing by traversing semantic knowledge graphs](https://mlsanity.com/paper/2503.01386/) (2025)

## Abstract (arXiv)

We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone or geographic region, using the content of users tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations and makes use of a geographic gazetteer dictionary to identify place-name entities. We find that a hierarchical classification approach, where time zone, state or geographic region is predicted first and city is predicted next, can improve prediction accuracy. We have also analyzed movement variations of Twitter users, built a classifier to predict whether a user was travelling in a certain period of time and use that to further improve the location detection accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the home location of Twitter users.