Inferring user location from time series of social media activity

Abstract

Combining social media posts with known user locations can lead to unique insights with applications ranging from tracking diffusion of sentiment to earthquake detection. One approach used to determine a user's home location is to examine the timing of their posts, but the precision of existing time-based location predictors is limited to discrimination among time zones. In this thesis, we formulate a general time-based geolocation algorithm that has greater precision, using knowledge of a social media user's real world activities derived from his or her membership in a particular class. Our activity-based model discriminates among locations within a time zone, with city-level accuracy. We also develop methods to solve two related inference tasks. The first method detects when a user travels, allowing us to exclude posts when a user is away from his or her home location. Our other method classifies an account as belonging to a particular user group based on the time series of posts and a known user location. Finally, we test the performance of our geolocation model and related methods using Twitter accounts belonging to Muslims. Using Islamic prayer activity to inform our model, we are able to infer the locations of Muslim accounts. We are also able to accurately determine if an account belongs to a Muslim or non-Muslim using their activity patterns and location. Our work challenges the accepted practices used to protect online privacy by demonstrating that timing of user activity can provide specific location or group membership information.