Trends in social network analysis : information propagation, user behavior modeling, forecasting, and vulnerability assessment / Rokia Missaoui, Talel Abdessalem, Matthieu Latapy, editors.

Preface; Contents; Contributors; The Perceived Assortativity of Social Networks: Methodological Problems and Solutions; 1 Introduction; 2 Assortativity in Social and Other Networks; 2.1 Literature Search: Method; 2.2 Literature Search: Results; 2.3 Literature Search: Conclusions; 3 Methodological Pitfalls and False Assortativity; 3.1 Group-Based Networks and Assortativity; 3.2 Modeling Group-Based Sampling; 3.3 Filtering Networks; 4 Solutions; 4.1 Increased Sampling; 4.2 Use of Null Models; 4.3 Analysing Weighted Networks; 4.4 Using Diadic Over Group-Based Approaches; 4.5 Modern Technology

4.6 Alternatives to the Newman Degree Correlation Measure5 Conclusions; References; A Parametric Study to Construct Time-Aware Social Profiles; 1 Introduction; 2 Related Works; 2.1 User Profile Building Process; 2.2 Incorporating Dynamic Interests in the Profile; 2.3 Social Network Evolution; 3 Proposition: Temporal Scores to Construct Social Profiles; 3.1 Notations; 3.2 *-0.9pc; 3.3 Community-Based Social Profile Construction Process with Temporal Score; 3.3.1 Temporal Score Calculation; 3.3.2 Temporal Score Integration; 4 Experiments; 4.1 Dataset Description

4.2 Analysis of Common Keywords Between DBLP and Mendeley4.3 Case Study; 4.3.1 Ground Truth: Extraction of the Real User Profile from Mendeley; 4.3.2 Social Profiles Construction and Parametric Study; 4.4 Results; 4.4.1 All Users Results; 4.4.2 Results for Selected Users; 4.4.3 Different Time Decay Rate for the Relationships and the Information; 4.4.4 Discussion; 5 Conclusion and Future Works; Appendix; References; Sarcasm Analysis on Twitter Data Using Machine LearningApproaches; 1 Introduction; 2 Related Work; 2.1 Machine Learning-Based Approach; 2.2 Corpus-Based; 2.3 Lexical Features

2.4 Pragmatic Feature2.5 Hyperbolic Feature; 3 Preliminaries; 3.1 System Model; 3.2 Part-of-Speech (POS) Tagging; 3.3 Parse Tree Generation; 4 Data Collection and Preprocessing; 4.1 Data Collection; 4.2 Preprocessing; 5 Proposed Scheme; 5.1 PBLGA; 5.2 LDC; 5.3 TCUF; 5.4 TCTDP; 6 Classifiers; 7 Results and Discussion; 7.1 Experimental Results; 8 Conclusion; References; The DEvOTION Algorithm for Delurking in Social Networks; 1 Introduction; 2 Targeted Influence Maximization; 3 Delurking-Oriented Targeted Influence Maximization; 3.1 Problem Statement; 3.2 Identifying and Characterizing Lurkers

3.3 Choosing the Information Diffusion Model3.4 Properties of the Proposed Objective Function; 3.5 Modeling the Information Diffusion Graph; 3.6 The DEvOTION Algorithm; 4 Experimental Evaluation; 4.1 Evaluation Methodology; 4.2 Experimental Setting; 4.3 Data; 5 Results; 5.1 Impact of Parameters in DEvOTION; 5.2 Comparison with Baselines; 5.3 Comparison with Influence Maximization Algorithms; 5.4 Comparison with KB-TIM; 5.5 Seed Characteristics; 5.6 Discussion; 6 Conclusions and Future Work; References; Social Engineering Threat Assessment Using a Multi-Layered Graph-Based Model