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Topic Detection and Classification in Social Networks: The Twitter Case 1st ed. 2018 [Hardback]

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  • Format: Hardback, 105 pages, height x width: 235x155 mm, weight: 454 g, 25 Illustrations, color; 13 Illustrations, black and white; XVI, 105 p. 38 illus., 25 illus. in color., 1 Hardback
  • Pub. Date: 13-Oct-2017
  • Publisher: Springer International Publishing AG
  • ISBN-10: 3319664131
  • ISBN-13: 9783319664132
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Topic Detection and Classification in Social Networks: The Twitter Case 1st ed. 2018Larger Image
  • Format: Hardback, 105 pages, height x width: 235x155 mm, weight: 454 g, 25 Illustrations, color; 13 Illustrations, black and white; XVI, 105 p. 38 illus., 25 illus. in color., 1 Hardback
  • Pub. Date: 13-Oct-2017
  • Publisher: Springer International Publishing AG
  • ISBN-10: 3319664131
  • ISBN-13: 9783319664132
Other books in subject:
Permanent link: https://www.kriso.ee/db/9783319664132.html
Keywords:

This book provides a novel method for topic detection and classification in social networks. The book addresses several research and technical challenges that are currently being investigated by the research community, from the analysis of relations and communications between members of a community, to quality, authority, relevance and timeliness of the content, traffic prediction based on media consumption, spam detection, to security, privacy and protection of personal information. Furthermore, the book discusses innovative techniques to address those challenges and provides novel solutions based on information theory, sequence analysis and combinatorics, which are applied on real data obtained from Twitter.

1 Introduction
1(8)
1.1 Dynamic Social Networks
1(2)
1.1.1 The Twitter Social Network
3(1)
1.2 Research and Technical Challenges
3(1)
1.3 Problem Statement and Objectives
4(2)
1.4 Scope and Plan of the Book
6(3)
2 Background and Related Work
9(12)
2.1 Introduction
9(1)
2.2 Document-Pivot Methods
10(1)
2.3 Feature-Pivot Methods
10(2)
2.4 Related Work
12(7)
2.4.1 Problem Definition
12(1)
2.4.2 Data Preprocessing
12(1)
2.4.3 Latent Dirichlet Allocation
13(1)
2.4.4 Document-Pivot Topic Detection
14(1)
2.4.5 Graph-Based Feature-Pivot Topic Detection
14(2)
2.4.6 Frequent Pattern Mining
16(1)
2.4.7 Soft Frequent Pattern Mining
16(2)
2.4.8 BNgram
18(1)
2.5
Chapter Summary
19(2)
3 Joint Sequence Complexity: Introduction and Theory
21(36)
3.1 Introduction
21(1)
3.2 Sequence Complexity
22(1)
3.3 Joint Complexity
22(4)
3.4 Contributions and Results
26(3)
3.4.1 Models and Notations
27(1)
3.4.2 Summary of Contributions and Results
27(2)
3.5 Proofs of Contributions and Results
29(7)
3.5.1 An Important Asymptotic Equivalence
29(2)
3.5.2 Functional Equations
31(1)
3.5.3 Double DePoissonization
32(1)
3.5.4 Same Markov Sources
32(1)
3.5.5 Different Markov Sources
33(3)
3.6 Expending Asymptotics and Periodic Terms
36(1)
3.7 Numerical Experiments in Twitter
37(2)
3.8 Suffix Trees
39(2)
3.8.1 Examples of Suffix Trees
41(1)
3.9 Snow Data Challenge
41(7)
3.9.1 Topic Detection Method
44(2)
3.9.2 Headlines
46(1)
3.9.3 Keywords Extraction
47(1)
3.9.4 Media URLs
47(1)
3.9.5 Evaluation of Topic Detection
48(1)
3.10 Tweet Classification
48(5)
3.10.1 Tweet Augmentation
48(1)
3.10.2 Training Phase
49(1)
3.10.3 Run Phase
49(2)
3.10.4 Experimental Results on Tweet Classification
51(2)
3.11
Chapter Summary
53(4)
4 Text Classification via Compressive Sensing
57(12)
4.1 Introduction
57(1)
4.2 Compressive Sensing Theory
58(1)
4.3 Compressive Sensing Classification
59(3)
4.3.1 Training Phase
59(1)
4.3.2 Run Phase
60(2)
4.4 Tracking via Kalman Filter
62(2)
4.5 Experimental Results
64(3)
4.5.1 Classification Performance Based on Ground Truth
65(2)
4.6
Chapter Summary
67(2)
5 Extension of Joint Complexity and Compressive Sensing
69(24)
5.1 Introduction
69(1)
5.2 Classification Encryption via Compressed Permuted Measurement Matrices
70(6)
5.2.1 Preprocessing Phase
71(1)
5.2.2 Run Phase
71(1)
5.2.3 Security System Architecture
72(1)
5.2.4 Possible Attacks from Malicious Users
73(1)
5.2.5 Experimental Results
74(2)
5.3 Dynamic Classification Completeness
76(5)
5.3.1 Motivation
77(1)
5.3.2 Proposed Framework
78(1)
5.3.3 Experimental Results
79(2)
5.4 Stealth Encryption Based on Eulerian Circuits
81(10)
5.4.1 Background
83(1)
5.4.2 Motivation and Algorithm Description
83(3)
5.4.3 Performance in Markov Models
86(5)
5.4.4 Experimental Results
91(1)
5.5
Chapter Summary
91(2)
6 Conclusions and Perspectives
93(2)
A Suffix Trees
95(2)
A.1 Suffix Tree Construction
95(1)
A.2 Suffix Trees Superposition
96(1)
References 97(6)
Index 103
Dr. Dimitrios Milioris is a research associate and lecturer at the Massachusetts Institute of Technology (MIT). He received his Ph.D. from Ιcole Polytechnique Paris (2015, honors) while a scholar at Columbia University, New York, USA, as an Alliance Program awardee (2013 2014). He received his double M.Sc. degree (2011, first in class, honors) in computer science & applied mathematics from Paris XI University and the Ιcole Polytechnique, and his B.Sc. degree (2009, honors) in computer science from the University of Crete, Greece. Prior to joining MIT, he was a researcher at Bell Labs, Alcatel-Lucent in Paris, France, and a member of the Mathematics of Dynamic & Complex Networks Department. Prior to joining Bell Labs, he served as a research assistant at the Institute of Computer Science (ICS) of the Foundation for Research and Technology Hellas (FO.R.T.H.), and as a research engineer with the Hipercom Team at the National Institute for Research in Computer Science and Automatic Control (I.N.R.I.A.), followed by a compulsory military service in Telecommunications Division.