Coverart for item
The Resource Prediction and inference from social networks and social media, Jalal Kawash, Nitin Agarwal, Tansel Özyer, editor

Prediction and inference from social networks and social media, Jalal Kawash, Nitin Agarwal, Tansel Özyer, editor

Label
Prediction and inference from social networks and social media
Title
Prediction and inference from social networks and social media
Statement of responsibility
Jalal Kawash, Nitin Agarwal, Tansel Özyer, editor
Contributor
Editor
Subject
Language
eng
Summary
This book addresses the challenges of social network and social media analysis in terms of prediction and inference. The chapters collected here tackle these issues by proposing new analysis methods and by examining mining methods for the vast amount of social content produced. Social Networks (SNs) have become an integral part of our lives; they are used for leisure, business, government, medical, educational purposes and have attracted billions of users. The challenges that stem from this wide adoption of SNs are vast. These include generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, and behavior detection. This text has applications to widely used platforms such as Twitter and Facebook and appeals to students, researchers, and professionals in the field
Member of
Cataloging source
N$T
Dewey number
303.49
Index
no index present
LC call number
HM901
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Kawash, Jalal
  • Agarwal, Nitin
  • Özyer, Tansel
Series statement
Lecture notes in social networks
http://library.link/vocab/subjectName
  • Social prediction
  • Social networks
  • Social media
Label
Prediction and inference from social networks and social media, Jalal Kawash, Nitin Agarwal, Tansel Özyer, editor
Instantiates
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Preface; Contents; 1 Having Fun?: Personalized Activity-Based Mood Prediction in Social Media; 1 Introduction; 2 Related Work; 3 Social Media Data; 3.1 Twitter Dataset; 3.2 Ground Truth; 4 Features; 5 Prediction; 5.1 Prediction Framework; 5.2 General Prediction Results; 5.3 Personalized Prediction Results; 6 Conclusion and Future Work; References; 2 Automatic Medical Image Multilingual Indexation Through a Medical Social Network; 1 Introduction; 2 Related Work; 2.1 Medical Social Networks; 2.2 Multilingual Indexation Approaches; 2.2.1 An Overview
  • 2.2.2 Indexation Approaches via Social Networks3 Social Network Architecture Description and Implementation; 4 The Proposed Methodology; 4.1 Comments' Pre-processing; 4.2 Cleaning, Correcting, and Lemmatization; 4.2.1 Cleaning; 4.2.2 Correcting Words; 4.2.3 Lemmatization Words; 4.3 Terms' Extraction; 4.3.1 Simple Terms' Extraction; 4.3.2 Compound Terms' Extraction; 4.3.3 Concepts' Extraction; 5 Experimental Results; 5.1 Data Test and Evaluation Criteria; 5.2 Evaluation and Results of Our Approach; 6 Conclusion and Future Work; References
  • 3 The Significant Effect of Overlapping Community Structures in Signed Social Networks1 Introduction; 1.1 Contribution of the Paper; 2 Related Work; 3 Use of Terms, Variables and Definitions; 4 Signed Disassortative Degree Mixing and Information Diffusion Approach; 4.1 Identifying Leaders; 4.2 Signed Cascading Process; 4.3 Overlapping Community-Based Ranking Algorithms; 4.3.1 Overlapping Community-Based HITS; 4.3.2 Overlapping Community-Based PageRank; 4.4 Baseline OCD Methods; 4.4.1 Signed Probabilistic Mixture Model ; 4.4.2 Multi-Objective Evolutionary Algorithm in Signed Networks
  • 5 Sign Prediction5.1 Classifiers; 5.1.1 Logistic Regression; 5.1.2 Bagging; 5.1.3 J48; 5.1.4 Decision Table; 5.1.5 Bayesian Network and Naive Bayesian; 5.2 Sign Prediction Features; 5.2.1 Simple Degree Sign Prediction Features; 5.2.2 OC-HITS Sign Prediction; 5.2.3 OC-PageRank Sign Prediction; 6 Dataset and Metrics; 6.1 Real World Networks; 6.2 Synthetic Networks; 6.3 Evaluation Metrics; 6.3.1 Normalized Mutual Information; 6.3.2 Modularity; 6.3.3 Frustration; 7 Results; 7.1 Results of OCD; 7.1.1 Network Size n; 7.1.2 Average Node Degree k; 7.1.3 Maximum Node Degree maxk
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319510484
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
ocn978248695
Label
Prediction and inference from social networks and social media, Jalal Kawash, Nitin Agarwal, Tansel Özyer, editor
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Preface; Contents; 1 Having Fun?: Personalized Activity-Based Mood Prediction in Social Media; 1 Introduction; 2 Related Work; 3 Social Media Data; 3.1 Twitter Dataset; 3.2 Ground Truth; 4 Features; 5 Prediction; 5.1 Prediction Framework; 5.2 General Prediction Results; 5.3 Personalized Prediction Results; 6 Conclusion and Future Work; References; 2 Automatic Medical Image Multilingual Indexation Through a Medical Social Network; 1 Introduction; 2 Related Work; 2.1 Medical Social Networks; 2.2 Multilingual Indexation Approaches; 2.2.1 An Overview
  • 2.2.2 Indexation Approaches via Social Networks3 Social Network Architecture Description and Implementation; 4 The Proposed Methodology; 4.1 Comments' Pre-processing; 4.2 Cleaning, Correcting, and Lemmatization; 4.2.1 Cleaning; 4.2.2 Correcting Words; 4.2.3 Lemmatization Words; 4.3 Terms' Extraction; 4.3.1 Simple Terms' Extraction; 4.3.2 Compound Terms' Extraction; 4.3.3 Concepts' Extraction; 5 Experimental Results; 5.1 Data Test and Evaluation Criteria; 5.2 Evaluation and Results of Our Approach; 6 Conclusion and Future Work; References
  • 3 The Significant Effect of Overlapping Community Structures in Signed Social Networks1 Introduction; 1.1 Contribution of the Paper; 2 Related Work; 3 Use of Terms, Variables and Definitions; 4 Signed Disassortative Degree Mixing and Information Diffusion Approach; 4.1 Identifying Leaders; 4.2 Signed Cascading Process; 4.3 Overlapping Community-Based Ranking Algorithms; 4.3.1 Overlapping Community-Based HITS; 4.3.2 Overlapping Community-Based PageRank; 4.4 Baseline OCD Methods; 4.4.1 Signed Probabilistic Mixture Model ; 4.4.2 Multi-Objective Evolutionary Algorithm in Signed Networks
  • 5 Sign Prediction5.1 Classifiers; 5.1.1 Logistic Regression; 5.1.2 Bagging; 5.1.3 J48; 5.1.4 Decision Table; 5.1.5 Bayesian Network and Naive Bayesian; 5.2 Sign Prediction Features; 5.2.1 Simple Degree Sign Prediction Features; 5.2.2 OC-HITS Sign Prediction; 5.2.3 OC-PageRank Sign Prediction; 6 Dataset and Metrics; 6.1 Real World Networks; 6.2 Synthetic Networks; 6.3 Evaluation Metrics; 6.3.1 Normalized Mutual Information; 6.3.2 Modularity; 6.3.3 Frustration; 7 Results; 7.1 Results of OCD; 7.1.1 Network Size n; 7.1.2 Average Node Degree k; 7.1.3 Maximum Node Degree maxk
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319510484
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
ocn978248695

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