Comparing methods for determining home and work locations from geotagged social media data

Geotagged social media data have emerged as a rich source of insight about spatial dimensions of social phenomena. This methodological article exploits a unique dataset that combines geotagged social media content and home and work locations collected from social media users through a survey to compare three methods of assigning home locations from geotagged social media: majority voting, time frame clustering, and a novel method using activity spaces created from users’ geotagged posts. Using exact match accuracy as the measure, the basic majority voting method achieved better high estimates for both current and previous home location predictions compared to the time frame clustering method. However, for work location prediction, time frame clustering showed better accuracy, and the activity space method contained 25.3% of true home and 44.4% of true work locations. The study found lower precision than others and highlights accuracy trade-offs among each option for assigning home or work locations from geotagged social media.