Social Media Data Mining and Analytics

Cover -- Title Page -- Copyright -- Contents -- Introduction -- Human Interactions Measured -- Online Behavior Through Data Collection -- What Types of Data Are Essential to Collect? -- Asking and Answering Questions with Data -- The Datasets Used in This Book -- Wikipedia -- Twitter -- Stack Exchange -- LiveJournal -- Scientific Documents from Cora -- Amazon Fine Food Reviews -- MovieLens Movie Ratings -- The Languages and Frameworks Used in This Book -- Python -- Scalding -- System Requirements to Run the Examples -- Overview of the Chapters -- Online Repository for the Book -- Chapter 1 Users: The Who of Social Media -- Measuring Variations in User Behavior in Wikipedia -- The Diversity of User Activities -- The Origin of the User Activity Distribution -- The Consequences of the Power Law -- The Long Tail in Human Activities -- Long Tails Everywhere: The 80/20 Rule (p/q Rule) -- Online Behavior on Twitter -- Retrieving Tweets for Users -- Logarithmic Binning -- User Activities on Twitter -- Summary -- Chapter 2 Networks: The How of Social Media -- Types and Properties of Social Networks -- When Users Create the Connections: Explicit Networks -- Directed Versus Undirected Graphs -- Node and Edge Properties -- Weighted Graphs -- Creating Graphs from Activities: Implicit Networks -- Visualizing Networks -- Degrees: The Winner Takes All -- Counting the Number of Connections -- The Long Tail in User Connections -- Beyond the Idealized Network Model -- Capturing Correlations: Triangles, Clustering, and Assortativity -- Local Triangles and Clustering -- Assortativity -- Summary -- Chapter 3 Temporal Processes: The When of Social Media -- What Traditional Models Tell You About Events in Time -- When Events Happen Uniformly in Time -- Inter-Event Times -- Comparing to a Memoryless Process -- Autocorrelations -- Deviations from Memorylessness