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Öğe Cloud Computing-Based Socially Important Locations Discovery on Social Media Big Datasets(World Scientific Publ Co Pte Ltd, 2020) Dokuz, Ahmet Sakir; Celik, MeteSocially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provides valuable information, such as which locations are frequently visited by a social media user, which locations are common for a social media user group, and which locations are socially important for a group of urban area residents. However, discovering socially important locations is challenging due to huge volume, velocity, and variety of social media datasets, inefficiency of current interest measures and algorithms on social media big datasets, and the need of massive spatial and temporal calculations for spatial social media analyses. In contrast, cloud computing provides infrastructure and platforms to scale compute-intensive jobs. In the literature, limited number of studies related to socially important locations discovery takes into account cloud computing systetns to scale increasing dataset size and to handle massive calculations. This study proposes a cloud-based socially important locations discovery algorithm of Cloud SS-ILM to handle volume and variety of social media big datasets. In particular, in this study, we used Apache Hadoop framework and Hadoop MapReduce programming model to scale dataset size and handle massive spatial and temporal calculations. The performance evaluation of the proposed algorithm is conducted on a cloud computing environment using Turkey Twitter social media big dataset. The experimental results show that using cloud computing systems for socially important locations discovery provide much faster discovery of results than classical algorithms. Moreover, the results show that it is necessary to use cloud computing systems for analyzing social media big datasets that could not be handled with traditional stand-alone computer systems. The proposed Cloud SS-ILM algorithm could be applied on many application areas, such as targeted advertisement of businesses, social media utilization of cities for city planners and local governments, and handling emergency situations.Öğe Dense Region Detection of Streaming Social Media Datasets using Blockchain-based Secure Computation(Institute of Electrical and Electronics Engineers Inc., 2022) Ecemis, Alper; Dokuz, Ahmet Sakir; Celik, MeteThe popularity of blockchain technology is increasing day by day, and it has a widespread usage as a reliable database structure. Blockchain has been utilized across a wide range of industries, such as IoT, social media, robotics, government, supply chain, and healthcare. As the usage of blockchain is emerged in social media studies, the problem of storing and analyzing streaming social media data in a safe, anonymous, and reliable manner is emerged. Within the scope of this problem, in this study, the adaptation of the streaming social media dataset to the blockchain systems and analysis of this data are carried out. A new algorithm of Dense Region Detection Algorithm (DRDA) is proposed to detect dense social regions of urban areas. Streaming data are collected from New York City, and the proposed DRDA is performed by miners to determine the dense regions of New York. As a result, dense regions with intensive tweet sharing in New York are detected and discussed. © 2022 IEEE.Öğe Discovering socially important locations of social media users(Pergamon-Elsevier Science Ltd, 2017) Dokuz, Ahmet Sakir; Celik, MeteSocially important locations are places that are frequently visited by social media users in their social media life. Discovering socially interesting, popular or important locations from a location based social network has recently become important for recommender systems, targeted advertisement applications, and urban planning, etc. However, discovering socially important locations from a social network is challenging due to the data size and variety, spatial and temporal dimensions of the datasets, the need for developing computationally efficient approaches, and the difficulty of modeling human behavior. In the literature, several studies are conducted for discovering socially important locations. However, majority of these studies focused on discovering locations without considering historical data of social media users. They focused on analysis of data of social groups without considering each user's preferences in these groups. In this study, we proposed a method and interest measures to discover socially important locations that consider historical user data and each user's (individual's) preferences. The proposed algorithm was compared with a naive alternative using real-life Twitter dataset. The results showed that the proposed algorithm outperforms the naive alternative. (C) 2017 Elsevier Ltd. All rights reserved.Öğe Discovery of hydrometeorological patterns(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2014) Celik, Mete; Dadaser-Celik, Filiz; Dokuz, Ahmet SakirHydrometeorological patterns can be defined as meaningful and nontrivial associations between hydrological and meteorological parameters over a region. Discovering hydrometeorological patterns is important for many applications, including forecasting hydrometeorological hazards (floods and droughts), predicting the hydrological responses of ungauged basins, and filling in missing hydrological or meteorological records. However, discovering these patterns is challenging due to the special characteristics of hydrological and meteorological data, and is computationally complex due to the archival history of the datasets. Moreover, defining monotonic interest measures to quantify these patterns is difficult. In this study, we propose a new monotonic interest measure, called the hydrometeorological prevalence index, and a novel algorithm for mining hydrometeorological patterns (HMP-Miner) out of large hydrological and meteorological datasets. Experimental evaluations using real datasets show that our proposed algorithm outperforms the naive alternative in discovering hydrometeorological patterns efficiently.Öğe Temporal Sentiment Analysis of Socially Important Locations of Social Media Users(Springer Science and Business Media Deutschland GmbH, 2021) Ecemiş, Alper; Dokuz, Ahmet Şakir; Celik, MeteSocially important locations are the places which are frequently visited by social media users. Temporal sentiment analysis of socially important locations is the process of interpretation and classification of emotions within their sharings in their socially important locations over time. Observing the temporal sentiment changes in these locations helps both to examine the emotion change in the locations and to understand the thoughts of the social media users in these locations. In this paper, Twitter is selected as social media data source and temporal sentiment analysis of socially important locations of social media users are analyzed in different time frames. For the analysis, a method, called Temporal Sentiment Analysis of Socially Important Locations (TS-SIL), is proposed in this study. In this method, first of all, socially important locations are discovered from the collected Twitter dataset. Then, sentiment analysis is performed using a dictionary based approach and several machine learning algorithms. Finally, the sharings in the locations are listed and the sentiments at these locations are analyzed by daily, weekly, and monthly basis. As a result, socially important locations of the city of Istanbul are discovered and temporal sentiment analysis of these locations are performed. Results shows that all of the socially important locations of İstanbul, except Beşiktaş Fish Market, showed emotional fluctuations over the time. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
