Multi-stage Collaborative filtering for Tweet Geolocation

Data from social media platforms such as Twitter can be used to analyze severe weather reports and foodborne illness outbreaks. Government officials use online reports for early estimation of the impact of catastrophes and to aid resource distribution. For online reports to be useful they must be geotagged, but location is often not available. Less then one percent of users share their location information and/or acquisition of significant sample of geolocation messages is prohibitively expensive. In this paper, we propose a multi-stage iterative model based on the popular matrix factorization technique. This algorithm uses the partial information and exploits the relationship of messages, location, and keywords to recommend locations for non-geotagged messages. We present this model for geotagging messages using recommender systems and discussion the potential applications and next steps in this work.