Topic Detection and Classification in Social Networks : the Twitter Case.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Milioris, Dimitrios
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing AG, z. Hd. Alexander Grossmann, 2017.
Temas:
Twitter.
Twitter
Data mining.
Online social networks.
Exploration de données (Informatique)
Réseaux sociaux (Internet)
Data mining
Online social networks
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Preface
  • Acknowledgments
  • Contents
  • Acronyms
  • 1 Introduction
  • 1.1 Dynamic Social Networks
  • 1.1.1 The Twitter Social Network
  • 1.2 Research and Technical Challenges
  • 1.3 Problem Statement and Objectives
  • 1.4 Scope and Plan of the Book
  • 2 Background and Related Work
  • 2.1 Introduction
  • 2.2 Document-Pivot Methods
  • 2.3 Feature-Pivot Methods
  • 2.4 Related Work
  • 2.4.1 Problem Definition
  • 2.4.2 Data Preprocessing
  • 2.4.3 Latent Dirichlet Allocation
  • 2.4.4 Document-Pivot Topic Detection
  • 2.4.5 Graph-Based Feature-Pivot Topic Detection2.4.6 Frequent Pattern Mining
  • 2.4.7 Soft Frequent Pattern Mining
  • 2.4.8 BNgram
  • 2.5 Chapter Summary
  • 3 Joint Sequence Complexity: Introduction and Theory
  • 3.1 Introduction
  • 3.2 Sequence Complexity
  • 3.3 Joint Complexity
  • 3.4 Contributions and Results
  • 3.4.1 Models and Notations
  • 3.4.2 Summary of Contributions and Results
  • 3.5 Proofs of Contributions and Results
  • 3.5.1 An Important Asymptotic Equivalence
  • 3.5.2 Functional Equations
  • 3.5.3 Double DePoissonization
  • 3.5.4 Same Markov Sources3.5.5 Different Markov Sources
  • 3.6 Expending Asymptotics and Periodic Terms
  • 3.7 Numerical Experiments in Twitter
  • 3.8 Suffix Trees
  • 3.8.1 Examples of Suffix Trees
  • 3.9 Snow Data Challenge
  • 3.9.1 Topic Detection Method
  • 3.9.2 Headlines
  • 3.9.3 Keywords Extraction
  • 3.9.4 Media URLs
  • 3.9.5 Evaluation of Topic Detection
  • 3.10 Tweet Classification
  • 3.10.1 Tweet Augmentation
  • 3.10.2 Training Phase
  • 3.10.3 Run Phase
  • 3.10.4 Experimental Results on Tweet Classification
  • 3.10.4.1 Classification Performance Based on Ground Truth3.11 Chapter Summary
  • 4 Text Classification via Compressive Sensing
  • 4.1 Introduction
  • 4.2 Compressive Sensing Theory
  • 4.3 Compressive Sensing Classification
  • 4.3.1 Training Phase
  • 4.3.2 Run Phase
  • 4.4 Tracking via Kalman Filter
  • 4.5 Experimental Results
  • 4.5.1 Classification Performance Based on Ground Truth
  • 4.6 Chapter Summary
  • 5 Extension of Joint Complexity and Compressive Sensing
  • 5.1 Introduction
  • 5.2 Classification Encryption via Compressed Permuted Measurement Matrices
  • 5.2.1 Preprocessing Phase5.2.2 Run Phase
  • 5.2.3 Security System Architecture
  • 5.2.3.1 Privacy System
  • 5.2.3.2 Key Description
  • 5.2.4 Possible Attacks from Malicious Users
  • 5.2.5 Experimental Results
  • 5.3 Dynamic Classification Completeness
  • 5.3.1 Motivation
  • 5.3.2 Proposed Framework
  • 5.3.3 Experimental Results
  • 5.4 Stealth Encryption Based on Eulerian Circuits
  • 5.4.1 Background
  • 5.4.1.1 Syntax Graph
  • 5.4.1.2 Eulerian Path and Circuit
  • 5.4.2 Motivation and Algorithm Description
  • 5.4.2.1 Motivation

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