Description
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 coherent platform 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 for social media mining.
Chapter
2.4 Connectivity in Graphs
2.6.1 Graph/Tree Traversal
2.6.2 Shortest Path Algorithms
2.6.3 Minimum Spanning Trees
2.6.4 Network Flow Algorithms
2.6.5 Maximum Bipartite Matching
3.1.2 Eigenvector Centrality
3.1.5 Betweenness Centrality
3.1.6 Closeness Centrality
3.2 Transitivity and Reciprocity
3.4.1 Structural Equivalence
3.4.2 Regular Equivalence
4.1 Properties of Real-World Networks
4.1.1 Degree Distribution
4.1.2 Clustering Coefficient
4.1.3 Average Path Length
4.2.1 Evolution of Random Graphs
4.2.2 Properties of Random Graphs
4.2.3 Modeling Real-World Networks with Random Graphs
4.3.1 Properties of the Small-World Model
4.3.2 Modeling Real-World Networks with the Small-World Model
4.4 Preferential Attachment Model
4.4.1 Properties of the Preferential Attachment Model
4.4.2 Modeling Real-World Networks with the Preferential Attachment Model
5.3 Data Mining Algorithms
5.4.1 Decision Tree Learning
5.4.2 Naive Bayes Classifier
5.4.3 Nearest Neighbor Classifier
5.4.4 Classification with Network Information
5.4.6 Supervised Learning Evaluation
5.5 Unsupervised Learning
5.5.1 Clustering Algorithms
5.5.2 Unsupervised Learning Evaluation
Part II Communities and Interactions
6.1.1 Community Detection Algorithms
6.1.2 Member-Based Community Detection
6.1.3 Group-Based Community Detection
6.2.1 How Networks Evolve
6.2.2 Community Detection in Evolving Networks
6.3.1 Evaluation with Ground Truth
6.3.2 Evaluation without Ground Truth
7 Information Diffusion in Social Media
7.1.1 Bayesian Modeling of Herd Behavior
7.2.1 Independent Cascade Model (ICM)
7.2.2 Maximizing the Spread of Cascades
7.3 Diffusion of Innovations
7.3.1 Innovation Characteristics
7.3.2 Diffusion of Innovations Models
7.3.3 Modeling Diffusion of Innovations
8 Influence and Homophily
8.1 Measuring Assortativity
8.1.1 Measuring Assortativity for Nominal Attributes
8.1.2 Measuring Assortativity for Ordinal Attributes
8.2.1 Measuring Influence
8.3.1 Measuring Homophily
8.4 Distinguishing Influence and Homophily
9 Recommendation in Social Media
9.2 Classical Recommendation Algorithms
9.2.1 Content-Based Methods
9.2.2 Collaborative Filtering (CF)
9.2.3 Extending Individual Recommendation to Groups of Individuals
9.3 Recommendation Using Social Context
9.3.1 Using Social Context Alone
9.3.2 Extending Classical Methods with Social Context
9.3.3 Recommendation Constrained by Social Context
9.4 Evaluating Recommendations
9.4.1 Evaluating Accuracy of Predictions
9.4.2 Evaluating Relevancy of Recommendations
9.4.3 Evaluating Ranking of Recommendations
10.1.1 Individual Behavior Analysis
10.1.2 Individual Behavior Modeling
10.1.3 Individual Behavior Prediction
10.2.1 Collective Behavior Analysis
10.2.2 Collective Behavior Modeling
10.2.3 Collective Behavior Prediction
Social Media Mining
：An Introduction
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