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E-grāmata: Social Media Mining: An Introduction

4.09/5 (12 ratings by Goodreads)
(Arizona State University), (Arizona State University), (Arizona State University)
  • Formāts: EPUB+DRM
  • Izdošanas datums: 28-Apr-2014
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781139904445
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  • Formāts: EPUB+DRM
  • Izdošanas datums: 28-Apr-2014
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781139904445
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.

Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining.

Recenzijas

'This is an exceptionally well-constructed book on social media that will be useful to academia and industry alike. The book covers the entire area of social network analysis in a comprehensive and understandable way.' Charu Aggarwal, IBM T. J. Watson Research Center 'This is a delightful exploration of a multidisciplinary field in its simple and straightforward style. Social Media Mining introduces and connects underlying concepts with clarity and enables you to explore this amazing field further with confidence.' Philip Yu, University of Illinois, Chicago

Papildus informācija

Integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining.
Preface xi
Acknowledgments xv
1 Introduction
1(12)
1.1 What Is Social Media Mining
1(1)
1.2 New Challenges for Mining
2(1)
1.3 Book Overview and Reader's Guide
3(3)
1.4 Summary
6(1)
1.5 Bibliographic Notes
7(1)
1.6 Exercises
8(5)
Part I Essentials
2 Graph Essentials
13(38)
2.1 Graph Basics
14(4)
2.2 Graph Representation
18(2)
2.3 Types of Graphs
20(2)
2.4 Connectivity in Graphs
22(4)
2.5 Special Graphs
26(5)
2.6 Graph Algorithms
31(15)
2.7 Summary
46(1)
2.8 Bibliographic Notes
47(1)
2.9 Exercises
48(3)
3 Network Measures
51(29)
3.1 Centrality
52(12)
3.2 Transitivity and Reciprocity
64(5)
3.3 Balance and Status
69(2)
3.4 Similarity
71(5)
3.5 Summary
76(1)
3.6 Bibliographic Notes
77(1)
3.7 Exercises
78(2)
4 Network Models
80(25)
4.1 Properties of Real-World Networks
80(4)
4.2 Random Graphs
84(9)
4.3 Small-World Model
93(4)
4.4 Preferential Attachment Model
97(4)
4.5 Summary
101(1)
4.6 Bibliographic Notes
102(1)
4.7 Exercises
103(2)
5 Data Mining Essentials
105(36)
5.1 Data
106(5)
5.2 Data Preprocessing
111(2)
5.3 Data Mining Algorithms
113(1)
5.4 Supervised Learning
113(14)
5.5 Unsupervised Learning
127(6)
5.6 Summary
133(1)
5.7 Bibliographic Notes
134(1)
5.8 Exercises
135(6)
Part II Communities and Interactions
6 Community Analysis
141(38)
6.1 Community Detection
144(17)
6.2 Community Evolution
161(7)
6.3 Community Evaluation
168(6)
6.4 Summary
174(1)
6.5 Bibliographic Notes
175(1)
6.6 Exercises
176(3)
7 Information Diffusion in Social Media
179(38)
7.1 Herd Behavior
181(5)
7.2 Information Cascades
186(7)
7.3 Diffusion of Innovations
193(7)
7.4 Epidemics
200(9)
7.5 Summary
209(1)
7.6 Bibliographic Notes
210(2)
7.7 Exercises
212(5)
Part III Applications
8 Influence and Homophily
217(28)
8.1 Measuring Assortativity
218(7)
8.2 Influence
225(9)
8.3 Homophily
234(2)
8.4 Distinguishing Influence and Homophily
236(4)
8.5 Summary
240(1)
8.6 Bibliographic Notes
241(1)
8.7 Exercises
242(3)
9 Recommendation in Social Media
245(26)
9.1 Challenges
246(1)
9.2 Classical Recommendation Algorithms
247(11)
9.3 Recommendation Using Social Context
258(5)
9.4 Evaluating Recommendations
263(4)
9.5 Summary
267(1)
9.6 Bibliographic Notes
268(1)
9.7 Exercises
269(2)
10 Behavior Analytics
271(24)
10.1 Individual Behavior
271(12)
10.2 Collective Behavior
283(7)
10.3 Summary
290(1)
10.4 Bibliographic Notes
291(1)
10.5 Exercises
292(3)
Notes 295(4)
Bibliography 299(16)
Index 315
Reza Zafarani is a Research Associate of Computer Science and Engineering at Arizona State University. He performs research in user behavioral modeling and was among the first to research on user identification and behavioral analysis across sites. Mohammad Ali Abbasi is a Research Associate of Computer Science and Engineering at Arizona State University. His research is focused on evaluating user credibility in social media and using social media for humanitarian assistance and disaster relief. Huan Liu is a Professor of Computer Science and Engineering at Arizona State University where he has been recognized for excellence in teaching and research. His research interests include real-world, data intensive applications with high-dimensional data of disparate forms, such as social media.