Influence and Behavior Analysis in Social Networks and Social Media (eBook)

Mehmet Kaya, Reda Alhajj (Herausgeber)

eBook Download: PDF

2018
235 Seiten
Springer International Publishing (Verlag)
9783030025922 (ISBN)

Infos

This timely book focuses on influence and behavior analysis in 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.

Inhalt

Contents 6
Social Network to Improve the Educational Experience with the Deployment of Different Learning Models 8
1 Introduction 8
2 Social Networks in Education 10
2.1 Facebook 11
2.2 Twitter 12
2.3 YouTube 13
3 SLNs: Sporadic Learning Networks 14
4 OPPIA Platform 18
4.1 Layer Model 18
4.2 OPPIA Architecture 21
4.3 OPPIA Operation 24
5 OPPIA Implementation 27
6 Conclusions and Future Work 29
References 30
Temporal Model of the Online Customer Review Helpfulness Prediction with Regression Methods 33
1 Introduction 33
2 Related Works 34
2.1 Linear Regression 35
2.2 The Coefficient of Determination 36
2.3 The Akaike Information Criterion 36
3 Method 36
3.1 Corpus Collection 37
3.2 Morphological Preprocessing 37
3.3 Feature Set 38
3.4 Sentiment Feature Selection 38
3.5 Evaluation Index 39
4 Experiments 39
4.1 Authors and Affiliations of Chinese Customer Review Corpus 40
4.2 Experimental Tools 40
4.3 Experimental Results 40
4.4 Discussion 42
5 Conclusion and Future Works 43
References 44
Traits of Leaders in Movement Initiation: Classification and Identification 45
1 Introduction 45
2 The Proposed Approach 46
2.1 Bidirectional Agreement in Multi-Agent Systems 47
2.2 Bidirectional Agreement Condition 47
2.3 Leaders as State Changers 48
2.4 Approach Overview 49
2.5 FLICA 49
2.6 Leadership Trait Characterization Scheme 51
3 Experimental Setup 55
3.1 Trait of Leadership Model 55
3.2 Datasets 56
3.3 Sensitivity Analysis in Model Classification 57
3.4 Hypotheses Tests 57
3.5 Parameter Setting 58
4 Results 58
4.1 Traits of Leader Classification: Sensitivity Analysis 58
4.2 Trait Identification of Baboon Movement 60
4.3 Trait Identification of Fish Movement 63
4.4 Traits of Leaders as Measure of Degree of Hierarchy Structure 65
5 Conclusions 66
References 67
Emotional Valence Shifts and User Behavior on Twitter, Facebook, and YouTube 69
1 Introduction 69
2 Related Work 70
3 Data Analysis Procedure 72
3.1 Data Extraction 72
3.2 Data Preprocessing 74
3.3 Emotion Extraction 74
3.4 Data Analysis and Research Questions 75
4 Results 76
4.1 Emotion Intensity During Positive, Negative, and Polarizing Events 76
4.2 User Behavior 81
5 Discussion 85
6 Conclusion 86
References 87
Diffusion Algorithms in Multimedia Social Networks: A Novel Model 90
1 Introduction 90
2 Related Works 92
3 The Data Model 94
3.1 Definitions 94
3.2 Hypergraph Building and Computation 97
4 Influence Diffusion and Maximization in OSNs 98
5 Experimental Results 99
6 Conclusion and Discussions 104
References 106
Detecting Canadian Internet Satisfaction by Analyzing Twitter Accounts of Shaw Communications 109
1 Introduction 109
1.1 Problem Definition 110
1.2 Motivation 110
1.3 Summary 111
2 Related Work 111
2.1 Internet Issues 111
2.2 Sentiment Analysis 112
2.3 Consumer Satisfaction 113
3 Methodology 115
3.1 Internet Issues 115
Reported Outages 115
Common Internet Issues 116
Average Sentiment on Outage Days 116
Locations 117
3.2 Consumer Satisfaction 118
Sentiment 118
Word Usage 119
Hashtags 119
Response Time 119
4 Results 120
4.1 Internet Issues 120
Reported Outages 120
Common Internet Issues 121
Average Sentiment on Outage Days 121
Locations 122
4.2 Consumer Satisfaction 123
Sentiment 123
Word Usage 127
Hashtags 128
Response Time 128
5 Conclusions and Future Work 129
References 130
Editing Behavior Analysis for Predicting Active and Inactive Users in Wikipedia 131
1 Introduction 131
2 Related Work 133
3 Dataset 135
4 Differences in Editing Behavior 136
5 Predicting Active and Inactive Users 138
5.1 Most Important Features 139
6 Experimental Results 142
6.1 Comparison with Related Work 143
6.2 Early Prediction of Inactive Users 144
6.3 Varying the Threshold ? 145
7 Conclusions 149
References 149
Incentivized Social Sharing: Characteristics and Optimization 152
1 Introduction 152
2 Incentivized Sharing: A Motivating Example and Evaluation Framework 154
3 Notation and Problem Statement 157
4 Theoretical Analysis 158
5 Characteristics of the Me+3 Incentive 160
5.1 Incentivized Sharing Degree Distribution 161
5.2 Social Pressure and Adoption 162
5.3 Purchase Probabilities and Free Deals 164
5.4 Shares and Recipient Purchase References 165
5.5 Impact of the Incentive Amount 167
5.6 Arrivals and Awakening 168
6 Me+N: A Model for Incentivized Sharing Optimization 169
6.1 Arrival and Awakening Functions 170
6.2 Creating a Me+N Sharing Distribution 171
6.3 Generated Purchase Probabilities 172
6.4 Cost of Sharing Incentives 173
7 Experiments 173
8 Related Work 174
9 Conclusions 176
References 176
Rumor Source Detection in Finite Graphs with Boundary Effects by Message-Passing Algorithms 178
1 Introduction 178
1.1 Our Contributions 179
2 Preliminaries of Rumor Centrality 179
3 Trees with a Single End Vertex 181
3.1 Impact of Boundary Effects on P(Gn "026A30C v) 181
3.2 Analytical Characterization of Likelihood Function 183
3.3 Optimality Characterization of Likelihood Estimate 185
3.4 Likelihood Ratio Between Centroid and End Vertex on Different Network Topology 186
4 Trees with Multiple End Vertices 188
4.1 Degree-Regular Tree (d?3) Special Case: Gn is Broom-Shaped 188
4.2 Message-Passing Algorithm 190
4.3 Simulation Results for Finite d-Regular Tree Networks 192
4.4 Simulation Results for Finite General Tree Networks 193
5 Conclusion 194
References 194
Robustness of Influence Maximization Against Non-adversarial Perturbations 196
1 Introduction 196
2 Related Work 199
3 Preliminaries and Problem Formulation 200
4 Methodology 201
4.1 Networks 201
4.2 Influence Spread Probability and Types of Perturbations 201
4.3 Influence Maximization Algorithms 203
5 Results 204
5.1 Overlap of Seed Nodes 204
5.2 Influence Spread 205
5.3 Relation Between Amount of Error and Effectiveness of Algorithm 207
6 Discussion 210
6.1 Implication 210
6.2 Limitations 211
7 Conclusion 211
References 211
Analyzing Social Book Reading Behavior on Goodreads and How It Predicts Amazon Best Sellers 214
1 Introduction 214
2 Related Works 217
3 Dataset Preparation 219
4 Characteristic Behavior 220
4.1 Book Ratings and Reviews 220
4.2 Book Genres and Book Shelves 221
4.3 Goodreads Users' Status Posts 223
4.4 Author Characteristics 225
5 Will a Book Become an Amazon Best Seller? 226
5.1 Performance of the Prediction Model 228
5.2 Discriminative Power of the Features 229
6 Close Competitors 230
6.1 Comparisons 231
7 Conclusions and Future Works 235
References 235

Produkt-Details

• Erscheint lt. Verlag: 11.12.2018
• Reihe/Serie: Lecture Notes in Social Networks
• Reihe/Serie: Lecture Notes in Social Networks
• Zusatzinfo: VI, 235 p. 96 illus., 84 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Sozialwissenschaften ► Politik / Verwaltung
• Themenwelt: Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Marketing / Vertrieb
• Schlagworte: Analyzing Twitter Accounts • ASONAM 2017 • Computational Social Sciences • influence analysis • misuse of social media • Predicting 'Amazon Best Sellers' • Recommendation Systems • Social Book Reading Behavior • social network analysis and mining • twitter analysis shaw communications
• ISBN-13: 9783030025922 / 9783030025922