Evidential estimation of event locations in microblogs using the Dempster-Shafer theory

We estimate locations of events detected in Twitter using Dempster-Shafer theory.Our method combines evidence from multiple tweet features using combination rules.Our method is applicable to any event type and does not require training.Comparisons were made with the Bayesian methods under different settings.Estimations are made for two levels of location granularity with enhanced accuracy. Detecting real-world events by following posts in microblogs has been the motivation of numerous recent studies. In this work, we focus on the spatio-temporal characteristics of events detected in microblogs, and propose a method to estimate their locations using the Dempster-Shafer theory. We utilize three basic location-related features of the posts, namely the latitude-longitude metadata provided by the GPS sensor of the user's device, the textual content of the post, and the location attribute in the user profile, as three independent sources of evidence. Considering this evidence in a complementary way, we apply combination rules in the Dempster-Shafer theory to fuse them into a single model, and estimate the whereabouts of a detected event. Locations are treated at two levels of granularity, namely, city and town. Using the Dempster-Shafer theory to solve this problem allows uncertainty and missing data to be tolerated, and estimations to be made for sets of locations in terms of upper and lower probabilities. We demonstrate our solution using public tweets on Twitter posted in Turkey. The experimental evaluations conducted on a wide range of events including earthquakes, sports, weather, and street protests indicate higher success rates than the existing state of the art methods.