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F-SED: Feature-Centric Social Event Detection

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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https://hal.archives-ouvertes.fr/hal-01905727
Contributor : Elio Mansour <>
Submitted on : Monday, March 25, 2019 - 6:25:12 PM
Last modification on : Thursday, March 5, 2020 - 7:20:10 PM
Long-term archiving on: : Wednesday, June 26, 2019 - 5:11:11 PM

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Elio Mansour, Gilbert Tekli, Philippe Arnould, Richard Chbeir, Yudith Cardinale. F-SED: Feature-Centric Social Event Detection. 28th International Conference on Database and Expert Systems Applications - DEXA 2017, Aug 2017, Lyon, France. ⟨10.1007/978-3-319-64471-4_33⟩. ⟨hal-01905727⟩

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