Discovering socio-spatio-temporal important locations of social media users
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier B.V.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Socio-spatio-temporal important locations (SSTILs) are places which are frequently visited by social media users in their social media history. Discovering SSTILs is important for several application domains, such as, recommender systems, advertisement applications, urban planning, etc. However, discovering SSTILs is challenging due to spatial, temporal, and social dimensions of the datasets, the lack of sufficient interest measures, and the need for developing computationally-efficient algorithms. In the literature, several methods are proposed to discover social important locations. However, these studies, usually, do not take into account temporal and social dimensions of the datasets and preferences of each user in a social group. In this study, we define SSTILs and SSTIL mining problem by taking into account spatial, temporal, and social dimensions of the social media datasets. We propose methods and interest measures to discover SSTILs efficiently based on both user and group preferences. The proposed algorithms were compared with a naïve alternative using real-life Twitter dataset. The results showed that the proposed algorithms outperform the naïve alternative. © 2017 Elsevier B.V.
Açıklama
Anahtar Kelimeler
Historical social media dataset, Socio-spatio-temporal important locations discovery, Spatial social media mining, SSTIL discovery, Twitter
Kaynak
Journal of Computational Science
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
22
