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2017 | OriginalPaper | Buchkapitel

F-SED: Feature-Centric Social Event Detection

verfasst von : Elio Mansour, Gilbert Tekli, Philippe Arnould, Richard Chbeir, Yudith Cardinale

Erschienen in: Database and Expert Systems Applications

Verlag: Springer International Publishing

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Abstract

In the context of social media, existent works offer social-event-based organization of multimedia objects (e.g., photos, videos) by mainly considering spatio-temporal data, while neglecting other user-related information (e.g., people, user interests). In this paper we propose an automated, extensible, and incremental Feature-centric Social Event Detection (F-SED) approach, based on Formal Concept Analysis (FCA), to organize shared multimedia objects on social media platforms and sharing applications. F-SED simultaneously considers various event features (e.g., temporal, geographical, social (user related)), and uses the latter to detect different feature-centric events (e.g., user-centric, location-centric). Our experimental results show that detection accuracy is improved when, besides spatio-temporal information, other features, such as social, are considered. We also show that the performance of our prototype is quasi-linear in most cases.

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Titel: F-SED: Feature-Centric Social Event Detection
verfasst von: Elio Mansour
Gilbert Tekli
Philippe Arnould
Richard Chbeir
Yudith Cardinale
Verlag: Springer International Publishing
Buch: Database and Expert Systems Applications
Print ISBN: 978-3-319-64470-7

Electronic ISBN: 978-3-319-64471-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-64471-4_33

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