# Geocoding Without Geotags: A Text-based Approach for reddit

arXiv: https://arxiv.org/abs/1810.03067 · HTML: https://mlsanity.com/paper/1810.03067/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis](https://mlsanity.com/paper/2104.11612/) (2021)
- [GraphProfiler: Source-Linked Sensitive Attribute Inference via Personal Knowledge Graphs](https://mlsanity.com/paper/2609.12448/) (2026)

## Abstract (arXiv)

In this paper, we introduce the first geolocation inference approach for reddit, a social media platform where user pseudonymity has thus far made supervised demographic inference difficult to implement and validate. In particular, we design a text-based heuristic schema to generate ground truth location labels for reddit users in the absence of explicitly geotagged data. After evaluating the accuracy of our labeling procedure, we train and test several geolocation inference models across our reddit data set and three benchmark Twitter geolocation data sets. Ultimately, we show that geolocation models trained and applied on the same domain substantially outperform models attempting to transfer training data across domains, even more so on reddit where platform-specific interest-group metadata can be used to improve inferences.