# Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization

arXiv: https://arxiv.org/abs/1404.7152 · HTML: https://mlsanity.com/paper/1404.7152/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2014.

## Built on by (most cited first, 9 of 9)

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- [When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models](https://mlsanity.com/paper/2502.13246/) (2025)
- [Using Sociolinguistic Variables to Reveal Changing Attitudes Towards Sexuality and Gender](https://mlsanity.com/paper/2109.11061/) (2021)
- [Bridging Nations: Quantifying the Role of Multilinguals in Communication on Social Media](https://mlsanity.com/paper/2304.03797/) (2023)
- [Location Aware Modular Biencoder for Tourism Question Answering](https://mlsanity.com/paper/2401.02187/) (2024)
- [Networks and Identity Drive Geographic Properties of the Diffusion of Linguistic Innovation](https://mlsanity.com/paper/2202.04842/) (2022)
- [The Role of Network and Identity in the Diffusion of Hashtags](https://mlsanity.com/paper/2407.12771/) (2024)
- [Deriving Disinformation Insights from Geolocalized Twitter Callouts](https://mlsanity.com/paper/2108.03067/) (2021)
- [Top Gear or Black Mirror: Inferring Political Leaning From Non-Political Content](https://mlsanity.com/paper/2208.05662/) (2022)

## Abstract (arXiv)

Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data. Our method infers an unknown user's location by examining their friend's locations. We frame the geotagging problem as an optimization over a social network with a total variation-based objective and provide a scalable and distributed algorithm for its solution. Furthermore, we show how a robust estimate of the geographic dispersion of each user's ego network can be used as a per-user accuracy measure which is effective at removing outlying errors. Leave-many-out evaluation shows that our method is able to infer location for 101,846,236 Twitter users at a median error of 6.38 km, allowing us to geotag over 80% of public tweets.