Work

Evidential location estimation for events detected in Twitter

Year: 2013

Type: conference-paper

Abstract: 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 tweets, we aim to produce belief intervals for a set of possible discrete locations. DST helps us deal with uncertainties, assign belief values to subsets of solutions, and combine pieces of evidence obtained from different tweet features. The initial results on several real cases suggest the applicability and usefulness of DST for the problem.

Source: Workshop on Geographic Information Retrieval (GIR)

DOI: 10.1145/2533888.2533929

Authors: Ozer Ozdikis, Halit Oguztuzun, Pinar Karagoz

Institution: Middle East Technical University

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

Funder: Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

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