Cover image for Social Media Data Mining and Analytics

Social Media Data Mining and Analytics

Author Antonios Chalkiopoulos , P. Oscar Boykin , Gungor Polatkan , Gabor Szabo
Release Date: 2018/10/01
ISBN: 9781118824856
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Book Description

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. 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, Wikipedia, Stack Exchange, LiveJournal, movie reviews, 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
  • How to personalize the social media experience using machine learning

Using the techniques the authors detail will provide organizations the competitive advantage they need to harness the rich data available from social media platforms.

Table of Contents

  1. Cover
  2. Introduction
    1. Human Interactions Measured
    2. Asking and Answering Questions with Data
    3. The Datasets Used in This Book
    4. The Languages and Frameworks Used in This Book
    5. System Requirements to Run the Examples
    6. Overview of the Chapters
    7. Online Repository for the Book
  3. CHAPTER 1: Users: The Who of Social Media
    1. Measuring Variations in User Behavior in Wikipedia
    2. Long Tails Everywhere: The 80/20 Rule (p/q Rule)
    3. Online Behavior on Twitter
    4. Summary
  4. CHAPTER 2: Networks: The How of Social Media
    1. Types and Properties of Social Networks
    2. Visualizing Networks
    3. Degrees: The Winner Takes All
    4. Capturing Correlations: Triangles, Clustering, and Assortativity
    5. Summary
  5. CHAPTER 3: Temporal Processes: The When of Social Media
    1. What Traditional Models Tell You About Events in Time
    2. Inter-Event Times
    3. Bursty Activities of Individuals
    4. Forecasting Metrics in Time
    5. Summary
  6. CHAPTER 4: Content: The What of Social Media
    1. Defining Content: Focus on Text and Unstructured Data
    2. Using Content Features to Identify Topics
    3. Extracting Low-Dimensional Information from High-Dimensional Text
    4. Summary
  7. CHAPTER 5: Processing Large Datasets
    1. MapReduce: Structuring Parallel and Sequential Operations
    2. Multi-Stage MapReduce Flows
    3. Patterns in MapReduce Programming
    4. Sampling and Approximations: Getting Results with Less Computation
    5. Executing on a Hadoop Cluster (Amazon EC2)
    6. Summary
  8. CHAPTER 6: Learn, Map, and Recommend
    1. Social Media Services Online
    2. Problem Formulation
    3. Learning and Mapping
    4. Prediction and Recommendation
    5. Summary
  9. CHAPTER 7: Conclusions
    1. The Surprising Stability of Human Interaction Patterns
    2. Averages, Standard Deviations, and Sampling
    3. Removing Outliers
  10. Index
  11. End User License Agreement
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