Social Media Data Mining and Analytics

Gabor Szabo , Gungor Polatkan , P. Oscar Boykin , Antonios Chalkiopoulos 지음

Wiley 2018년 09월 18일 출간

작품소개

책소개

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.

목차

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

작가정보

저자(글) Gabor Szabo

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.

저자(글) Gungor Polatkan

저자(글) P. Oscar Boykin

저자(글) Antonios Chalkiopoulos