# Confounds and Consequences in Geotagged Twitter Data

arXiv: https://arxiv.org/abs/1506.02275 · HTML: https://mlsanity.com/paper/1506.02275/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2015.

## Built on by (most cited first, 8 of 8)

- [LMSOC: An Approach for Socially Sensitive Pretraining](https://mlsanity.com/paper/2110.10319/) (2021)
- [Pre-Trained Language Models Represent Some Geographic Populations Better Than Others](https://mlsanity.com/paper/2403.11025/) (2024)
- [Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input](https://mlsanity.com/paper/2404.18784/) (2024)
- [Capturing the diversity of multilingual societies](https://mlsanity.com/paper/2105.02570/) (2021)
- [American cultural regions mapped through the lexical analysis of social media](https://mlsanity.com/paper/2208.07649/) (2022)
- [When Dialects Collide: How Socioeconomic Mixing Affects Language Use](https://mlsanity.com/paper/2307.10016/) (2023)
- [The Human Flourishing Geographic Index: A County-Level Dataset for the United States, 2013--2023](https://mlsanity.com/paper/2511.03915/) (2025)
- [Does Geo-co-location Matter? A Case Study of Public Health Conversations during COVID-19](https://mlsanity.com/paper/2405.17710/) (2024)

## Abstract (arXiv)

Twitter is often used in quantitative studies that identify geographically-preferred topics, writing styles, and entities. These studies rely on either GPS coordinates attached to individual messages, or on the user-supplied location field in each profile. In this paper, we compare these data acquisition techniques and quantify the biases that they introduce; we also measure their effects on linguistic analysis and text-based geolocation. GPS-tagging and self-reported locations yield measurably different corpora, and these linguistic differences are partially attributable to differences in dataset composition by age and gender. Using a latent variable model to induce age and gender, we show how these demographic variables interact with geography to affect language use. We also show that the accuracy of text-based geolocation varies with population demographics, giving the best results for men above the age of 40.