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

Ozdikis O., Ogurtuzun H., Karagöz P.

Information Processing and Management, vol.52, no.6, pp.1227-1246, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 52 Issue: 6
  • Publication Date: 2016
  • Doi Number: 10.1016/j.ipm.2016.06.001
  • Journal Name: Information Processing and Management
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.1227-1246
  • Keywords: location estimation, Microblogs, Event location, Dempster-Shafer theory, Evidential reasoning, COMBINATION, FRAMEWORK, TWITTER
  • Middle East Technical University Affiliated: Yes


© 2016 Elsevier LtdDetecting 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.