Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/552
Title: F-SED : Feature-Centric Social Event Detection
Authors: Mansour, Elio
Tekli, Gilbert 
Arnould, Philippe
Chbeir, Richard
Cardinale, Yudith
Affiliations: Department of Mechatronics Engineering 
Keywords: Social Event Detection
Semantic clustering
Multimedia sharing
Formal Concept Analysis
Subjects: Social networks
Issue Date: 2017
Publisher: Springer
Part of: Database and Expert Systems Applications : 28th International Conference, DEXA 2017, Lyon, France, August 28-31, 2017, Proceedings, Part II
Start page: 409
End page: 426
Conference: International Conference on Database and Expert Systems Applications (28-31 August 2017 : Lyon, France) 
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.
URI: https://scholarhub.balamand.edu.lb/handle/uob/552
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Mechatronics Engineering

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