Year: 2016
Type: conference-paper
Abstract: Automated geolocation of social media messages can benefit a variety of downstream applications.However, these geolocation systems are typically evaluated without attention to how changes in time impact geolocation.Since different people, in different locations write messages at different times, these factors can significantly vary the performance of a geolocation system over time.We demonstrate cyclical temporal effects on geolocation accuracy in Twitter, as well as rapid drops as test data moves beyond the time period of training data.We show that temporal drift can effectively be countered with even modest online model updates.
DOI: 10.18653/v1/n16-1122
Institutions: Bloomberg (United States), Johns Hopkins University
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W2467321947