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Öğe Discovering socially similar users in social media datasets based on their socially important locations(Elsevier Ltd, 2018) Celik M.; Dokuz A.S.Socially similar social media users can be defined as users whose frequently visited locations in their social media histories are similar. Discovering socially similar social media users is important for several applications, such as, community detection, friendship analysis, location recommendation, urban planning, and anomaly user and behavior detection. Discovering socially similar users is challenging due to dataset size and dimensions, spam behaviors of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar social media users out of social media datasets using spatial and temporal information. However, most of these studies rely on trajectory pattern mining methods or take into account semantic information of social media datasets. Limited number of studies focus on discovering similar users based on their social media location histories. In this study, to discover socially similar users, frequently visited or socially important locations of social media users are taken into account instead of all locations that users visited. A new interest measure, which is based on Levenshtein distance, was proposed to quantify user similarity based on their socially important locations and two algorithms were developed using the proposed method and interest measure. The algorithms were experimentally evaluated on a real-life Twitter dataset. The results show that the proposed algorithms could successfully discover similar social media users based on their socially important locations. © 2018 Elsevier LtdÖğe Discovering socio-spatio-temporal important locations of social media users(Elsevier B.V., 2017) Celik M.; Dokuz A.S.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.Öğe FAST SS-ILM: A COMPUTATIONALLY EFFICIENT ALGORITHM to DISCOVER SOCIALLY IMPORTANT LOCATIONS(Copernicus GmbH, 2017) Dokuz A.S.; Celik M.Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provide several valuable information about user behaviours on social media networking sites. However, discovering socially important locations are challenging due to data volume and dimensions, spatial and temporal calculations, location sparseness in social media datasets, and inefficiency of current algorithms. In the literature, several studies are conducted to discover important locations, however, the proposed approaches do not work in computationally efficient manner. In this study, we propose Fast SS-ILM algorithm by modifying the algorithm of SS-ILM to mine socially important locations efficiently. Experimental results show that proposed Fast SS-ILM algorithm decreases execution time of socially important locations discovery process up to 20 %. © Authors 2017.
