A survey of location inference techniques on Twitter

TL;DR

This survey reviews various techniques for inferring Twitter user locations, highlighting advancements in accuracy and granularity through algorithm improvements and spatial features over time.

Contribution

It provides a comprehensive overview of location inference methods on Twitter, emphasizing recent progress and challenges in the field.

Findings

Significant improvements in location accuracy over time

Enhanced granularity levels achieved with refined algorithms

Inclusion of diverse spatial features boosts inference performance

Abstract

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.