Social Media Data Mining and Analytics

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

GABOR SZABO, PHD, is a Senior Staff Software Engineer at Tesla and a former data scientist at Twitter, where he focused on predicting user behavior and content popularity in crowdsourced online services, and on modeling large-scale content dynamics. He also authored the PyCascading data processing library.
 
GUNGOR POLATKAN, PHD, is a Tech Lead/Engineering Manager designing and implementing end-to-end machine learning and artificial intelligence offline/online pipelines for the LinkedIn Learning relevance backend. He was previously a machine learning scientist at Twitter, where he worked on topics such as ad targeting and user modeling.
 
P. OSCAR BOYKIN, PHD, is a software engineer at Stripe where he works on machine learning infrastructure. He was previously a Senior Staff Engineer at Twitter, where he worked on data infrastructure problems. He is coauthor of the Scala big-data libraries Algebird, Scalding and Summingbird.
 
ANTONIOS CHALKIOPOULOS, MSC, is a Distributed Systems Specialist. A system engineer who has delivered fast/big data projects in media, betting, and finance, he is now leading the effort on the Lenses platform for data streaming as a co-founder and CEO at https://lenses.stream.