Please use this identifier to cite or link to this item:
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) 
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.
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Mechatronics Engineering

Show full item record

Record view(s)

checked on Jun 8, 2023

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.