# Where in the World are You? Geolocation and Language Identification in Twitter

arXiv: https://arxiv.org/abs/1308.0683 · HTML: https://mlsanity.com/paper/1308.0683/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2013.

## Built on by (most cited first, 11 of 11)

- [From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets](https://mlsanity.com/paper/2404.17874/) (2024)
- [Implicit Personalization in Language Models: A Systematic Study](https://mlsanity.com/paper/2405.14808/) (2024)
- [HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter](https://mlsanity.com/paper/2411.15462/) (2024)
- [Global Syntactic Variation in Seven Languages: Towards a Computational Dialectology](https://mlsanity.com/paper/2104.01306/) (2021)
- [Representations of Language Varieties Are Reliable Given Corpus Similarity Measures](https://mlsanity.com/paper/2104.01294/) (2021)
- [Leave no Place Behind: Improved Geolocation in Humanitarian Documents](https://mlsanity.com/paper/2309.02914/) (2023)
- [Regionalized models for Spanish language variations based on Twitter](https://mlsanity.com/paper/2110.06128/) (2021)
- [WikiGap: Promoting Epistemic Equity by Surfacing Knowledge Gaps Between English Wikipedia and other Language Editions](https://mlsanity.com/paper/2505.24195/) (2025)
- [Bridging Nations: Quantifying the Role of Multilinguals in Communication on Social Media](https://mlsanity.com/paper/2304.03797/) (2023)
- [Measuring Linguistic Diversity During COVID-19](https://mlsanity.com/paper/2104.01290/) (2021)
- [The Impact of Twitter Sentiments on Stock Market Trends](https://mlsanity.com/paper/2302.07244/) (2023)

## Abstract (arXiv)

The movements of ideas and content between locations and languages are unquestionably crucial concerns to researchers of the information age, and Twitter has emerged as a central, global platform on which hundreds of millions of people share knowledge and information. A variety of research has attempted to harvest locational and linguistic metadata from tweets in order to understand important questions related to the 300 million tweets that flow through the platform each day. However, much of this work is carried out with only limited understandings of how best to work with the spatial and linguistic contexts in which the information was produced. Furthermore, standard, well-accepted practices have yet to emerge. As such, this paper studies the reliability of key methods used to determine language and location of content in Twitter. It compares three automated language identification packages to Twitter's user interface language setting and to a human coding of languages in order to identify common sources of disagreement. The paper also demonstrates that in many cases user-entered profile locations differ from the physical locations users are actually tweeting from. As such, these open-ended, user-generated, profile locations cannot be used as useful proxies for the physical locations from which information is published to Twitter.