The official online compendium for Mining the Social Web, 2nd Edition (O'Reilly, 2013)

Mining the Social Web, 2nd Edition is currently available through O'Reilly Media's Early Access and Rough Cuts programs. The final version of the book will not be complete until the September/October timeframe, but in the meanwhile, you can get the latest source code here and pre-order a copy of the ebook directly from O'Reilly. Pre-ordering through O'Reilly's Early Access program contains a number of great benefits including regular updates as the final manuscript of the book is completed as well as continual updates to the book for life! (And for a book that's built on social web APIs, rest assured that API changes will occasionally require the text of the book and examples to be updated.)

There's an incredible turn-key virtual machine experience for this second edition of the book (see complete details below) that provides you with the ability to explore and run all of the source code in a hassle-free manner. All that you have to do is follow a few simple steps to get the virtual machine installed, and you'll be off to the races. The estimated burden for setting all of this up is about 15 minutes, and it is strongly recommended that you first take advantage of use the virtual machine before attempting to run the samples on your own.

Note that the virtual machine for this book is designed to install every single dependency for you automatically and save you a lot of time, even if you are a fairly sophisticated power user. Please try it out.

If you experience any problems at all, file an issue here on GitHub. Be sure to also follow @SocialWebMining on Twitter and like http://facebook.com/MiningTheSocialWeb on Facebook.

This edition of Mining the Social Web extensively uses IPython Notebook to facilitate the learning and development process, and the best way to preview the source code is with the links below. Chapters that aren't hyperlinked yet will be available as soon as that content is available in the ebook through O'Reilly Media's Early Access. All source code is estimated to appear in this repository by mid-July 2013.

• [Chapter 0 - Preface](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/Chapter 0 - Preface.ipynb)
• [Chapter 1 - Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/Chapter 1 - Mining Twitter.ipynb)
• [Chapter 2 - Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/Chapter 2 - Mining Facebook.ipynb)
• [Chapter 3 - Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/Chapter 3 - Mining LinkedIn.ipynb)
• [Chapter 4 - Mining Google+: Computing Document Similarity, Extracting Collocations, and More](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/Chapter 4 - Mining Google+.ipynb)
• [Chapter 5 - Mining Web Pages: Using Natural Language Processing to Understand Human Language, Summarize Blog Posts and More](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/Chapter 5 - Mining Web Pages.ipynb)
• [Chapter 6 - Mining Mailboxes: Analyzing Who's Talking To Whom About What, How Often, and More](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/Chapter 6 - Mining Mailboxes.ipynb)
• [Chapter 7 - Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs, and More](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/Chapter 7 - Mining GitHub.ipynb)
• [Chapter 8 - Mining the Semantically Marked-Up Web: Extracting Microformats, Inferencing Over RDF, and More](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/Chapter 8 - Mining the Semantically Marked-Up Web.ipynb)
• [Chapter 9 - Twitter Cookbook](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/Chapter 9 - Twitter Cookbook.ipynb)
• [Appendix A - Virtual Machine Experience](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/_Appendix A - Virtual Machine Experience.ipynb)
• [Appendix B - OAuth Primer](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/_Appendix B - OAuth Primer.ipynb)
• [Appendix C - Python & IPython Notebook Tips](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/_Appendix C - Python & IPython Notebook Tips.ipynb)

The code for Mining the Social Web is organized by chapter in an IPython Notebook format to maximize enjoyment of following along with examples as part of an interactive experience. Unfortunately, some of the Python dependencies for the example code can be a little bit tricky to get installed and configured, so providing a completely turn-key virtual machine to make your reading experience as simple and enjoyable as possible is in order. Even if you are a seasoned developer, you may still find some value in using this virtual machine to get started in order to save yourself some time because it's powered with Vagrant, an amazing development tool that you'll probably want to know about and arguably makes working with virtualization even easier than a native Virtualbox, VMWare image, etc.

You're more interested in following along and learning from the example code than learning how to install and manage system dependencies, right? Just follow the instructions described in [Appendix A - Virtual Machine Experience](http://nbviewer.ipython.org/urls/raw.github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition/master/ipynb/_Appendix A - Virtual Machine Experience.ipynb) and streamline you troubles.

You may also enjoy this short ~4 minute screencast that summarizes the steps involved in getting started.

The source code in this repository is free for your use whether or not you have a copy of the book, but it does generally assume that you are following along with the book where some valuable explanations and extended discussion of the source code occurs. The book is probably your single best source of support for questions you will have about the code or things that it does. Its product description from the publisher follows:

With this Early Access edition of Mining the Social Web (2nd Ed), you'll get access the author's raw and unedited content as he finishes writing so that you can take advantage of this powerful content long before the official release. You'll be able to influence and shape the final manuscript of the book by leaving the author direct feedback, and you'll also receive updates when significant changes are made, new chapters as they're written, and the final ebook bundle once it's available.

Facebook, Twitter, LinkedIn, Google+, and other social web properties generate a wealth of valuable social data, but how can you tap into this data and discover who’s connecting with whom, which insights are lurking just beneath the surface, and what people are talking about? This book shows you how to answer these questions and many more. Each chapter combines popular and useful social web data with analysis techniques and visualization to help you find the needles in the social haystack that you've been looking for—as well as many you probably didn't even know existed.

In this expanded and thoroughly revised second edition you’ll learn how to:

• Navigate the most popular social web APIs to access, collect, analyze, and visualize social web data
• Employ IPython Notebook and other easy to use Python packages such as the Natural Language Toolkit, NetworkX, and Matplotlib to efficiently sift through social web data as part of an experimentally-driven approach to discovering insights in social web data
• Apply advanced text-mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection to human language data that you'll encounter all over the web
• Bootstrap interest graphs by discovering latent affinities between people, programming languages, and coding projects from GitHub data
• Visualize social web data with D3, a state-of-the-art HTML5 and JavaScript toolkit

The book's source code is maintained here in this GitHub repository by its author and can be deployed as turn-key virtual machine with each chapter's source code presented in an interactive and easy to use IPython Notebook format. No complex third-party installations or advanced Python knowledge is required to get the most out of this book.