# A survey of location inference techniques on Twitter

arXiv: https://arxiv.org/abs/1701.03639 · HTML: https://mlsanity.com/paper/1701.03639/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2017.

## Built on by (most cited first, 6 of 6)

- [TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels](https://mlsanity.com/paper/2110.03664/) (2021)
- [OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants](https://mlsanity.com/paper/2406.14883/) (2024)
- [Robust language-based mental health assessments in time and space through social media](https://mlsanity.com/paper/2302.12952/) (2023)
- [Land use identification through social network interaction](https://mlsanity.com/paper/2112.06704/) (2021)
- [Geo-Semantic-Parsing: AI-powered geoparsing by traversing semantic knowledge graphs](https://mlsanity.com/paper/2503.01386/) (2025)
- [GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks](https://mlsanity.com/paper/2606.08303/) (2026)

## Abstract (arXiv)

The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or author's location remains a challenge thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state-of-the-art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features.