Social Media Data Mining and Analytics

ISBN-13: 9781118824856 / Angielski / Miękka / 2018 / 352 str.

Gabor Szabo; Oscar Boykin

Opis

Harness the power of social media to predict customer behavior and improve sales Social media is the biggest source of Big Data. Because of this, 90% of Fortune 500 companies are investing in Big Data initiatives that will help them predict consumer behavior to produce better sales results. Written by Dr. Gabor Szabo, a Senior Data Scientist at Twitter, and Dr. Oscar Boykin, a Software Engineer at Twitter, Social Media Data Mining and Analytics shows analysts how to use sophisticated techniques to mine social media data, obtaining the information they need to generate amazing results for their businesses. Social Media Data Mining and Analytics isn't just another book on the business case for social media. Rather, this book provides hands-on examples for applying state-of-the-art tools and technologies to mine social media - examples include Twitter, Facebook, Pinterest, Wikipedia, Reddit, Flickr, Web hyperlinks, and other rich data sources. In it, you will learn:

• The four key characteristics of online services-users, social networks, actions, and content
• The full data discovery lifecycle-data extraction, storage, analysis, and visualization
• How to work with code and extract data to create solutions
• How to use Big Data to make accurate customer predictions

Szabo and Boykin wrote this book to provide businesses with the competitive advantage they need to harness the rich data that is available from social media platforms.

Spis treści

Introduction xvii Chapter 1 Users: TheWho of Social Media 1 Measuring Variations in User Behavior in Wikipedia 2 The Diversity of User Activities 3 The Origin of the User Activity Distribution 12 The Consequences of the Power Law 20 The Long Tail in Human Activities 25 Long Tails Everywhere: The 80/20 Rule (p/q Rule) 28 Online Behavior on Twitter 32 Retrieving Tweets for Users 33 Logarithmic Binning 36 User Activities on Twitter 37 Summary 39 Chapter 2 Networks: The How of Social Media 41 Types and Properties of Social Networks 42 When Users Create the Connections: Explicit Networks 43 Directed Versus Undirected Graphs 45 Node and Edge Properties 45 Weighted Graphs 46 Creating Graphs from Activities: Implicit Networks 48 Visualizing Networks 51 Degrees: The Winner Takes All 55 Counting the Number of Connections 57 The Long Tail in User Connections 58 Beyond the Idealized Network Model 62 Capturing Correlations: Triangles, Clustering, and Assortativity 64 Local Triangles and Clustering 64 Assortativity 70 Summary 75 Chapter 3 Temporal Processes: The When of Social Media 77 What Traditional Models Tell You About Events in Time 77 When Events Happen Uniformly in Time 79 Inter-Event Times 81 Comparing to a Memoryless Process 86 Autocorrelations 89 Deviations from Memorylessness 91 Periodicities in Time in User Activities 93 Bursty Activities of Individuals 99 Correlations and Bursts 105 Reservoir Sampling 106 Forecasting Metrics in Time 110 Finding Trends 112 Finding Seasonality 115 Forecasting Time Series with ARIMA 117 The Autoregressive Part ("AR") 118 The Moving Average Part ("MA") 119 The Full ARIMA(p, d, q) Model 119 Summary 121 Chapter 4 Content: The What of Social Media 123 Defining Content: Focus on Text and Unstructured Data 123 Creating Features from Text: The Basics of Natural Language Processing 125 The Basic Statistics of Term Occurrences in Text 128 Using Content Features to Identify Topics 129 The Popularity of Topics 138 How Diverse Are Individual Users' Interests? 141 Extracting Low-Dimensional Information from High-Dimensional Text 144 Topic Modeling 145 Unsupervised Topic Modeling 147 Supervised Topic Modeling 155 Relational Topic Modeling 162 Summary 169 Chapter 5 Processing Large Datasets 171 Map Reduce: Structuring Parallel and Sequential Operations 172 Counting Words 174 Skew: The Curse of the Last Reducer 177 Multi-Stage MapReduce Flows 179 Fan-Out 180 Merging Data Streams 181 Joining Two Data Sources 183 Joining Against Small Datasets 186 Models of Large-Scale MapReduce 187 Patterns in MapReduce Programming 188 Static MapReduce Jobs 188 Iterative MapReduce Jobs 195 PageRank for Ranking in Graphs 195 K-means Clustering 199 Incremental MapReduce Jobs 203 Temporal MapReduce Jobs 204 Rollups and Data Cubing 205 Expanding Rollup Jobs 211 Challenges with Processing Long-Tailed Social Media Data 212 Sampling and Approximations: Getting Results with Less Computation 214 HyperLogLog 217 HyperLogLog Example 219 HyperLogLog on the Stack Exchange Dataset 221 Performance of HLL on Large Datasets 222 Bloom Filters 223 A Bloom Filter Example 226 Bloom Filter as Pre-Computed Membership Knowledge 228 Bloom Filters on Large Social Datasets 229 Count-Min Sketch 231 Count-Min Sketch--Heavy Hitters Example 233 Count-Min Sketch--Top Percentage Example 235 Aggregating Approximate Data Structures 235 Summary of Approximations 236 Executing on a Hadoop Cluster (Amazon EC2) 237 Installing a CDH Cluster on Amazon EC2 237 Providing IAM Access to Collaborators 241 Adding On-Demand Cluster Capabilities 242 Summary 243 Chapter 6 Learn, Map, and Recommend 245 Social Media Services Online 246 Search Engines 246 Content Engagement 246 Interactions with the Real World 248 Interactions with People 249 Problem Formulation 251 Learning and Mapping 253 Matrix Factorization 255 Learning, Training 257 Under- and Overfitting 257 Regularizing in Matrix Factorization 259 Non-Negative Matrix Factorization and Sparsity 260 Demonstration on Movie Ratings 261 Interpreting the Learned Stereotypes 265 Exploratory Analysis 269 Prediction and Recommendation 274 Evaluation 277 Overview of Methodologies 278 Nearest Neighbor-Based Approaches 278 Approaches Based on Supervised Learning 280 Predicting Movie Ratings with Logistic Regression 280 Common Issues with Features 288 Domain-Specific Applications 289 Summary 290 Chapter 7 Conclusions 293 The Surprising Stability of Human Interaction Patterns 293 Averages, Standard Deviations, and Sampling 296 Removing Outliers 303 Index 309

O autorze

Gabor Szabo, Ph.D. is a Senior Data Scientist at Twitter.   He works on predicting user behavior and modeling large–scale content dynamics on Twitter.  He authored the open–source Hadoop framework PyCascading. Before that he worked on predicting content popularity in crowdsourced online services, and did research on large–scale social and biological systems.   He is a frequent speaker at major conferences on analytics and Big Data. Oscar Boykin, Ph.D. is a Staff Software Engineer at Twitter. He is co–author of Scalding, Twitter s library for large–scale Hadoop programming. At Twitter, he is building systems for real–time big–data with applications to ad targeting.  He is a frequent speaker at major conferences on analytics and Big Data. E