# A Survey of Location Prediction on Twitter

arXiv: https://arxiv.org/abs/1705.03172 · HTML: https://mlsanity.com/paper/1705.03172/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2017.

## Built on by (most cited first, 17 of 17)

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- [Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility](https://mlsanity.com/paper/2211.14885/) (2022)
- [Multi-modal Representation Learning for Social Post Location Inference](https://mlsanity.com/paper/2306.07935/) (2023)
- [FewUser: Few-Shot Social User Geolocation via Contrastive Learning](https://mlsanity.com/paper/2404.08662/) (2024)
- [Embedding Privacy in Computational Social Science and Artificial Intelligence Research](https://mlsanity.com/paper/2404.11515/) (2024)
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## Abstract (arXiv)

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.