A survey of location inference techniques on Twitter

Ajao, Oluwaseun; Hong, Jun; Liu, Weiru

Abstract

© The Author(s) 2015. 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 as 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 an 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.

• Journal Article Type: Review
• Acceptance Date: Nov 20, 2015
• Publication Date: Jan 1, 2015
• Deposit Date: Feb 15, 2017
• Journal: Journal of Information Science
• Print ISSN: 0165-5515
• Electronic ISSN: 1741-6485
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 41
• Issue: 6
• Pages: 855-864
• DOI: https://doi.org/10.1177/0165551515602847
• Keywords: location inference, Twitter analytics, information retrieval
• Public URL: https://uwe-repository.worktribe.com/output/802967
• Publisher URL: http://dx.doi.org/10.1177/0165551515602847
• Contract Date: Feb 15, 2017