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

Ozdikis, Ozer
Ogurtuzun, Halit
Karagöz, Pınar
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.


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Detection of real-world events using messages posted in microblogs has been the motivation of numerous recent studies. In this thesis, we study spatiotemporal data mining techniques to improve situation awareness by detecting events and estimating their locations using the content in microblogs, particularly in Twitter. We present an enhancement to the clustering techniques in the literature by measuring associations between terms in tweets in a temporal context and using these associations in a vector expa...
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Event detection from microblogs and social networks, especially from Twitter, is an active and rich research topic. By grouping similar tweets in clusters, people can extract events and follow the happenings in a community. In this work, we focus on estimating the geographical locations of events that are detected in Twitter. An important novelty of our work is the application of evidential reasoning techniques, namely the Demspter-Shafer Theory (DST), for this problem. By utilizing several features of twee...
Citation Formats
O. Ozdikis, H. Ogurtuzun, and P. Karagöz, “Evidential estimation of event locations in microblogs using the Dempster-Shafer theory,” INFORMATION PROCESSING & MANAGEMENT, pp. 1227–1246, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34707.