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This book includes chapters which discuss effective and efficient approaches in dealing with various aspects of social media analysis by using machine learning techniques from clustering to deep learning. A variety of theoretical aspects, application domains and case studies are covered to highlight how it is affordable to maximize the benefit of various applications from postings on social media platforms. Social media platforms have significantly influenced and reshaped various social aspects. They have set new means of communication and interaction between people, turning the whole world into a small village where people with internet connect can easily communicate without feeling any barriers. This has attracted the attention of researchers who have developed techniques and tools capable of studying various aspects of posts on social media platforms with main concentration on Twitter. This book addresses challenging applications in this dynamic domain where it is not possible to continue applying conventional techniques in studying social media postings. The content of this book helps the reader in developing own perspective about how to benefit from machine learning techniques in dealing with social media postings and how social media postings may directly influence various applications

This book includes chapters which discuss effective and efficient approaches in dealing with various aspects of social media analysis by using machine learning techniques from clustering to deep learning. A variety of theoretical aspects, application domains and case studies are covered to highlight how it is affordable to maximize the benefit of various applications from postings on social media platforms. Social media platforms have significantly influenced and reshaped various social aspects. They have set new means of communication and interaction between people, turning the whole world into a small village where people with internet connect can easily communicate without feeling any barriers. This has attracted the attention of researchers who have developed techniques and tools capable of studying various aspects of posts on social media platforms with main concentration on Twitter. This book addresses challenging applications in this dynamic domain where it is not possible to continue applying conventional techniques in studying social media postings. The content of this book helps the reader in developing own perspective about how to benefit from machine learning techniques in dealing with social media postings and how social media postings may directly influence various applications.

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields.

Intro -- Preface -- Introduction -- From Security to Community Detection in Social Networking Platforms -- Contents -- Real-World Application of Ego-Network Analysis to Evaluate Environmental Management Structures -- 1 Introduction -- 2 Methods -- 2.1 Study Areas -- 2.2 Research Design and Data Collection -- 3 Concepts and Methodology -- 4 Research Findings and Discussion -- 4.1 Structure of the Ego-Networks -- 4.2 Correlation Between the Indegree and Betweenness Values of the Networks -- 4.3 Consequences of Removing the Ego: Role of Simmelian Ties and Girvan-Newman Clustering -- 5 Conclusions and Future Work -- References -- An Evolutionary Approach for Detecting Communities in Social Networks -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 The Algorithm -- 3.2 Encoding and Initialization -- 3.3 Fitness Function -- 3.4 Crossover -- 3.5 Mutation -- 3.6 Selection -- 3.7 Preprocess -- 4 Experimental Results and Discussion -- 4.1 Datasets -- 4.2 Comparison Between Genetic Algorithms -- 4.3 Comparison with Different Community Detection Algorithms for Social Networks -- 4.4 Experiments with Using Preprocess Step -- 5 Conclusion -- References -- On Detecting Multidimensional Communities -- 1 Introduction -- 1.1 Related Approaches -- 1.2 Motivations and Contributions -- 2 The MCDA Approach -- 2.1 Problem Statement -- 2.2 Outlier Handling -- 2.2.1 Step 1: Estimating Outlier Scores -- 2.2.2 Step 2: Automatic Identification of Outliers -- 2.3 Summary of Outlier Handling Procedure -- 2.4 Mining Community Structures -- 2.4.1 Community Detection Approach -- 2.4.2 Selection of Relevant Dimensions of Communities -- 2.5 Summary of the MCDA Approach -- 3 Experimental Results -- 3.1 Experiments on Synthetic Networks -- 3.1.1 Synthetic Network Generation -- 3.1.2 Community Detection Accuracy -- 3.1.3 Outlier Immunity -- 3.1.4 Scalability Experiments.

This book focuses on recent technical advancements and state-of-the art technologies for analyzing characteristic features and probabilistic modelling of complex social networks and decentralized online network architectures. Such research results in applications related to surveillance and privacy, fraud analysis, cyber forensics, propaganda campaigns, as well as for online social networks such as Facebook. The text illustrates the benefits of using advanced social network analysis methods through application case studies based on practical test results from synthetic and real-world data. This book will appeal to researchers and students working in these areas.

Social network analysis increasingly bridges the discovery of patterns in diverse areas of study as more data becomes available and complex. Yet the construction of huge networks from large data often requires entirely different approaches for analysis including; graph theory, statistics, machine learning and data mining. This work covers frontier studies on social network analysis and mining from different perspectives such as social network sites, financial data, e-mails, forums, academic research funds, XML technology, blog content, community detection and clique finding, prediction of user's- behavior, privacy in social network analysis, mobility from spatio-temporal point of view, agent technology and political parties in parliament. These topics will be of interest to researchers and practitioners from different disciplines including, but not limited to, social sciences and engineering