This book shows readers, through hands-on examples, how to use collected data to understand, describe, and make predictions in social media systems.  The authors show how to apply state-of-the-art tools and technologies to various data mining scenarios against diverse sources of social media data (including, Twitter, Wikipedia, Reddit, Flickr, Web hyperlinks, and more).   They highlight results that are obvious once someone has spent considerable time analyzing these systems, yet are counterintuitive at first sight.  Readers will learn how to identify common patterns of online user behavior so that they can independently build and apply predictive algorithms that exploit these patterns. The authors discuss four key characteristics of online services: the users, their social network, the time of users actions, and the content users create (the who, how, when, and what of social media), and illustrate them in action using actual datasets from Twitter, Wikipedia, Reddit, Flickr, Web hyperlinks, and last.fm, as well as such services as Facebook and Pinterest.  They will provide examples backed up by research conducted at Twitter.  The authors also cover state-of-the-art tools and techniques for working with very large datasets, focusing on the defacto industry standard, Hadoop, as well as new technologies like Storm that support real-time analysis of streaming data.   They cover the full discovery lifecycle:  including data extraction, storage, analysis, and visualization.  Throughout the book, the authors illustrate solutions using code-level examples written in Python; they will provide a public repository on GitHub for both the working code and data used for these examples.

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