A survey of location inference techniques on Twitter
Oluwaseun Ajao, Jun Hong, Weiru Liu

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
