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This timely book focuses on influence and behavior analysisin the broader context of social network applications and social media. Twitter accounts of telecommunications companies are analyzed. Rumor sources in finite graphs with boundary effects by message-passing algorithms are identified. The coherent, state-of-the-art collection of chapters was initially selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM '17). Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. Original chapters coming from outside of the meeting round out the coverage. The result will appeal to researchers and students working in social network and social media analysis.

Intro -- Contents -- Social Network to Improve the Educational Experience with the Deployment of Different Learning Models -- 1 Introduction -- 2 Social Networks in Education -- 2.1 Facebook -- 2.2 Twitter -- 2.3 YouTube -- 3 SLNs: Sporadic Learning Networks -- 4 OPPIA Platform -- 4.1 Layer Model -- 4.2 OPPIA Architecture -- 4.3 OPPIA Operation -- 5 OPPIA Implementation -- 6 Conclusions and Future Work -- References -- Temporal Model of the Online Customer Review Helpfulness Prediction with Regression Methods -- 1 Introduction -- 2 Related Works -- 2.1 Linear Regression -- 2.2 The Coefficient of Determination -- 2.3 The Akaike Information Criterion -- 3 Method -- 3.1 Corpus Collection -- 3.2 Morphological Preprocessing -- 3.3 Feature Set -- 3.4 Sentiment Feature Selection -- 3.5 Evaluation Index -- 4 Experiments -- 4.1 Authors and Affiliations of Chinese Customer Review Corpus -- 4.2 Experimental Tools -- 4.3 Experimental Results -- 4.4 Discussion -- 5 Conclusion and Future Works -- References -- Traits of Leaders in Movement Initiation: Classification and Identification -- 1 Introduction -- 2 The Proposed Approach -- 2.1 Bidirectional Agreement in Multi-Agent Systems -- 2.2 Bidirectional Agreement Condition -- 2.3 Leaders as State Changers -- 2.4 Approach Overview -- 2.5 FLICA -- 2.6 Leadership Trait Characterization Scheme -- 3 Experimental Setup -- 3.1 Trait of Leadership Model -- 3.2 Datasets -- 3.3 Sensitivity Analysis in Model Classification -- 3.4 Hypotheses Tests -- 3.5 Parameter Setting -- 4 Results -- 4.1 Traits of Leader Classification: Sensitivity Analysis -- 4.2 Trait Identification of Baboon Movement -- 4.3 Trait Identification of Fish Movement -- 4.4 Traits of Leaders as Measure of Degree of Hierarchy Structure -- 5 Conclusions -- References -- Emotional Valence Shifts and User Behavior on Twitter, Facebook, and YouTube.

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.

From Sociology to Computing in Social Networks provides an analysis of social networking and the emerging trends in data mining. Topics include social network modeling, customizable social network infrastructure construction, dynamic growth and evolution patterns identification, and more.

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.

Intro -- Preface -- Contents -- Predicting Implicit Negative Relations in Online Social Networks -- Introduction -- Related Work -- Methodology -- Dataset Description -- Formulation in R -- Loading the Data -- Transforming the Data by Loading Features -- Splitting the Data into Training and Testing Data -- Fitting a Logistic Regression Model Using Training Data -- Using the Fitted Model to Do Predictions for the Test Data -- Results and Discussion -- Future Work -- Conclusion -- References -- Automobile Insurance Fraud Detection Using Social Network Analysis -- Introduction -- Literature Review -- Research Methodology -- Evaluation with the Prototype System -- Summary and Conclusion -- References -- Improving Circular Layout Algorithm for Social Network Visualization Using Genetic Algorithm -- Introduction -- Initial Circular Layout -- Improvement -- Genetic Algorithm -- Edge Crossing Detection -- Results -- Conclusion -- References -- Live Twitter Sentiment Analysis -- Introduction -- Related Work -- Method -- Data Ingestion -- Data Preparation and Analysis -- Corpus Building (Bootstrapping) -- Model Building -- The Hashtag Problem -- Testing, Tuning and Security -- Future Work and Observations -- Results -- Conclusion -- References -- Artificial Neural Network Modeling and Forecasting of Oil Reservoir Performance -- Abbreviations -- Introduction -- Modeling of Big Data Based on Artificial and Computational Intelligence -- Application of ACI to Petroleum Engineering -- Workflow and Design of Proxy Modeling -- Neural Network Interpolation Algorithms -- Radial Basis Function Networks (RBF) -- Multilayer Neural Network Algorithm: Levenberg-Marquardt Optimization -- Big Data Assembly and Base Cases -- Applied Ranges for Model Parameter Space -- Results and Discussion -- Conclusions -- References.

BACKGROUND: The first half of 2020 has been marked as the era of COVID-19 pandemic which affected the world globally in almost every aspect of the daily life from societal to economical. To prevent the spread of COVID-19, countries have implemented diverse policies regarding Non-Pharmaceutical Intervention (NPI) measures. This is because in the first stage countries had limited knowledge about the virus and its contagiousness. Also, there was no effective medication or vaccines. This paper studies the effectiveness of the implemented policies and measures against the deaths attributed to the virus between January and May 2020. METHODS: Data from the European Centre for Disease Prevention and Control regarding the identified cases and deaths of COVID-19 from 48 countries have been used. Additionally, data concerning the NPI measures related policies implemented by the 48 countries and the capacity of their health care systems was collected manually from their national gazettes and official institutes. Data mining, time series analysis, pattern detection, machine learning, clustering methods and visual analytics techniques have been applied to analyze the collected data and discover possible relationships between the implemented NPIs and COVID-19 spread and mortality. Further, we recorded and analyzed the responses of the countries against COVID-19 pandemic, mainly in urban areas which are over-populated and accordingly COVID-19 has the potential to spread easier among humans. RESULTS: The data mining and clustering analysis of the collected data showed that the implementation of the NPI measures before the first death case seems to be very effective in controlling the spread of the disease. In other words, delaying the implementation of the NPI measures to after the first death case has practically little effect on limiting the spread of the disease. The success of implementing the NPI measures further depends on the way each government monitored their application. Countries with stricter policing of the measures ...

Background: The first half of 2020 has been marked as the era of COVID-19 pandemic which affected the world globally in almost every aspect of the daily life from societal to economical. To prevent the spread of COVID-19, countries have implemented diverse policies regarding Non-Pharmaceutical Intervention (NPI) measures. This is because in the first stage countries had limited knowledge about the virus and its contagiousness. Also, there was no effective medication or vaccines. This paper studies the effectiveness of the implemented policies and measures against the deaths attributed to the virus between January and May 2020. Methods: Data from the European Centre for Disease Prevention and Control regarding the identified cases and deaths of COVID-19 from 48 countries have been used. Additionally, data concerning the NPI measures related policies implemented by the 48 countries and the capacity of their health care systems was collected manually from their national gazettes and official institutes. Data mining, time series analysis, pattern detection, machine learning, clustering methods and visual analytics techniques have been applied to analyze the collected data and discover possible relationships between the implemented NPIs and COVID-19 spread and mortality. Further, we recorded and analyzed the responses of the countries against COVID-19 pandemic, mainly in urban areas which are over-populated and accordingly COVID-19 has the potential to spread easier among humans. Results: The data mining and clustering analysis of the collected data showed that the implementation of the NPI measures before the first death case seems to be very effective in controlling the spread of the disease. In other words, delaying the implementation of the NPI measures to after the first death case has practically little effect on limiting the spread of the disease. The success of implementing the NPI measures further depends on the way each government monitored their application. Countries with stricter policing of the measures seems to be more effective in controlling the transmission of the disease. Conclusions: The conducted comparative data mining study provides insights regarding the correlation between the early implementation of the NPI measures and controlling COVID-19 contagiousness and mortality. We reported a number of useful observations that could be very helpful to the decision makers or epidemiologists regarding the rapid implementation and monitoring of the NPI measures in case of a future wave of COVID-19 or to deal with other unknown infectious pandemics. Regardless, after the first wave of COVID-19, most countries have decided to lift the restrictions and return to normal. This has resulted in a severe second wave in some countries, a situation which requires re-evaluating the whole process and inspiring lessons for the future.

Internet-enabled devices or Internet of Things as it has been prevailed are increasing exponentially every day. The lack of security standards in the manufacturing of these devices along with the haste of the manufacturers to increase their market share in this area has created a very large network of vulnerable devices that can be easily recruited as bot members and used to initiate very large volumetric Distributed Denial of Service (DDoS) attacks. The significance of the problem can be easily acknowledged due to the large number of cases regarding attacks on institutions, enterprises and even countries which have been recently revealed. In the current paper a novel method is introduced, which is based on a data mining technique that can analyze incoming IP traffic details and early warn the network administrator about a potentially developing DDoS attack. The method can scale depending on the availability of the infrastructure from a conventional laptop computer to a complex cloud infrastructure. Based on the hardware configuration as it is proved with the experiments the method can easily monitor and detect abnormal network traffic of several Gbps in real time using the minimum hardware equipment.

Intro -- Preface -- Contents -- Contributors -- Crowd Behavior Modeling in Emergency Evacuation Scenarios Using Belief-Desire-Intention Model -- 1 Introduction -- 2 Background and Related Work -- 3 Multi-Agent Emergency Evacuation Simulation Model -- 4 BDI Framework in Emergency Evacuation -- 4.1 BDI Framework -- 4.2 Proposed Model -- 5 Experiments -- 6 Conclusions and Future Work -- References -- Entering Their World: Using Social Media to Support Students in Modern Times -- 1 Introduction -- 2 Literature Review -- 3 Context -- 3.1 Students as Partners in Research -- 4 Methodology -- 5 Results -- 5.1 Quantitative Data -- 5.2 Qualitative Data -- 6 Discussion -- 7 Conclusion -- References -- Utilizing Multilingual Social Media Analysis for Food Venue Recommendation -- 1 Introduction -- 2 Related Work -- 3 Food Venue Recommendation Based on Geo-Tagged Tweets -- 3.1 Extracting Venues from Geo-Tagged Tweets -- 3.2 Calculating the Venue Score Based on Popularity -- 3.3 Calculating the Venue Scores for Locations with Sparse Tweets for a Given Language User -- 4 Adjusting Food Venue Recommendation Through Explicit Rating and Sentiment Information -- 5 System Implementation -- 6 User Evaluation Study -- 7 Conclusion -- References -- Simplifying E-Commerce Analytics by Discovering Hidden Knowledge in Big Data Clickstreams -- 1 Introduction -- 2 Related Work -- 2.1 Clickstream Analytics -- 2.2 Big Data Clickstream Analytics Fundamentals -- 3 The SAFID Methodology -- 3.1 LERP-RSA Data Structure -- 3.2 ARPaD Algorithm -- 3.3 SAFID Methodology -- 4 Experimental Analysis -- 5 Conclusion -- References -- Event Detection on Communities: Tracking the Change in Community Structure within Temporal CommunicationNetworks -- 1 Introduction -- 2 Overview on Community Detection.