# A Survey of Location Prediction on Twitter

## TL;DR

This survey reviews methods for predicting user and tweet locations on Twitter, discussing challenges, input types, and evaluation metrics, and highlights recent approaches, related problems, and future research directions.

## Contribution

It provides a comprehensive overview of location prediction tasks on Twitter, categorizing approaches based on input types and summarizing recent advancements and challenges.

## Key findings

- Summarizes state-of-the-art strategies for location prediction.
- Highlights the importance of network, content, and context inputs.
- Identifies future research directions in the field.

## Abstract

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.